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Peer-reviewed

Research Article

The emergence of inequality in social groups: Network structure and institutions affect the distribution of earnings in cooperation games

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Methodology, London School of Economics and Political Science, London, United Kingdom

Roles Conceptualization, Funding acquisition, Writing – review & editing

Affiliation GESIS – Leibniz Institute for Social Sciences, Cologne, Germany

Current address: CTRL-labs, New York, NY, United States of America

Affiliation Department of Management, Aarhus University, Aarhus, Denmark

• Milena Tsvetkova, 
• Claudia Wagner, 

• Published: July 20, 2018
• https://doi.org/10.1371/journal.pone.0200965
• Reader Comments

From small communities to entire nations and society at large, inequality in wealth, social status, and power is one of the most pervasive and tenacious features of the social world. What causes inequality to emerge and persist? In this study, we investigate how the structure and rules of our interactions can increase inequality in social groups. Specifically, we look into the effects of four structural conditions—network structure, network fluidity, reputation tracking, and punishment institutions—on the distribution of earnings in network cooperation games. We analyze 33 experiments comprising 96 experimental conditions altogether. We find that there is more inequality in clustered networks compared to random networks, in fixed networks compared to randomly rewired and strategically updated networks, and in groups with punishment institutions compared to groups without. Secondary analyses suggest that the reasons inequality emerges under these conditions may have to do with the fact that fixed networks allow exploitation of the poor by the wealthy and clustered networks foster segregation between the poor and the wealthy, while the burden of costly punishment falls onto the poor, leaving them poorer. Surprisingly, we do not find evidence that inequality is affected by reputation in a systematic way but this could be because reputation needs to play out in a particular network environment in order to have an effect. Overall, our findings suggest possible strategies and interventions to decrease inequality and mitigate its negative impact, particularly in the context of mid- and large-sized organizations and online communities.

Citation: Tsvetkova M, Wagner C, Mao A (2018) The emergence of inequality in social groups: Network structure and institutions affect the distribution of earnings in cooperation games. PLoS ONE 13(7): e0200965. https://doi.org/10.1371/journal.pone.0200965

Editor: Floriana Gargiulo, Centre National de la Recherche Scientifique, FRANCE

Received: April 6, 2018; Accepted: July 4, 2018; Published: July 20, 2018

Copyright: © 2018 Tsvetkova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data files are available from the GESIS Data Archive for the Social Sciences (DOI http://dx.doi.org/10.7802/1697 ).

Funding: This research was made possible through the generous support of the Volkswagen Foundation ( https://www.volkswagenstiftung.de/ ) under Grant Ref. 92 173. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

From social status hierarchies in kindergarten play groups and college fraternities [ 1 ] to pay dispersion at the workplace [ 2 ] and from extreme levels of popularity in online communities [ 3 ] to shockingly uneven distributions of material wealth within states [ 4 ], inequality takes many forms and occurs at many different levels of social organization. Why do we observe extreme distributions of outcomes when people only moderately vary in intelligence, physical abilities, and psychological traits? Why does inequality exist when people actually have strong preference for equity and fairness [ 5 , 6 ]?

Evidently, we cannot explain inequality with human heterogeneity or preferences. Instead, social scientists have looked at social structure and its effect on behavior to explain the outcomes of individuals and groups. For example, researchers focusing on historical approaches have argued that certain global, state-level, and group-level processes and trends have disadvantaged some classes of individuals in terms of economic capital and political representation while enriching others [ 7 – 9 ]. Sociologists working on social stratification and mobility have observed that structural, network, and psychological factors, such as institutional arrangements, neighborhood and school characteristics, parents’ socioeconomic standing, peer influences, and innate ability and motivation, translate into individual outcomes such as social class, education, health, and income [ 10 – 12 ]. Social psychologists and behavioral economists have used controlled experiments to demonstrate that social differentiation and scarcity of resources affects individual behavior in a way that reinforces and enhances pre-existing differences [ 13 – 16 ].

While this research has greatly expanded our understanding of the extent, trends, and consequences of inequality in society, it does not paint a complete picture. Historical approaches focus on the macro-level and often ignore individuals, stratification research focuses on individuals but disregards interpersonal interactions, while behavioral research centers on small-group interactions but rarely compares groups on a macro-level. In many social settings, large groups of individuals interact in a way that their behavior and interactions drive group-level outcomes such as inequality, while the emerging group-level patterns in turn affect individual decision-making and actions. In such complex systems, the coevolution of interaction structure and individual behavior might create self-reinforcing feedback loops that amplify or exaggerate small initial and/or accidental differences into extreme distributions of outcomes [ 17 ]. Inequality, then, can be viewed as an emergent phenomenon that can play out differently under different structural and institutional constraints. This will be the case in mid- and large-sized social groups based on face-to-face or online interactions such as hunter-gatherer groups, agrarian villages, large work offices, schools, social media sites, online forums, and user-generated content communities.

Some research on how inequality emerges in complex social systems already exists. Theoretical work from within this paradigm has shown, for example, that population growth and preferential attachment [ 18 ], multiplicative gains and losses [ 19 ], and trade and inheritance [ 20 ] create and maintain inequality. Empirical research on this topic is more challenging since one needs to observe and manipulate multiple large social groups, which could be both costly and logistically complicated. Nevertheless, empirical work has also started to accumulate. From large-scale online experiments on cultural and attention markets, we know that due to social influence, the visibility of success increases inequality [ 21 ], even if the initial success was arbitrarily awarded [ 22 ]. In addition, experiments on cooperation games have shown that the visibility of wealth affects the cooperative behavior of the wealthy and exacerbates pre-existing inequality [ 23 ], reputation systems increase inequality by facilitating increasing returns from early cooperation [ 24 ], and inequality is reduced in networks where the very wealthy and the very poor are connected [ 25 ].

Extending this line of research, we present empirical evidence on how four structural conditions and institutional constraints affect the emergence of inequality in social groups. We investigate the effects of network structure, network fluidity, visibility of reputation, and punishment institutions. These conditions determine whether people interact within local clusters or with random others, whether they interact with the same others again and again, whether they have third-party information about others’ past behavior, and whether they can punish, at a cost, those who do not behave nicely. These are conditions that characterize some existing social groups more than others (compare, for example, the stable interactions in agrarian villages versus the constant recombination of nomadic groups) and can be implemented in the design of new formal organizations and online communities (for example, deciding whether to institute a reputation system in a local community classifieds website).

To gather observations on social groups, we use data from a large number of experiments on cooperation games in networks [ 26 – 43 ]. In the past couple of decades, these kinds of experiments have been most commonly used to study the emergence of cooperation and network formation. The experiments use games such as the Prisoner’s Dilemma and the Public Good Game in which participants have a choice between cooperating/contributing and defecting/doing nothing. Cooperation is costly and hence not individually rational but it has positive returns for the recipients so hence, it is collectively beneficial. Thus, the decision situation poses a social dilemma. Participants play the game repeatedly with numerous partners simultaneously. Further, different groups play in different network structures, with different information, or with additional action possibilities. This interaction setup models well situations in which members of large groups interact in subgroups and work together towards a common goal, such as rendering a service, producing a product, or increasing knowledge.

To study inequality, we look at the distribution of the points individuals accrue by the end of the game. These points can be understood as the individuals’ wealth, whether material or immaterial. For example, if we perceive cooperation as exchanging valuable information or useful advice, then the points will measure wealth in knowledge or wisdom.

The experiments we analyze were originally used to study the emergence of cooperation. The researchers found that network clustering, network stability, strategic network rewiring, and the possibility for reputation tracking and punishment increase the average level of cooperation. With the exception of costly punishment which reduces point earnings, more cooperation implies higher average number of points, i.e. higher wealth. However, how do the different structural factors affect the distribution of wealth in the interaction groups? This question has not been systematically investigated before. The answer is nontrivial as high levels of cooperation can have opposite effects on inequality, depending on how cooperators and defectors are clustered and interconnected. Further, some structural conditions may affect the behavior of different individuals differently, either erasing or exacerbating emerging wealth disparities.

In what follows, we discuss how network structure, network fluidity, reputation tracking, and punishment institutions affect the network composition and the behavior of individuals in group cooperation games and hypothesize what this implies for inequality. In the Results section, we use the experimental data to test the hypotheses causally at the group level and to provide some insights as to the actual behavioral and interaction mechanisms at play.

Network structure.

From previous research we know that networks with high levels of clustering, whereby an individual’s neighbors tend to be neighbors with each other, are expected to foster forward-looking behavior, imitation, and social reinforcement and promote the emergence of cooperative clusters [ 30 , 38 ]. This implies that cooperators and defectors are more likely to become segregated in their own separate clusters. Since cooperators interacting with cooperators benefit, while defectors interacting with defectors lose out, we would expect higher inequality in clustered networks compared to networks that are more random and without local structure. To relate this to a realistic setting, this hypothesis would imply, for example, that schools with stringent degree requirements that concentrate most of learning within classrooms or majors will be marked with higher inequality in educational outcomes than schools that allow elective courses and more uniform interactions among students, assuming that student cooperation and collaboration enhance learning.

Network fluidity.

The above rationale assumed that the networks are relatively stable and individuals interact repeatedly with the same partners. If the partnerships are more volatile, cooperative clusters are less likely to emerge unless individuals self-select into them. Moreover, fixed networks enable exploitation: knowing that your cooperating partners cannot leave, you may as well start free-riding on their efforts and contributions; and since defectors gain disproportionately more when they exploit cooperators, inequality may increase. In contrast, exploitative behavior is not possible in networks that are regularly reshuffled and can be costlessly punished with exclusion in networks that allow choosing partners strategically. In short, higher clustering of cooperators and more exploitation by defectors would imply higher inequality in fixed networks compared to networks in which interaction partners change randomly. But the comparison between fixed networks and networks in which individuals can select their interaction partners is less straightforward. The latter structures preclude exploitative defectors due to the possibility to exclude them but enable even higher segregation due to the possibility to self-select into cooperative clusters. However, previous research has clearly shown that strategic partner selection essentially eradicates defection [ 40 , 44 , 45 ], so again, we would expect fixed networks to result in more inequality. To extrapolate to a concrete social setting, the hypotheses would imply, for example, that sedentary agrarian villages, which are marked by relatively stable social relations, have higher inequality in material and immaterial wealth than nomadic groups, which tend to change composition and structure on a regular basis.

Reputation tracking.

Reputation institutions, which make available information about everyone’s past behavior towards others, present another factor that is expected to increase inequality in social groups. Previous research has shown that individuals are more likely to select partners who have reputation as cooperators [ 24 ], as well as more likely to cooperate with them [ 29 ]. This implies that initial differences in cooperative behavior could have long-lasting effects that get reinforced and exaggerated over time. As a result, social groups where reputation is visible will have higher inequality than groups without reputation tracking. Such an effect would have important implications for the design of online marketplaces and social media communities, for example.

Punishment institutions.

Finally, the possibility for peer punishment is also expected to increase inequality in social groups. Previous research on the Prisoner’s Dilemma game has shown that the poor are more likely to pay to punish defecting partners; in contrast, the wealthy are more likely to use a tit-for-tat strategy and defect in response to defection [ 32 ]. This suggests the poor will use costly punishment disproportionately, which will make them even poorer compared to the wealthy. As a result, punishment will increase not only cooperation, but also inequality. To give a concrete example, this hypothesis would imply that communities relying on peer monitoring and punishment would have higher inequality than communities in which everyone is required to contribute equally, or even proportionally to wealth, to a single centralized sanctioning institution.

In the following, we investigate whether inequality is higher in groups interacting in clustered networks compared to random networks, in fixed networks compared to randomly or strategically rewired networks, with punishment institutions compared to no possibility for costly punishment, and with reputation tracking compared to without. In addition, we look for possible explanations for the observed results related to the clustering of cooperators, ease of excluding exploitative defectors, and differential punishment behavior between successful and unsuccessful players.

Detailed information about the data, the definitions, the measures and the statistical tests we use in the study can be found at the end of the article in the section Materials and Methods. Table 1 summarizes the data we use for the analyses. The data come from 18 previously published studies and to identify the different data sets, we use an abbreviation constructed from the first four letters of the first author’s last name and the last two digits of the study’s year of publication. Some of the studies have substantially different orthogonal treatment that allows us to split them into separate experiments, which we mark with a lower-case letter appended at the end of the name. This gives us 33 experiments. The experiments vary in terms of the game used (Prisoner’s Dilemma, Public Good, or helping), the size of the interaction group (from 8 to 625), the number of interaction groups (from 3 to 84), the number of players’ interaction partners (from 1 to all other group members), the number of periods of interaction (from 6 to more than 90), and whether the experimental design is within-subject (participants play in more than one experimental group) or within-group (groups play in more than one experimental condition). Further, each experiment can have multiple treatment conditions.

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https://doi.org/10.1371/journal.pone.0200965.t001

Since we rely on experimental data, our tests of the main hypotheses are causal. However, since we execute our analyses at the group level, we cannot clearly disentangle the individual-level mechanisms. Nevertheless, we use observational analysis techniques to provide some descriptive and exploratory evidence as to the extent to which the assumed mechanisms play a role.

We first compare the level of inequality at the end of the games between control-treatment pairs. We measure inequality with the Gini coefficient and use the Mann-Whitney test to assess differences between the control and each treatment. However, this evidence is not always sufficient because the experiments were not designed to test group-level hypotheses nor to study inequality, so only on several occasions we have large enough effect sizes and enough statistical power to provide evidence at the level of a single experiment. Instead, what we do is to treat each of the very different control-treatment pairs as independent trials and to focus on the direction of the effect, rather than its size and significance. This allows us to use an established meta-analytic approach known as the sign test [ 46 ]. In essence, the sign test establishes whether the distribution of observed effects is significantly different from the 50 negative/50 positive distribution we expect if, in reality, there was no causal effect from the particular structural condition on inequality (see Materials and methods ).

For the effect of network structure on inequality, we compare the Gini coefficient in the least clustered networks (usually the random networks) to the other types of networks in the experiment. In SURI11 we find that paired cliques and cliques have significantly higher inequality than random networks ( Fig 1A ). We confirm the finding about clique networks in WANG12, which uses the Prisoner’s Dilemma instead of the Public Goods game. The results for the other types of networks and in the other experiments are not statistically significant but overall, eight of the nine control-treatment differences point in the same direction. This indicates that more clustered networks have significantly higher inequality (Binomial test, 1-sided p = 0.020).

The figure shows boxplots for each experimental condition and results from the Mann-Whitney tests comparing each treatment condition to the control condition within each experiment (Mann-Whitney U on top and p -value on bottom, with asterisk if p < 0.05). For each experiment, the first bar shown in the figure is the control condition and each test result compares this control condition to the treatment conditions represented by each next bar in order. Explanation of the experimental conditions can be found in Table 1 . In addition, for GRUJ10, “fixed*” refers to a second fixed condition played by the same group and for WANG12, “strategic a / b ” means that participants can make up to a partner updates in each of b partner-update periods of the game.

https://doi.org/10.1371/journal.pone.0200965.g001

For the effect of network fluidity, we compare the Gini coefficients in fixed networks to randomly rewired, or shuffled, networks ( Fig 1B ) and to strategically updated networks ( Fig 1C ). As expected, fixed networks have higher inequality than shuffled networks (Binomial test, 1-sided p = 0.008). The direction of the effect is the same in all seven experiments, although it is significant only in two of the RAND14 setups. Fixed networks also appear to have higher inequality than strategically updated networks. The results are significant in the PAGE05 experiment with punishment and in the WANG12 experiments starting from clique networks and varying the level of rewiring. These results were also found in another published study that investigated the effect of strategic network updating on inequality [ 47 ]. Based on the data we analyze here, 14 out of the 20 control-treatment differences show higher inequality for fixed networks compared to networks with strategic updating. The sign test is marginally not significant at the 0.05 level (Binomial test, 1-sided p = 0.058). However, adding the results from [ 47 ], for which we did not have raw data, gives us 22 out of 28 and stronger statistical evidence overall (Binomial test, 1-sided p = 0.002).

For the effect of reputation, we compare any conditions with visible reputation to the condition without. We expected that reputation tracking increases inequality but the effects in the different experiments appear to go in both directions ( Fig 1D ). We observe significantly higher inequality in groups with reputation compared to without in CUES15 and KIRC07 but exactly the opposite result in SEIN06, while in BOLT05 and KAME17 the effects are in both directions and non-significant. Overall, only nine out of the 15 differences point in the expected direction (Binomial test, 1-sided p = 0.304). This suggests that either there is no effect of reputation, or the effect of reputation is complex and crucially depends on some of the other structural conditions.

Finally, groups with punishment have higher levels of inequality in ten out of the 11 control-treatment comparisons with the effects being significant in the CASA09 and OGOR09 experiments ( Fig 1D ). In other words, the existence of punishment institutions significantly increases inequality (Binomial test, 1-sided p = 0.006).

We next analyze the individual behavior and the network dynamics in the experiments to investigate possible explanations for the observed higher levels of inequality. Unfortunately, we do not have individual and network data for all of the experiments, so we conduct our analyses on a smaller subset. We use observational data techniques to provide suggestive evidence on the different behavioral mechanisms behind the main effects that we presupposed.

First, we investigate the extent to which the emergence of cooperative clusters could explain the higher inequality we observe in fixed and clustered networks. We measure the clustering of cooperators with the network assortativity by cooperativeness in the final period. We find that assortativity by cooperativeness is significantly higher than expected for the most clustered fixed networks in SURI11 and CASS07 ( S1A Fig ), which also show higher inequality than random networks ( Fig 1A ). Furthermore, in SURI11 we also find that cycles and paired cliques have significantly higher assortativity by cooperativeness than random networks. Although these effects do not explain all of the results, they suggest that the emergence of cooperative clusters is a plausible pathway via which clustered networks increase inequality, at least in some cases.

Exploitation, whereby individuals get tempted to defect in fixed networks since the cooperators around them cannot move away, could be another pathway for inequality to increase. We find suggestive evidence for this in S1B and S2B Figs, which show that in many cases fixed networks have lower network assortativity by cooperativeness and wealth than randomly rewired or strategic networks. Additionally, we see that in fixed networks both defectors and cooperators could achieve the highest wealth ( S3 – S5 Figs). This suggests that, when networks are fixed, inequality could increase due to both the clustering of the cooperators and the exploitation of these clusters by defectors.

To investigate whether punishment institutions increase inequality because the poor pay for punishment disproportionately more than the wealthy, we correlate the tendency to use punishment with wealth and cooperativeness. In alignment with [ 32 ], we find overwhelming evidence that this is the case: in all 11 punishment treatments, wealth and the use of punishment are negatively correlated ( S6 Fig ; Binomial test, 1-sided p = 0.000). This finding cannot be explained with cooperativeness, as we actually find no systematic relation between being cooperative and using punishment ( S7 Fig ; Binomial test, 1-sided p = 0.274). Of course, with the data we have we cannot establish whether the wealthy do not punish or whether those who do not punish become wealthy.

We analyzed data from a number of experiments on repeated cooperation games in networks to study how network structure, network fluidity, reputation tracking, and punishment institutions affect inequality in social groups. The results confirm that there is more inequality in clustered networks compared to random networks, fixed networks compared to randomly rewired networks, and groups with punishment institutions compared to groups without. We also find convincing evidence that fixed networks have higher inequality than networks with strategic updating. These conditions give rise to inequality because they affect either the structure of interactions or the behavior of individuals in a way that differences between the wealthy and the poor grow. Fixed networks allow exploitation of the poor by the wealthy (similarly to the dynamic discovered in [ 23 ]), clustered fixed networks foster segregation between the poor and the wealthy, and costly punishment imposes a disproportionate burden on the poor, making them poorer.

Surprisingly, we do not find evidence that inequality increases when reputation is visible. Given that the experiments we analyze differ on many dimensions, we are unwilling to take this as a proof that reputation has no effect on inequality; rather, we believe that reputation needs to interact with another structural condition to play a role. In the experiments that we find to confirm the expected effect, interaction is situated in a network (whether fixed or strategically updated) and not in pairs that get re-matched in each period. This would suggest that the effect of reputation is perhaps only enacted in more stable networks and perhaps even more so when exclusion is possible. Future research should investigate these ideas.

The findings from our study should be interpreted within the confines of our data-driven approach. The experiments we analyzed were not designed to study inequality and they were rarely large enough to test group-level hypotheses. However, although only a handful of the individual experiments may be convincing by themselves, the fact that we found the same effects across so many different experimental setups provides strong evidence for the hypothesized causes of inequality. Nevertheless, as is often the case with scientific research, our study is not the definitive answer to the question of what causes inequality to emerge in social groups. Ideally, our findings will be replicated and extended using carefully designed large-group experiments, as well as validated with observational data from different social contexts. Further, future research should develop experiments to test a more complete set of structural conditions and mechanisms that cause inequality. For example, it is worth mentioning that the levels of inequality we observe in our experiments are relatively low. Compared to national-level wealth inequality, the highest Gini coefficient in our data of 0.34 is well below what we observe in any country [ 4 ]. This is to be expected because the experiments we analyzed did not allow for the investment of wealth, as in [ 48 ]. Thus, one possible avenue for future research is to experimentally test how different investment rules and opportunities affect inequality in social groups.

The significance of our findings depends on the extent to which one considers inequality to be an undesirable social problem. One argument against worrying about inequality is that it might fairly reflect differences between individuals. In the context of network games, perhaps inequality is not so bad if cooperators are rewarded, while defectors struggle. Unfortunately, as we saw above, this is not always the case ( S3 – S5 Figs). Another argument for maintaining inequality is that inequality increases the collective wealth and well-being is thus essential for prosperity. Again, our data show poor support for this argument: there is no evidence for a systematic relationship between inequality and average wealth ( S8 and S9 Figs; Binomial test, 1-sided p = 0.243). In other words, slightly different incentives, information, or rules may not affect the collective wealth but may significantly skew how it is distributed in the group, irrespective of common-sense ideas of fairness; inequality in social groups can indeed be problematic.

Overall, our study suggests that highly clustered fixed networks where punishment is possible have higher inequality than more unstable communities. This finding has an interesting parallel to previous research showing that sedentary agrarian communities have higher inequality than nomadic communities [ 49 , 50 ]. This research suggested that inequality is higher in agrarian communities due to the possibility to accumulate wealth and transmit it to subsequent generations. Our study points to additional structural conditions that could explain the difference: agrarian communities are likely to have higher inequality because they are stable and with functioning punishment institutions. These structural characteristics enable self-reinforcing social processes that create and perpetuate divisions between the wealthy and the poor. Our findings also provide insights for managing inequality in organizations such as companies, universities, hospitals, and online communities around user-generated content sites and peer-to-peer markets. In such social settings, more dynamic and less locally clustered interaction structures that do not encourage costly mutual sanctioning could enable a more even distribution of resources and gains.

Materials and methods

To identify suitable data, we conducted online searches on the Scopus database and the Google Scholar search engine. Our searches looked for articles with the words “cooperation”, “game”, and “experiment”, together with one of “punishment”, “reputation”, or “network”, in their title and abstract. When we identified a relevant study, we then also investigated the articles cited by it. In general, we restricted our interest to experiments in groups of at least 10 (although we did not exclude the smaller eight-person groups in [ 31 ] for our analyses). We thus identified 21 relevant studies and contacted the corresponding authors. In the end, we obtained data for 18 of them.

We measure wealth with the amount of points the player has amassed at the end of the game. We then use the Gini coefficient to measure inequality in wealth. A Gini coefficient of 0 indicates perfect equality where all group members have equal resources, while 1 indicates perfect inequality where a single individual commands all of the resources. The Gini coefficient is the most common measure of inequality but nevertheless has some limitations. In particular, it is sensitive to changes in inequality among those in the middle of the distribution and not so much to those at the lower end of the distribution [ 51 ]. We replicated our analyses using another measure, the Theil index, and the results are qualitatively similar.

To measure cooperativeness, we calculate the mean number (or amount) of contributions over all periods. To measure the tendency for clustering, we calculate the network assortativity by cooperativeness and wealth in the final period. For continuous measures, which is the case for cooperativeness and wealth here, network assortativity is essentially the correlation in the measure for all connected pairs [ 52 ].

Statistical tests

To test differences between experimental conditions for inequality, we use the Mann-Whitney U test. This is a non-parametric test that does not assume a normal distribution for the residuals. It essentially checks against the null hypothesis that a randomly selected value from one condition would be equally likely to be less than or greater than a randomly selected value from the other condition. For the experiments that have a within-group design, whereby groups interact in more than one experimental condition, we considered using the Wilcoxon signed rank sum test but unfortunately, the sample sizes were always too small to have reliable results. Consequently, we only report the Mann-Whitney U in Fig 1 . This limitation is not severe because our main results are based on the meta-analysis of the direction of all control-treatment differences, rather than the precise test of any individual one.

To test the statistical significance of the observed differences between control and treatment across all relevant experiments, we use a meta-analysis technique known as the sign test [ 46 ]. This technique allows us to focus on whether the effect exists, rather than to accurately measure its size. We first count the number of positive and negative effects, regardless of whether they are statistically significant. We then conduct the Binomial test, testing against the null hypothesis that there is no effect in reality and thus negative and positive effects are equally likely to occur by chance. This approach is somewhat limited because it does not take into account the amount of evidence: neither the effect magnitudes nor the sample sizes. Yet, it is best suited for the research question and experimental data we have. First, although meta analyses typically aim to estimate the effect size, effect sizes in controlled social experiments are not very meaningful as they are highly sensitive to the experimental design and, in particular, aspects such as the framing of the decision situation, the monetary incentives, the experience of the participant pool, and the experimenter demand effect [ 53 ]. In relying on experimental data, our major objective here is to prove a causal relationship and the sign test is perfectly sufficient for that. Second, pooling effect sizes from studies with different designs and research populations is usually problematic in meta analysis but for us, the variation in our data sources is a boon, not a drawback. Experiments are often criticized for their lack of replicability and validity, so being able to confirm the same effect repeatedly in completely different experimental settings only reaffirms the robustness of the result.

To test the significance of the observed network assortativity by cooperativeness and wealth, we need a suitable baseline. The reason is that network assortativity depends both on the connectivity patterns and on the distribution of values of interest. For the baseline, we take the actual final structure in the networks, randomly shuffle the nodes’ cooperativeness and wealth values, and estimate the new assortativity. We repeat this 2000 times, which gives us an expected distribution against which we can estimate the z-score for the empirically observed value. The z-score is estimated using Z = ( X obs − μ ( X ran ))/ σ ( X ran ), where X obs is the assortativity in the observed network, X ran is the network assortativity in the randomly shuffled network, μ is the mean, and σ is the standard deviation. If the z-score is above 1.96 (below −1.96), it implies that the chance to obtain the high (low) network assortativity we actually observe under the assumption that the network formation processes are random is less than 5%. Regardless of whether the network assortativity by cooperation and wealth is significantly different than what we would expect by chance, we can also test whether it is significantly different between experimental conditions. As with inequality, we use Mann-Whitney test to do this ( S1 and S2 Figs).

To estimate the correlations between individual wealth and cooperativeness, wealth and the use of punishment, cooperativeness and the use of punishment, as well as group inequality and wealth ( S3 – S9 Figs), we use ordinary least-square regressions with standard errors accounting for clustering by experimental groups.

Supporting information

S1 fig. z-scores for network assortativity by cooperativeness for fixed and strategically rewired networks from a subset of the experiments on (a) network structure, (b) network fluidity, and (c) reputation tracking..

The z-score shows to what extent the observed assortativity by cooperativeness for each experimental condition deviates from what we expect to see in a network with the same structure but with cooperativeness randomly assigned to nodes. The figure shows the z-score distribution and results from the Mann-Whitney test comparing each treatment condition to the control condition (Mann-Whitney U on top and p -value on bottom, with asterisk if p < 0.05). The grey band spans from Z = −1.96 to Z = 1.96, equivalent to p ≥ 0.05. For each experiment, the first bar shown in the figure is the control condition and each test result compares this control condition to the treatment conditions represented by each next bar in order.

https://doi.org/10.1371/journal.pone.0200965.s001

S2 Fig. Z-scores for network assortativity by wealth for fixed and strategically rewired networks from a subset of the experiments on (A) network structure, (B) network fluidity, and (C) reputation tracking.

The z-score shows to what extent the observed assortativity by wealth for each experimental condition deviates from what we expect to see in a network with the same structure but with wealth randomly assigned to nodes. The figure shows the z-score distribution and results from the Mann-Whitney test comparing each treatment condition to the control condition (Mann-Whitney U on top and p -value on bottom, with asterisk if p < 0.05). The grey band spans from Z = −1.96 to Z = 1.96, equivalent to p ≥ 0.05. For each experiment, the first bar shown in the figure is the control condition and each test result compares this control condition to the treatment conditions represented by each next bar in order.

https://doi.org/10.1371/journal.pone.0200965.s002

S3 Fig. The relationship between individual cooperativeness and individual wealth in the conditions from the experiments on network structure.

In each plot, values for individuals in the same interaction group are shown with the same symbol. The figure also shows fitted curves and estimates from ordinary least-square regressions (standardized regression coefficient for linear term on top and quadratic term on bottom, including p -values in brackets, with asterisk if p < 0.05). The standard errors in the regression models are estimated with correction for clustering by experimental group.

https://doi.org/10.1371/journal.pone.0200965.s003

S4 Fig. The relationship between individual cooperativeness and individual wealth in the conditions from the experiments on network fluidity.

https://doi.org/10.1371/journal.pone.0200965.s004

S5 Fig. The relationship between individual cooperativeness and individual wealth in the conditions from the experiments on network fluidity.

https://doi.org/10.1371/journal.pone.0200965.s005

S6 Fig. The relationship between individual use of punishment and individual wealth in the treatment conditions with punishment.

In each plot, values for individuals in the same interaction group are shown with the same symbol. The figure also shows fitted lines and estimates from ordinary least-square regressions (standardized regression coefficient, equivalent to the Pearson correlation, on top and p -value on bottom, with asterisk if p < 0.05). The standard errors in the regression models are estimated with correction for clustering by experimental group.

https://doi.org/10.1371/journal.pone.0200965.s006

S7 Fig. The relationship between individual use of punishment and individual cooperativeness in the treatment conditions with punishment.

https://doi.org/10.1371/journal.pone.0200965.s007

S8 Fig. The relationship between group average wealth and group inequality in the experiments on network structure and network fluidity.

The figure shows fitted lines and estimates from ordinary least-square regressions (standardized regression coefficient, equivalent to the Pearson correlation, on top and p -value on bottom, with asterisk if p < 0.05). The standard errors in the regression models are estimated with correction for clustering by experimental group. Colors correspond to experimental conditions as in Fig 1A–1C .

https://doi.org/10.1371/journal.pone.0200965.s008

S9 Fig. The relationship between group average wealth and group inequality in the experiments on reputation and punishment.

The figure shows fitted lines and estimates from ordinary least-square regressions (standardized regression coefficient, equivalent to the Pearson correlation, on top and p -value on bottom, with asterisk if p < 0.05). The standard errors in the regression models are estimated with correction for clustering by experimental group. Colors correspond to experimental conditions as in Fig 1D and 1E .

https://doi.org/10.1371/journal.pone.0200965.s009

Acknowledgments

We would like to thank Gary Bolton, Marco Casari, Jose Cuesta, Alessandra Cassar, Ernst Fehr, Jelena Grujic, Kenju Kamei, Oliver Kirchkamp, Yamir Moreno, Nikos Nikiforakis, Rick O’Gorman, Louis Putterman, David Rand, Angel Sánchez, Arthur Schram, Sid Suri, and Arne Traulsen for sharing data with us.

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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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6.1 Social Groups

Learning objectives.

• Describe how a social group differs from a social category or social aggregate.
• Distinguish a primary group from a secondary group.
• Define a reference group and provide one example of such a group.
• Explain the importance of networks in a modern society.

A social group consists of two or more people who regularly interact on the basis of mutual expectations and who share a common identity. It is easy to see from this definition that we all belong to many types of social groups: our families, our different friendship groups, the sociology class and other courses we attend, our workplaces, the clubs and organizations to which we belong, and so forth. Except in rare cases, it is difficult to imagine any of us living totally alone. Even people who live by themselves still interact with family members, coworkers, and friends and to this extent still have several group memberships.

It is important here to distinguish social groups from two related concepts: social categories and social aggregates. A social category is a collection of individuals who have at least one attribute in common but otherwise do not necessarily interact. Women is an example of a social category. All women have at least one thing in common, their biological sex, even though they do not interact. Asian Americans is another example of a social category, as all Asian Americans have two things in common, their ethnic background and their residence in the United States, even if they do not interact or share any other similarities. As these examples suggest, gender, race, and ethnicity are the basis for several social categories. Other common social categories are based on our religious preference, geographical residence, and social class.

Falling between a social category and a social group is the social aggregate , which is a collection of people who are in the same place at the same time but who otherwise do not necessarily interact, except in the most superficial of ways, or have anything else in common. The crowd at a sporting event and the audience at a movie or play are common examples of social aggregates. These collections of people are not a social category, because the people are together physically, and they are also not a group, because they do not really interact and do not have a common identity unrelated to being in the crowd or audience at that moment.

A social aggregate is a collection of people who are in the same place at the same time but who otherwise have nothing else in common. A crowd at a sporting event and the audience at a movie or play are examples of social aggregates.

Eliud Gil Samaniego – Art – Aguilas de Mexicali – CC BY-NC-ND 2.0.

With these distinctions laid out, let’s return to our study of groups by looking at the different types of groups sociologists have delineated.

Primary and Secondary Groups

A common distinction is made between primary groups and secondary groups. A primary group is usually small, is characterized by extensive interaction and strong emotional ties, and endures over time. Members of such groups care a lot about each other and identify strongly with the group. Indeed, their membership in a primary group gives them much of their social identity. Charles Horton Cooley, whose looking-glass-self concept was discussed in Chapter 5 “Social Structure and Social Interaction” , called these groups primary , because they are the first groups we belong to and because they are so important for social life. The family is the primary group that comes most readily to mind, but small peer friendship groups, whether they are your high school friends, an urban street gang, or middle-aged adults who get together regularly, are also primary groups.

Although a primary group is usually small, somewhat larger groups can also act much like primary groups. Here athletic teams, fraternities, and sororities come to mind. Although these groups are larger than the typical family or small circle of friends, the emotional bonds their members form are often quite intense. In some workplaces, coworkers can get to know each other very well and become a friendship group in which the members discuss personal concerns and interact outside the workplace. To the extent this happens, small groups of coworkers can become primary groups (Elsesser & Peplau, 2006; Marks, 1994).

Our primary groups play significant roles in so much that we do. Survey evidence bears this out for the family. Figure 6.1 “Percentage of Americans Who Say Their Family Is Very Important, Quite Important, Not Too Important, or Not at All Important in Their Lives” shows that an overwhelming majority of Americans say their family is “very important” in their lives. Would you say the same for your family?

Figure 6.1 Percentage of Americans Who Say Their Family Is Very Important, Quite Important, Not Too Important, or Not at All Important in Their Lives

Source: Data from World Values Survey, 2002.

Ideally, our primary groups give us emotional warmth and comfort in good times and bad and provide us an identity and a strong sense of loyalty and belonging. Our primary group memberships are thus important for such things as our happiness and mental health. Much research, for example, shows rates of suicide and emotional problems are lower among people involved with social support networks such as their families and friends than among people who are pretty much alone (Maimon & Kuhl, 2008). However, our primary group relationships may also not be ideal, and, if they are negative ones, they may cause us much mental and emotional distress. In this regard, the family as a primary group is the setting for much physical and sexual violence committed against women and children (Gosselin, 2010) (see Chapter 11 “Gender and Gender Inequality” ).

A secondary group is larger and more impersonal than a primary group and may exist for a relatively short time to achieve a specific purpose. The students in any one of your college courses constitute a secondary group.

Jeremy Wilburn – Students in Classrooms at UIS – CC BY-NC-ND 2.0.

Although primary groups are the most important ones in our lives, we belong to many more secondary groups , which are groups that are larger and more impersonal and exist, often for a relatively short time, to achieve a specific purpose. Secondary group members feel less emotionally attached to each other than do primary group members and do not identify as much with their group nor feel as loyal to it. This does not mean secondary groups are unimportant, as society could not exist without them, but they still do not provide the potential emotional benefits for their members that primary groups ideally do. The sociology class for which you are reading this book is an example of a secondary group, as are the clubs and organizations on your campus to which you might belong. Other secondary groups include religious, business, governmental, and civic organizations. In some of these groups, members get to know each other better than in other secondary groups, but their emotional ties and intensity of interaction generally remain much weaker than in primary groups.

Reference Groups

Primary and secondary groups can act both as our reference groups or as groups that set a standard for guiding our own behavior and attitudes. The family we belong to obviously affects our actions and views, as, for example, there were probably times during your adolescence when you decided not to do certain things with your friends to avoid disappointing or upsetting your parents. On the other hand, your friends regularly acted during your adolescence as a reference group, and you probably dressed the way they did or did things with them, even against your parents’ wishes, precisely because they were your reference group. Some of our reference groups are groups to which we do not belong but to which we nonetheless want to belong. A small child, for example, may dream of becoming an astronaut and dress like one and play like one. Some high school students may not belong to the “cool” clique in school but may still dress like the members of this clique, either in hopes of being accepted as a member or simply because they admire the dress and style of its members.

Samuel Stouffer and colleagues (Stouffer, Suchman, DeVinney, Star, & Williams, 1949) demonstrated the importance of reference groups in a well-known study of American soldiers during World War II. This study sought to determine why some soldiers were more likely than others to have low morale. Surprisingly, Stouffer found that the actual, “objective” nature of their living conditions affected their morale less than whether they felt other soldiers were better or worse off than they were. Even if their own living conditions were fairly good, they were likely to have low morale if they thought other soldiers were doing better. Another factor affecting their morale was whether they thought they had a good chance of being promoted. Soldiers in units with high promotion rates were, paradoxically, more pessimistic about their own chances of promotion than soldiers in units with low promotion rates. Evidently the former soldiers were dismayed by seeing so many other men in their unit getting promoted and felt worse off as a result. In each case, Stouffer concluded, the soldiers’ views were shaped by their perceptions of what was happening in their reference group of other soldiers. They felt deprived relative to the experiences of the members of their reference group and adjusted their views accordingly. The concept of relative deprivation captures this process.

In-Groups and Out-Groups

Members of primary and some secondary groups feel loyal to those groups and take pride in belonging to them. We call such groups in-groups . Fraternities, sororities, sports teams, and juvenile gangs are examples of in-groups. Members of an in-group often end up competing with members of another group for various kinds of rewards. This other group is called an out-group . The competition between in-groups and out-groups is often friendly, as among members of intramural teams during the academic year when they vie in athletic events. Sometimes, however, in-group members look down their noses at out-group members and even act very hostilely toward them. Rival fraternity members at several campuses have been known to get into fights and trash each other’s houses. More seriously, street gangs attack each other, and hate groups such as skinheads and the Ku Klux Klan have committed violence against people of color, Jews, and other individuals they consider members of out-groups. As these examples make clear, in-group membership can promote very negative attitudes toward the out-groups with which the in-groups feel they are competing. These attitudes are especially likely to develop in times of rising unemployment and other types of economic distress, as in-group members are apt to blame out-group members for their economic problems (Olzak, 1992).

Social Networks

These days in the job world we often hear of “networking,” or taking advantage of your connections with people who have connections to other people who can help you land a job. You do not necessarily know these “other people” who ultimately can help you, but you do know the people who know them. Your ties to the other people are weak or nonexistent, but your involvement in this network may nonetheless help you find a job.

Modern life is increasingly characterized by such social networks , or the totality of relationships that link us to other people and groups and through them to still other people and groups. Some of these relationships involve strong bonds, while other relationships involve weak bonds (Granovetter, 1983). Facebook and other Web sites have made possible networks of a size unimaginable just a decade ago. Social networks are important for many things, including getting advice, borrowing small amounts of money, and finding a job. When you need advice or want to borrow $5 or $10, to whom do you turn? The answer is undoubtedly certain members of your social networks—your friends, family, and so forth.

The indirect links you have to people through your social networks can help you find a job or even receive better medical care. For example, if you come down with a serious condition such as cancer, you would probably first talk with your primary care physician, who would refer you to one or more specialists whom you do not know and who have no connections to you through other people you know. That is, they are not part of your social network. Because the specialists do not know you and do not know anyone else who knows you, they are likely to treat you very professionally, which means, for better or worse, impersonally.

A social network is the totality of relationships that link us to other people and groups and through them to still other people and groups. Our involvement in certain networks can bring certain advantages, including better medical care if one’s network includes a physician or two.

Gavin Llewellyn – My social networks – CC BY 2.0.

Now suppose you have some nearby friends or relatives who are physicians. Because of their connections with other nearby physicians, they can recommend certain specialists to you and perhaps even get you an earlier appointment than your primary physician could. Because these specialists realize you know physicians they know, they may treat you more personally than otherwise. In the long run, you may well get better medical care from your network through the physicians you know. People lucky enough to have such connections may thus be better off medically than people who do not.

But let’s look at this last sentence. What kinds of people have such connections? What kinds of people have friends or relatives who are physicians? All other things being equal, if you had two people standing before you, one employed as a vice president in a large corporation and the other working part time at a fast-food restaurant, which person do you think would be more likely to know a physician or two personally? Your answer is probably the corporate vice president. The point is that factors such as our social class and occupational status, our race and ethnicity, and our gender affect how likely we are to have social networks that can help us get jobs, good medical care, and other advantages. As just one example, a study of three working-class neighborhoods in New York City—one white, one African American, and one Latino—found that white youths were more involved through their parents and peers in job-referral networks than youths in the other two neighborhoods and thus were better able to find jobs, even if they had been arrested for delinquency (Sullivan, 1989). This study suggests that even if we look at people of different races and ethnicities in roughly the same social class, whites have an advantage over people of color in the employment world.

Gender also matters in the employment world. In many businesses, there still exists an “old boys’ network,” in which male executives with job openings hear about male applicants from male colleagues and friends. Male employees already on the job tend to spend more social time with their male bosses than do their female counterparts. These related processes make it more difficult for females than for males to be hired and promoted (Barreto, Ryan, & Schmitt, 2009). To counter these effects and to help support each other, some women form networks where they meet, talk about mutual problems, and discuss ways of dealing with these problems. An example of such a network is The Links, Inc., a community service group of 12,000 professional African American women whose name underscores the importance of networking ( http://www.linksinc.org/index.shtml ). Its members participate in 270 chapters in 42 states; Washington, DC; and the Bahamas. Every two years, more than 2,000 Links members convene for a national assembly at which they network, discuss the problems they face as professional women of color, and consider fund-raising strategies for the causes they support.

Key Takeaways

• Groups are a key building block of social life but can also have negative consequences.
• Primary groups are generally small and include intimate relationships, while secondary groups are larger and more impersonal.
• Reference groups provide a standard for guiding and evaluating our attitudes and behaviors.
• Social networks are increasingly important in modern life, and involvement in such networks may have favorable consequences for many aspects of one’s life.

For Your Review

• Briefly describe one reference group that has influenced your attitudes or behavior, and explain why it had this influence on you.
• Briefly describe an example of when one of your social networks proved helpful to you (or describe an example when a social network helped someone you know).
• List at least five secondary groups to which you now belong and/or to which you previously belonged.

Barreto, M., Ryan, M. K., & Schmitt, M. T. (Eds.). (2009). The glass ceiling in the 21st century: Understanding barriers to gender equality . Washington, DC: American Psychological Association.

Elsesser, K., & Peplau L. A. (2006). The glass partition: Obstacles to cross-sex friendships at work. Human Relations, 59 , 1077–1100.

Gosselin, D. K. (2010). Heavy hands: An introduction to the crimes of family violence (4th ed.). Upper Saddle River, NJ: Prentice Hall.

Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1, 201–233.

Maimon, D., & Kuhl, D. C. (2008). Social control and youth suicidality: Situating Durkheim’s ideas in a multilevel framework. American Sociological Review, 73, 921–943.

Marks, S. R. (1994). Intimacy in the public realm: The case of co-workers. Social Forces, 72, 843–858.

Olzak, S. (1992). The dynamics of ethnic competition and conflict . Stanford, CA: Stanford University Press.

Stouffer, S. A., Suchman, E. A., DeVinney, L. C., Star, S. A., & Williams, R. M., Jr. (1949). The American soldier: Adjustment during army life (Studies in Social Psychology in World War II, Vol. 1). Princeton, NJ: Princeton University Press.

Sullivan, M. (1989). Getting paid: Youth crime and work in the inner city . Ithaca, NY: Cornell University Press.

Sociology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Conducting Case Study Research in Sociology

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A case study is a research method that relies on a single case rather than a population or sample. When researchers focus on a single case, they can make detailed observations over a long period of time, something that cannot be done with large samples without costing a lot of money. Case studies are also useful in the early stages of research when the goal is to explore ideas, test, and perfect measurement instruments, and to prepare for a larger study. The case study research method is popular not just within ​the field of sociology, but also within the fields of anthropology, psychology, education, political science, clinical science, social work, and administrative science.

Overview of the Case Study Research Method

A case study is unique within the social sciences for its focus of study on a single entity, which can be a person, group or organization, event, action, or situation. It is also unique in that, as a focus of research, a case is chosen for specific reasons, rather than randomly , as is usually done when conducting empirical research. Often, when researchers use the case study method, they focus on a case that is exceptional in some way because it is possible to learn a lot about social relationships and social forces when studying those things that deviate from norms. In doing so, a researcher is often able, through their study, to test the validity of the social theory, or to create new theories using the grounded theory method .

The first case studies in the social sciences were likely conducted by Pierre Guillaume Frédéric Le Play, a 19th-century French sociologist and economist who studied family budgets. The method has been used in sociology, psychology, and anthropology since the early 20th century.

Within sociology, case studies are typically conducted with qualitative research methods . They are considered micro rather than macro in nature , and one cannot necessarily generalize the findings of a case study to other situations. However, this is not a limitation of the method, but a strength. Through a case study based on ethnographic observation and interviews, among other methods, sociologists can illuminate otherwise hard to see and understand social relations, structures, and processes. In doing so, the findings of case studies often stimulate further research.

Types and Forms of Case Studies

There are three primary types of case studies: key cases, outlier cases, and local knowledge cases.

• Key cases are those which are chosen because the researcher has ​a particular interest in it or the circumstances surrounding it.
• Outlier cases are those that are chosen because the case stands out from other events, organizations, or situations, for some reason, and social scientists recognize that we can learn a lot from those things that differ from the norm .
• Finally, a researcher may decide to conduct a local knowledge case study when they already have amassed a usable amount of information about a given topic, person, organization, or event, and so is well-poised to conduct a study of it.

Within these types, a case study may take four different forms: illustrative, exploratory, cumulative, and critical.

• Illustrative case studies are descriptive in nature and designed to shed light on a particular situation, set of circumstances, and the social relations and processes that are embedded in them. They are useful in bringing to light something about which most people are not aware of.
• Exploratory case studies are also often known as pilot studies . This type of case study is typically used when a researcher wants to identify research questions and methods of study for a large, complex study. They are useful for clarifying the research process, which can help a researcher make the best use of time and resources in the larger study that will follow it.
• Cumulative case studies are those in which a researcher pulls together already completed case studies on a particular topic. They are useful in helping researchers to make generalizations from studies that have something in common.
• Critical instance case studies are conducted when a researcher wants to understand what happened with a unique event and/or to challenge commonly held assumptions about it that may be faulty due to a lack of critical understanding.

Whatever type and form of case study you decide to conduct, it's important to first identify the purpose, goals, and approach for conducting methodologically sound research.

• Definition of Idiographic and Nomothetic
• Pilot Study in Research
• The Different Types of Sampling Designs in Sociology
• Understanding Purposive Sampling
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• Introduction to Sociology
• All About Marxist Sociology
• Definition of Cultural Materialism
• Abstract Writing for Sociology
• The Sociology of Race and Ethnicity
• Definition and Overview of Grounded Theory
• Definition of Aggregate and Social Aggregate
• Macro- and Microsociology
• Cluster Sample in Sociology Research
• How W.E.B. Du Bois Made His Mark on Sociology
• Biography of Patricia Hill Collins, Esteemed Sociologist

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13 The Psychology of Groups

This module assumes that a thorough understanding of people requires a thorough understanding of groups. Each of us is an autonomous individual seeking our own objectives, yet we are also members of groups—groups that constrain us, guide us, and sustain us. Just as each of us influences the group and the people in the group, so, too, do groups change each one of us. Joining groups satisfies our need to belong, gain information and understanding through social comparison, define our sense of self and social identity, and achieve goals that might elude us if we worked alone. Groups are also practically significant, for much of the world’s work is done by groups rather than by individuals. Success sometimes eludes our groups, but when group members learn to work together as a cohesive team their success becomes more certain. People also turn to groups when important decisions must be made, and this choice is justified as long as groups avoid such problems as group polarization and groupthink.

Learning Objectives

• Review the evidence that suggests humans have a fundamental need to belong to groups.
• Compare the sociometer model of self-esteem to a more traditional view of self-esteem.
• Use theories of social facilitation to predict when a group will perform tasks slowly or quickly (e.g., students eating a meal as a group, workers on an assembly line, or a study group).
• Summarize the methods used by Latané, Williams, and Harkins to identify the relative impact of social loafing and coordination problems on group performance.
• Describe how groups change over time.
• Apply the theory of groupthink to a well-known decision-making group, such as the group of advisors responsible for planning the Bay of Pigs operation.
• List and discuss the factors that facilitate and impede group performance and decision making.
• Develop a list of recommendations that, if followed, would minimize the possibility of groupthink developing in a group.

The Psychology of Groups

Psychologists study groups because nearly all human activities—working, learning, worshiping, relaxing, playing, and even sleeping—occur in groups. The lone individual who is cut off from all groups is a rarity. Most of us live out our lives in groups, and these groups have a profound impact on our thoughts, feelings, and actions. Many psychologists focus their attention on single individuals, but social psychologists expand their analysis to include groups, organizations, communities, and even cultures.

This module examines the psychology of groups and group membership. It begins with a basic question: What is the psychological significance of groups? People are, undeniably, more often in groups rather than alone. What accounts for this marked gregariousness and what does it say about our psychological makeup? The module then reviews some of the key findings from studies of groups. Researchers have asked many questions about people and groups: Do people work as hard as they can when they are in groups? Are groups more cautious than individuals? Do groups make wiser decisions than single individuals? In many cases the answers are not what common sense and folk wisdom might suggest.

The Psychological Significance of Groups

Many people loudly proclaim their autonomy and independence. Like Ralph Waldo Emerson, they avow, “I must be myself. I will not hide my tastes or aversions . . . . I will seek my own” ( 1903/2004 , p. 127). Even though people are capable of living separate and apart from others, they join with others because groups meet their psychological and social needs.

The Need to Belong

Across individuals, societies, and even eras, humans consistently seek inclusion over exclusion, membership over isolation, and acceptance over rejection. As Roy Baumeister and Mark Leary conclude, humans have a need to belong : “a pervasive drive to form and maintain at least a minimum quantity of lasting, positive, and impactful interpersonal relationships” ( 1995 , p. 497). And most of us satisfy this need by joining groups. When surveyed, 87.3% of Americans reported that they lived with other people, including family members, partners, and roommates ( Davis & Smith, 2007 ). The majority, ranging from 50% to 80%, reported regularly doing things in groups, such as attending a sports event together, visiting one another for the evening, sharing a meal together, or going out as a group to see a movie ( Putnam, 2000 ).

People respond negatively when their need to belong is unfulfilled. For example, college students often feel homesick and lonely when they first start college, but not if they belong to a cohesive, socially satisfying group ( Buote et al., 2007 ). People who are accepted members of a group tend to feel happier and more satisfied. But should they be rejected by a group, they feel unhappy, helpless, and depressed. Studies of ostracism —the deliberate exclusion from groups—indicate this experience is highly stressful and can lead to depression, confused thinking, and even aggression ( Williams, 2007 ). When researchers used a functional magnetic resonance imaging scanner to track neural responses to exclusion, they found that people who were left out of a group activity displayed heightened cortical activity in two specific areas of the brain—the dorsal anterior cingulate cortex and the anterior insula. These areas of the brain are associated with the experience of physical pain sensations ( Eisenberger, Lieberman, & Williams, 2003 ). It hurts, quite literally, to be left out of a group.

Affiliation in Groups

Groups not only satisfy the need to belong, they also provide members with information, assistance, and social support. Leon Festinger’s theory of social comparison ( 1950 , 1954 ) suggested that in many cases people join with others to evaluate the accuracy of their personal beliefs and attitudes. Stanley Schachter ( 1959 ) explored this process by putting individuals in ambiguous, stressful situations and asking them if they wished to wait alone or with others. He found that people affiliate in such situations—they seek the company of others.

Although any kind of companionship is appreciated, we prefer those who provide us with reassurance and support as well as accurate information. In some cases, we also prefer to join with others who are even worse off than we are. Imagine, for example, how you would respond when the teacher hands back the test and yours is marked 85%. Do you want to affiliate with a friend who got a 95% or a friend who got a 78%? To maintain a sense of self-worth, people seek out and compare themselves to the less fortunate. This process is known as downward social comparison .

Identity and Membership

Groups are not only founts of information during times of ambiguity, they also help us answer the existentially significant question, “Who am I?” Common sense tells us that our sense of self is our private definition of who we are, a kind of archival record of our experiences, qualities, and capabilities. Yet, the self also includes all those qualities that spring from memberships in groups. People are defined not only by their traits, preferences, interests, likes, and dislikes, but also by their friendships, social roles, family connections, and group memberships. The self is not just a “me,” but also a “we.”

Even demographic qualities such as sex or age can influence us if we categorize ourselves based on these qualities. Social identity theory , for example, assumes that we don’t just classify other people into such social categories as man, woman, Anglo, elderly, or college student, but we also categorize ourselves. Moreover, if we strongly identify with these categories, then we will ascribe the characteristics of the typical member of these groups to ourselves, and so stereotype ourselves. If, for example, we believe that college students are intellectual, then we will assume we, too, are intellectual if we identify with that group ( Hogg, 2001 ).

Groups also provide a variety of means for maintaining and enhancing a sense of self-worth, as our assessment of the quality of groups we belong to influences our collective self-esteem ( Crocker & Luhtanen, 1990 ). If our self-esteem is shaken by a personal setback, we can focus on our group’s success and prestige. In addition, by comparing our group to other groups, we frequently discover that we are members of the better group, and so can take pride in our superiority. By denigrating other groups, we elevate both our personal and our collective self-esteem ( Crocker & Major, 1989 ).

Mark Leary’s sociometer model goes so far as to suggest that “self-esteem is part of a sociometer that monitors peoples’ relational value in other people’s eyes” ( 2007 , p. 328). He maintains self-esteem is not just an index of one’s sense of personal value, but also an indicator of acceptance into groups. Like a gauge that indicates how much fuel is left in the tank, a dip in self-esteem indicates exclusion from our group is likely. Disquieting feelings of self-worth, then, prompt us to search for and correct characteristics and qualities that put us at risk of social exclusion. Self-esteem is not just high self-regard, but the self-approbation that we feel when included in groups ( Leary & Baumeister, 2000 ).

Evolutionary Advantages of Group Living

Groups may be humans’ most useful invention, for they provide us with the means to reach goals that would elude us if we remained alone. Individuals in groups can secure advantages and avoid disadvantages that would plague the lone individuals. In his theory of social integration, Moreland concludes that groups tend to form whenever “people become dependent on one another for the satisfaction of their needs” ( 1987 , p. 104). The advantages of group life may be so great that humans are biologically prepared to seek membership and avoid isolation. From an evolutionary psychology perspective, because groups have increased humans’ overall fitness for countless generations, individuals who carried genes that promoted solitude-seeking were less likely to survive and procreate compared to those with genes that prompted them to join groups ( Darwin, 1859/1963 ). This process of natural selection culminated in the creation of a modern human who seeks out membership in groups instinctively, for most of us are descendants of “joiners” rather than “loners.”

Motivation and Performance

Groups usually exist for a reason. In groups, we solve problems, create products, create standards, communicate knowledge, have fun, perform arts, create institutions, and even ensure our safety from attacks by other groups. But do groups always outperform individuals?

Social Facilitation in Groups

Do people perform more effectively when alone or when part of a group? Norman Triplett ( 1898 ) examined this issue in one of the first empirical studies in psychology. While watching bicycle races, Triplett noticed that cyclists were faster when they competed against other racers than when they raced alone against the clock. To determine if the presence of others leads to the psychological stimulation that enhances performance, he arranged for 40 children to play a game that involved turning a small reel as quickly as possible (see Figure 1). When he measured how quickly they turned the reel, he confirmed that children performed slightly better when they played the game in pairs compared to when they played alone (see Stroebe, 2012 ; Strube, 2005 ).

Triplett succeeded in sparking interest in a phenomenon now known as social facilitation : the enhancement of an individual’s performance when that person works in the presence of other people. However, it remained for Robert Zajonc ( 1965 ) to specify when social facilitation does and does not occur. After reviewing prior research, Zajonc noted that the facilitating effects of an audience usually only occur when the task requires the person to perform dominant responses, i.e., ones that are well-learned or based on instinctive behaviors. If the task requires nondominant responses, i.e., novel, complicated, or untried behaviors that the organism has never performed before or has performed only infrequently, then the presence of others inhibits performance. Hence, students write poorer quality essays on complex philosophical questions when they labor in a group rather than alone ( Allport, 1924 ), but they make fewer mistakes in solving simple, low-level multiplication problems with an audience or a coactor than when they work in isolation ( Dashiell, 1930 ).

Social facilitation, then, depends on the task: other people facilitate performance when the task is so simple that it requires only dominant responses, but others interfere when the task requires nondominant responses. However, a number of psychological processes combine to influence when social facilitation, not social interference, occurs. Studies of the challenge-threat response and brain imaging, for example, confirm that we respond physiologically and neurologically to the presence of others ( Blascovich, Mendes, Hunter, & Salomon, 1999 ). Other people also can trigger evaluation apprehension, particularly when we feel that our individual performance will be known to others, and those others might judge it negatively ( Bond, Atoum, & VanLeeuwen, 1996 ). The presence of other people can also cause perturbations in our capacity to concentrate on and process information ( Harkins, 2006 ). Distractions due to the presence of other people have been shown to improve performance on certain tasks, such as the Stroop task , but undermine performance on more cognitively demanding tasks ( Huguet, Galvaing, Monteil, & Dumas, 1999 ).

Social Loafing

Groups usually outperform individuals. A single student, working alone on a paper, will get less done in an hour than will four students working on a group project. One person playing a tug-of-war game against a group will lose. A crew of movers can pack up and transport your household belongings faster than you can by yourself. As the saying goes, “Many hands make light the work” ( Littlepage, 1991 ; Steiner, 1972 ).

Groups, though, tend to be underachievers. Studies of social facilitation confirmed the positive motivational benefits of working with other people on well-practiced tasks in which each member’s contribution to the collective enterprise can be identified and evaluated. But what happens when tasks require a truly collective effort? First, when people work together they must coordinate their individual activities and contributions to reach the maximum level of efficiency—but they rarely do ( Diehl & Stroebe, 1987 ). Three people in a tug-of-war competition, for example, invariably pull and pause at slightly different times, so their efforts are uncoordinated. The result is coordination loss : the three-person group is stronger than a single person, but not three times as strong. Second, people just don’t exert as much effort when working on a collective endeavor, nor do they expend as much cognitive effort trying to solve problems, as they do when working alone. They display social loafing ( Latané, 1981 ).

Bibb Latané, Kip Williams, and Stephen Harkins ( 1979 ) examined both coordination losses and social loafing by arranging for students to cheer or clap either alone or in groups of varying sizes. The students cheered alone or in 2- or 6-person groups, or they were lead to believe they were in 2- or 6-person groups (those in the “pseudo-groups” wore blindfolds and headsets that played masking sound). As Figure 2 indicates, groups generated more noise than solitary subjects, but the productivity dropped as the groups became larger in size. In dyads, each subject worked at only 66% of capacity, and in 6-person groups at 36%. Productivity also dropped when subjects merely believed they were in groups. If subjects thought that one other person was shouting with them, they shouted 82% as intensely, and if they thought five other people were shouting, they reached only 74% of their capacity. These loses in productivity were not due to coordination problems; this decline in production could be attributed only to a reduction in effort—to social loafing (Latané et al., 1979, Experiment 2).

Social loafing is no rare phenomenon. When sales personnel work in groups with shared goals, they tend to “take it easy” if another salesperson is nearby who can do their work ( George, 1992 ). People who are trying to generate new, creative ideas in group brainstorming sessions usually put in less effort and are thus less productive than people who are generating new ideas individually ( Paulus & Brown, 2007 ). Students assigned group projects often complain of inequity in the quality and quantity of each member’s contributions: Some people just don’t work as much as they should to help the group reach its learning goals ( Neu, 2012 ). People carrying out all sorts of physical and mental tasks expend less effort when working in groups, and the larger the group, the more they loaf ( Karau & Williams, 1993 ).

Groups can, however, overcome this impediment to performance through teamwork . A group may include many talented individuals, but they must learn how to pool their individual abilities and energies to maximize the team’s performance. Team goals must be set, work patterns structured, and a sense of group identity developed. Individual members must learn how to coordinate their actions, and any strains and stresses in interpersonal relations need to be identified and resolved ( Salas, Rosen, Burke, & Goodwin, 2009 ).

Researchers have identified two key ingredients to effective teamwork: a shared mental representation of the task and group unity. Teams improve their performance over time as they develop a shared understanding of the team and the tasks they are attempting. Some semblance of this shared mental model is present nearly from its inception, but as the team practices, differences among the members in terms of their understanding of their situation and their team diminish as a consensus becomes implicitly accepted ( Tindale, Stawiski, & Jacobs, 2008 ).

Effective teams are also, in most cases, cohesive groups ( Dion, 2000 ). Group cohesion is the integrity, solidarity, social integration, or unity of a group. In most cases, members of cohesive groups like each other and the group and they also are united in their pursuit of collective, group-level goals. Members tend to enjoy their groups more when they are cohesive, and cohesive groups usually outperform ones that lack cohesion.

This cohesion-performance relationship, however, is a complex one. Meta-analytic studies suggest that cohesion improves teamwork among members, but that performance quality influences cohesion more than cohesion influences performance ( Mullen & Copper, 1994 ; Mullen, Driskell, & Salas, 1998 ; see Figure 3). Cohesive groups also can be spectacularly unproductive if the group’s norms stress low productivity rather than high productivity ( Seashore, 1954 ).

Group Development

In most cases groups do not become smooth-functioning teams overnight. As Bruce Tuckman’s ( 1965 ) theory of group development suggests, groups usually pass through several stages of development as they change from a newly formed group into an effective team. As noted in Focus Topic 1, in the forming phase, the members become oriented toward one another. In the storming phase, the group members find themselves in conflict, and some solution is sought to improve the group environment. In the norming, phase standards for behavior and roles develop that regulate behavior. In the performing, phase the group has reached a point where it can work as a unit to achieve desired goals, and the adjourning phase ends the sequence of development; the group disbands. Throughout these stages groups tend to oscillate between the task-oriented issues and the relationship issues, with members sometimes working hard but at other times strengthening their interpersonal bonds ( Tuckman & Jensen, 1977 ).

Focus Topic 1: Group Development Stages and Characteristics

Stage 1 – “Forming”. Members expose information about themselves in polite but tentative interactions. They explore the purposes of the group and gather information about each other’s interests, skills, and personal tendencies.

Stage 2 – “Storming”. Disagreements about procedures and purposes surface, so criticism and conflict increase. Much of the conflict stems from challenges between members who are seeking to increase their status and control in the group.

Stage 3 – “Norming”. Once the group agrees on its goals, procedures, and leadership, norms, roles, and social relationships develop that increase the group’s stability and cohesiveness.

Stage 4 – “Performing”. The group focuses its energies and attention on its goals, displaying higher rates of task-orientation, decision-making, and problem-solving.

Stage 5 – “Adjourning”. The group prepares to disband by completing its tasks, reduces levels of dependency among members, and dealing with any unresolved issues.

Sources based on Tuckman (1965) and Tuckman & Jensen (1977)

We also experience change as we pass through a group, for we don’t become full-fledged members of a group in an instant. Instead, we gradually become a part of the group and remain in the group until we leave it. Richard Moreland and John Levine’s ( 1982 ) model of group socialization describes this process, beginning with initial entry into the group and ending when the member exits it. For example, when you are thinking of joining a new group—a social club, a professional society, a fraternity or sorority, or a sports team—you investigate what the group has to offer, but the group also investigates you. During this investigation stage you are still an outsider: interested in joining the group, but not yet committed to it in any way. But once the group accepts you and you accept the group, socialization begins: you learn the group’s norms and take on different responsibilities depending on your role. On a sports team, for example, you may initially hope to be a star who starts every game or plays a particular position, but the team may need something else from you. In time, though, the group will accept you as a full-fledged member and both sides in the process—you and the group itself—increase their commitment to one another. When that commitment wanes, however, your membership may come to an end as well.

Making Decisions in Groups

Groups are particularly useful when it comes to making a decision, for groups can draw on more resources than can a lone individual. A single individual may know a great deal about a problem and possible solutions, but his or her information is far surpassed by the combined knowledge of a group. Groups not only generate more ideas and possible solutions by discussing the problem, but they can also more objectively evaluate the options that they generate during discussion. Before accepting a solution, a group may require that a certain number of people favor it, or that it meets some other standard of acceptability. People generally feel that a group’s decision will be superior to an individual’s decision.

Groups, however, do not always make good decisions. Juries sometimes render verdicts that run counter to the evidence presented. Community groups take radical stances on issues before thinking through all the ramifications. Military strategists concoct plans that seem, in retrospect, ill-conceived and short-sighted. Why do groups sometimes make poor decisions?

Group Polarization

Let’s say you are part of a group assigned to make a presentation. One of the group members suggests showing a short video that, although amusing, includes some provocative images. Even though initially you think the clip is inappropriate, you begin to change your mind as the group discusses the idea. The group decides, eventually, to throw caution to the wind and show the clip—and your instructor is horrified by your choice.

This hypothetical example is consistent with studies of groups making decisions that involve risk. Common sense notions suggest that groups exert a moderating, subduing effect on their members. However, when researchers looked at groups closely, they discovered many groups shift toward more extreme decisions rather than less extreme decisions after group interaction. Discussion, it turns out, doesn’t moderate people’s judgments after all. Instead, it leads to group polarization : judgments made after group discussion will be more extreme in the same direction as the average of individual judgments made prior to discussion ( Myers & Lamm, 1976 ). If a majority of members feel that taking risks is more acceptable than exercising caution, then the group will become riskier after a discussion. For example, in France, where people generally like their government but dislike Americans, group discussion improved their attitude toward their government but exacerbated their negative opinions of Americans ( Moscovici & Zavalloni, 1969 ). Similarly, prejudiced people who discussed racial issues with other prejudiced individuals became even more negative, but those who were relatively unprejudiced exhibited even more acceptance of diversity when in groups ( Myers & Bishop, 1970 ).

Common Knowledge Effect

One of the advantages of making decisions in groups is the group’s greater access to information. When seeking a solution to a problem, group members can put their ideas on the table and share their knowledge and judgments with each other through discussions. But all too often groups spend much of their discussion time examining common knowledge—information that two or more group members know in common—rather than unshared information. This common knowledge effect will result in a bad outcome if something known by only one or two group members is very important.

Researchers have studied this bias using the hidden profile task . On such tasks, information known to many of the group members suggests that one alternative, say Option A, is best. However, Option B is definitely the better choice, but all the facts that support Option B are only known to individual groups members—they are not common knowledge in the group. As a result, the group will likely spend most of its time reviewing the factors that favor Option A, and never discover any of its drawbacks. In consequence, groups often perform poorly when working on problems with nonobvious solutions that can only be identified by extensive information sharing ( Stasser & Titus, 1987 ).

Groups sometimes make spectacularly bad decisions. In 1961, a special advisory committee to President John F. Kennedy planned and implemented a covert invasion of Cuba at the Bay of Pigs that ended in total disaster. In 1986, NASA carefully, and incorrectly, decided to launch the Challenger space shuttle in temperatures that were too cold.

Irving Janis ( 1982 ), intrigued by these kinds of blundering groups, carried out a number of case studies of such groups: the military experts that planned the defense of Pearl Harbor; Kennedy’s Bay of Pigs planning group; the presidential team that escalated the war in Vietnam. Each group, he concluded, fell prey to a distorted style of thinking that rendered the group members incapable of making a rational decision. Janis labeled this syndrome groupthink : “a mode of thinking that people engage in when they are deeply involved in a cohesive in-group, when the members’ strivings for unanimity override their motivation to realistically appraise alternative courses of action” (p. 9).

Janis identified both the telltale symptoms that signal the group is experiencing groupthink and the interpersonal factors that combine to cause groupthink. To Janis, groupthink is a disease that infects healthy groups, rendering them inefficient and unproductive. And like the physician who searches for symptoms that distinguish one disease from another, Janis identified a number of symptoms that should serve to warn members that they may be falling prey to groupthink. These symptoms include overestimating the group’s skills and wisdom, biased perceptions and evaluations of other groups and people who are outside of the group, strong conformity pressures within the group, and poor decision-making methods.

Janis also singled out four group-level factors that combine to cause groupthink: cohesion, isolation, biased leadership, and decisional stress.

• Cohesion : Groupthink only occurs in cohesive groups. Such groups have many advantages over groups that lack unity. People enjoy their membership much more in cohesive groups, they are less likely to abandon the group, and they work harder in pursuit of the group’s goals. But extreme cohesiveness can be dangerous. When cohesiveness intensifies, members become more likely to accept the goals, decisions, and norms of the group without reservation. Conformity pressures also rise as members become reluctant to say or do anything that goes against the grain of the group, and the number of internal disagreements—necessary for good decision making—decreases.
• Isolation. Groupthink groups too often work behind closed doors, keeping out of the limelight. They isolate themselves from outsiders and refuse to modify their beliefs to bring them into line with society’s beliefs. They avoid leaks by maintaining strict confidentiality and working only with people who are members of their group.
• Biased leadership . A biased leader who exerts too much authority over group members can increase conformity pressures and railroad decisions. In groupthink groups, the leader determines the agenda for each meeting, sets limits on discussion, and can even decide who will be heard.
• Decisional stress. Groupthink becomes more likely when the group is stressed, particularly by time pressures. When groups are stressed they minimize their discomfort by quickly choosing a plan of action with little argument or dissension. Then, through collective discussion, the group members can rationalize their choice by exaggerating the positive consequences, minimizing the possibility of negative outcomes, concentrating on minor details, and overlooking larger issues.

You and Your Groups

Most of us belong to at least one group that must make decisions from time to time: a community group that needs to choose a fund-raising project; a union or employee group that must ratify a new contract; a family that must discuss your college plans; or the staff of a high school discussing ways to deal with the potential for violence during football games. Could these kinds of groups experience groupthink? Yes they could, if the symptoms of groupthink discussed above are present, combined with other contributing causal factors, such as cohesiveness, isolation, biased leadership, and stress. To avoid polarization, the common knowledge effect, and groupthink, groups should strive to emphasize open inquiry of all sides of the issue while admitting the possibility of failure. The leaders of the group can also do much to limit groupthink by requiring full discussion of pros and cons, appointing devil’s advocates, and breaking the group up into small discussion groups.

If these precautions are taken, your group has a much greater chance of making an informed, rational decision. Furthermore, although your group should review its goals, teamwork, and decision-making strategies, the human side of groups—the strong friendships and bonds that make group activity so enjoyable—shouldn’t be overlooked. Groups have instrumental, practical value, but also emotional, psychological value. In groups we find others who appreciate and value us. In groups we gain the support we need in difficult times, but also have the opportunity to influence others. In groups we find evidence of our self-worth, and secure ourselves from the threat of loneliness and despair. For most of us, groups are the secret source of well-being.

Text Attribution

Media attributions.

• AFF Level 1 – Skydive Langar
• Another Three
• Figure 13.1: The “competition machine”
• Figure 13.2
• Dragon Boat Races
• Figure 13.3
• USMC Sitting Volleyball Team wins gold

Excluding one or more individuals from a group by reducing or eliminating contact with the person, usually by ignoring, shunning, or explicitly banishing them.

The process by which people understand their own ability or condition by mentally comparing themselves to others.

Social identity theory notes that people categorize each other into groups, favoring their own group.

Feelings of self-worth that are based on evaluation of relationships with others and membership in social groups.

A conceptual analysis of self-evaluation processes that theorizes self-esteem functions to psychologically monitor of one’s degree of inclusion and exclusion in social groups.

When performance on simple or well-rehearsed tasks is enhanced when we are in the presence of others.

The reduction of individual effort exerted when people work in groups compared with when they work alone.

The process by which members of the team combine their knowledge, skills, abilities, and other resources through a coordinated series of actions to produce an outcome.

Knowledge, expectations, conceptualizations, and other cognitive representations that members of a group have in common pertaining to the group and its members, tasks, procedures, and resources.

The solidarity or unity of a group resulting from the development of strong and mutual interpersonal bonds among members and group-level forces that unify the group, such as shared commitment to group goals.

The tendency for members of a deliberating group to move to a more extreme position, with the direction of the shift determined by the majority or average of the members’ predeliberation preferences.

The tendency for groups to spend more time discussing information that all members know (shared information) and less time examining information that only a few members know (unshared).

A set of negative group-level processes, including illusions of invulnerability, self-censorship, and pressures to conform, that occur when highly cohesive groups seek concurrence when making a decision.

An Introduction to Social Psychology Copyright © 2022 by Thomas Edison State University is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Social Behaviour in Zoo Bachelor Groups: A Case Study of Related South American Fur Seals

Christa emmett.

1 Department of Field Conservation & Science, Bristol Zoological Society, Bristol BS8 3HA, UK; [email protected] or

Mathilda Digby

2 Department of Applied Sciences, University of the West of England, Bristol BS16 1QY, UK or [email protected] (M.D.); moc.liamg@69epopammej (J.P.)

Ellen Williams

Associated data.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Simple Summary

Appropriate management of social groups is one of the greatest challenges that face zoos and aquaria worldwide. All-male social groups provide an opportunity for facilities to house surplus males in groups which optimise their welfare whilst they are being retained for future use in breeding programmes. Here, we investigated social behaviour in a relatively poorly studied species, the South American fur seal (SAFS). Four individuals housed in a related group were studied over a 6-month period. The results showed that their social relationships changed over time, although the individuals always engaged in more positive than negative interactions. We recommend establishing baseline social behaviour profiles of individuals to enable long-term monitoring of SAFS social groups, as has been recommended in other species. This will enable enhanced understanding of South American fur seals and will contribute to the development of evidence-based social management guidelines for this species.

Appropriate management of social groups is one of the greatest challenges that face zoos and aquaria worldwide. To facilitate breeding programmes, particularly in polygynous species, there is a need to house surplus males in bachelor groups, yet for pinnipeds, the social impact of this management strategy is unknown. The aim of this research was to enhance understanding of sociality in South American fur seals (SAFSs), with a particular focus on social dynamics in a related bachelor group, and consider implications for evidence-based management of this species in zoos. The subjects were four related male seals housed at Bristol Zoo Gardens. Social interaction and nearest neighbour data were collected between February and July 2019. Individuals engaged in both positive and negative social interactions. Positive interactions were more frequent than negative interactions, and no excessive negative interactions were observed. Temporal dynamics were observed in social relationships, and negative interactions did not increase with the onset of the breeding season. Reciprocity in dyadic relationships was variable across the study months, and nearest neighbours were not necessarily reflective of social partners. This research highlights the importance of longitudinal monitoring of social relationships and establishment of baseline social behaviour profiles to support evidence-based species management. We advocate that this research is extended, to further develop our understanding of SAFS social needs within zoo environments, to understand the differences between single-sex and mixed-sex groups and to identify the degree to which the extensive research conducted in other polygynous species (e.g., gorillas) is applicable in the social management of South American fur seals moving forwards.

1. Introduction

Pinnipeds exhibit intricate and complex social systems [ 1 ]. Generally, non-breeding bulls and sub-adult males form bachelor groups in the vicinity of breeding colonies [ 2 , 3 ]. The South American fur seal ( Arctocephalus australis ) is no exception. It is an eared seal found along the Atlantic and Pacific coasts of South America [ 4 ]. They reside in groups of 14 to 12,955 individuals (average 1883), with variation seen throughout the year [ 5 ]. They are poorly studied in comparison to other pinniped species [ 6 ], and there is a paucity of literature on their social dynamics, however, it is known they have a predominantly polygynous social structure and breed in the spring/summer (October to December) [ 7 ]. Males are either territorial or satellite. Satellite males remain near breeding groups but interactions between individuals are lower than between dominant males [ 8 ]. Within zoos in the northern hemisphere, the otariid breeding season is usually spring/summer (May to August), but there may be an additional breeding season in December [ 9 ]. Sexual maturity is generally between the ages of four and seven, although otariids have been recorded as able to sire/carry offspring from the age of two years within zoos [ 9 ].

Traditionally, there were difficulties in catering for polygynous species in zoos [ 10 ]. Non-breeding males usually could not be maintained within breeding groups due to competition for mates [ 11 ]. Bachelor groups naturally occur in the wild in a number of polygynous species [ 3 ]. However, these groups may be relatively unstable, with individuals migrating once they reach sexual maturity [ 12 ]. Formation of bachelor groups in zoos provides an opportunity to manage these surplus males (both adults and sub-adults) [ 13 ]. Although a relatively recent development in zoos, bachelor groups have been identified as a successful solution to housing excess males [ 11 , 14 ], despite concerns of potential for aggression and wounding within groups [ 12 ].

Appropriate social groups are a form of social enrichment and support animals to develop species-specific behaviour [ 15 ]. Social interactions are recognised as a good source of enrichment for zoo-housed pinnipeds [ 9 ]. However, appropriate management of social groups is one of the greatest challenges that face zoos and aquaria today [ 16 ]. Group social stability is considered crucial to successful social management, however, sometimes zoo management practices (e.g., introduction of new social members, separation for individual training regimes) or other natural changes to social groups (e.g., the death of a social member) can disturb social groups [ 12 ]. A comprehensive understanding of species-specific social dynamics can help to mitigate management-related social disturbance [ 17 ]. Understanding natural fluctuations in social behaviour and temporal dynamics in relationships has been identified as a useful tool in evidence-based social management approaches [ 18 ]. Effective population management of social, in particular polygynous, species requires successful formation and maintenance of compatible bachelor groups. A plethora of work has been undertaken in primates to establish baseline knowledge in relation to the management of bachelor groups and to consider their management differences in relation to mixed-sex groups [ 12 ]. Expanding this knowledge into other social species is important for improved animal welfare across taxa.

Approximately 71% of marine mammals housed in zoos are pinnipeds [ 19 ], yet there is a paucity of research on pinniped social groupings, especially in terms of bachelor groups. The European Association of Zoos and Aquaria (EAZA) guidelines on the management of captive pinnipeds suggest that groups should comprise a variety of age groups, as an unbalanced age structure can create disruptive hierarchies [ 9 ]. However, this recommendation may not be practical or relevant for single sex, non-breeding groups. Within ZIMS-registered collections, SAFSs ( n = 58) are held at 20 facilities worldwide. Bristol Zoo is one of only three holders of bachelor groups of SAFSs. There are 4 female-only groups and 13 mixed-sex groups. Groups range in size from 1 to 7 individuals (mean of 2.9) [ 20 ]. The expression of natural behaviours (e.g., play and socialising) may improve in multi-male groups, if enough room is provided for choice of when to interact with or avoid conspecifics [ 9 ]. The development of evidence-based social group recommendations requires a baseline knowledge of animal social needs. Such recommendations should depend on whether individuals are housed in a single- or mixed-sex exhibit, and will likely be affected by the individuals in the group.

Pinniped social group composition has been highlighted as a required area for research, in order to advance species knowledge and improve animal management [ 9 ]. To the authors’ knowledge, at the time of writing there was no published research on social behaviour in bachelor groups of SAFSs in zoos. Capturing data on temporal dynamics is important in understanding animal social networks [ 21 ]. The aim of this research was to enhance understanding of sociality in SAFSs, with a particular focus on social dynamics in a related bachelor group, and to consider implications for evidence-based management of this species in zoos. Furthermore, it was to understand whether bachelor groups of SAFSs show temporal dynamics in their relationships which align with the breeding season. This knowledge will enable enhanced understanding of SAFS social relationships in zoos and proactive management approaches which ensure positive welfare.

2. Materials and Methods

2.1. study subjects and enclosure.

The study subjects were four related male SAFSs ( Table 1 ), all of whom were captive born. Individuals were housed in the Seal and Penguin Coasts exhibit at Bristol Zoo Gardens [ 22 ], in an enclosure (160 m 2 ) that consisted of two pools (total surface area approximately 80 m 2 , volume approximately 450 m 3 and 2.4–3 m deep), external rocks (approximately 80 m 2 ) and an indoor shelter. There were no female SAFSs housed at the zoo, nor were there any other pinniped species. All observations were made from a viewing deck above the surface of the water ( Figure 1 ). Random feeds occurred twice per day, as well as a daily public talk and feed occurring at 3:30 p.m.

A schematic drawing of the South American fur seals enclosure at Bristol Zoo Gardens [ 23 ]. Blue star indicates where observations were made from.

South American fur seals at Bristol Zoo Gardens.

2.2. Data Collection

Preliminary observations to identify individual seals and refine the ethogram and data collection methods were undertaken ad libitum during January 2019. Seals were identified using visually discernible differences. Data were collected from February to July 2019, on two randomly selected days per week (Monday to Friday) between 09:00 and 16:00. Where possible, two observation sessions (1 × AM and 1 × PM) were undertaken per day. Each observation session lasted 1 h. A total of 98 observation periods were undertaken over 50 days.

Due to the small number of seals in the group ( n = 4), data were collected on the entire group simultaneously. During each 1 h observation period, social interactions were captured using a behaviour sampling method with continuous (all-occurrence) recording [ 24 ]. When social interactions were recorded, the following data was collected: individuals involved, direction of interaction (e.g., unidirectional or bidirectional) and nature of the interaction (positive or negative) ( Table 2 ). Scan sampling (5 min inter-scan interval) with an instantaneous recording method was used to gather data on proximity to other seals (identity of closest seal or seals) [ 24 ]. If seals were not within two body lengths of another seal at the time of observation they were classed as being ‘alone’.

Ethogram detailing positive and negative behaviours recorded during the study. Ethogram was based on [ 25 ], and modified during preliminary observations prior to commencement of the study.

2.3. Data Analysis

Data analysis was split into two areas: (i) frequency of social interactions given by individual seals and within-seal dyads; (ii) group social matrices (group size n = 4 seals). Data were split into months of data collection (February–July), to investigate the stability of group dynamics over time.

2.3.1. Frequency of Social Interactions

To understand the role of individuals within the social network, centrality measures were calculated using NetDraw Version 2.160 [ 26 ]. Degree centrality has been identified as a useful approximation of individual centrality, which shows high correlations with other centrality measures [ 27 ]. Due to the small network size, this was considered an appropriate measure of centrality for this simple network. Betweenness centrality has been used to identify how important individuals are in terms of network cohesion [ 28 ]. This was additionally calculated to determine whether any individuals were considered ‘key’ in the social group, based on social interactions.

Statistical analysis was undertaken in R Studio (Version 4.0.3) [ 29 ], using packages ‘lme4′ [ 30 ], ‘emmeans’ [ 31 ] and ‘MASS’ [ 32 ]. Graphs were produced using ‘ggplot2′ [ 33 ]. All model results are reported as model estimate (β1) ± SE. Significance values were set at p < 0.05 for all analyses.

A wilcoxon test for paired samples was used to assess the relationship between the frequency of positive and negative social interactions at each observation period. Negative binomial general linear models (GLMs) were used to investigate differences in social interactions given by individual seals. Frequency of positive and negative social interactions was fitted as response variables and seal was fitted as a fixed effect. General linear mixed models (GLMMs), with Tukey-corrected post-hoc tests where appropriate, were used to investigate whether the frequency of social interactions given by individual seals and within seal dyads changed over time (study months February to July). Frequency of positive and negative interactions given by individual seals and within seal dyads were fitted as response variables in two separate models. Observation month was fitted as a fixed effect. To control for repeated observations, seal was included as a random effect in each model.

2.3.2. Assessment of Group-Level Interactions Using Social Matrices

Changes in seal relationships within the social group (using interaction frequencies and nearest neighbour preferences) over time and reciprocity in dyads were assessed using Mantel tests using package ‘vegan’ [ 34 ]. A total of 999 permutations were used per test, with the Pearson product moment correlation coefficient as the test statistic. Significance levels were set at 0.05.

Social interaction matrices were created using frequency of interaction data for positive and negative interactions, and nearest neighbour data. Matrices of total frequency of interactions and proximity to others were created for each month. All months were compared against all months (February to July) to establish whether social group structure differed throughout the study period. Each month was then compared with the subsequent month (February–March; March–April; April–May; May–June; June–July) to examine changes in group structure over a longitudinal period. Correlation between the matrices indicated consistency in social networks.

Mantel tests were then used to assess whether nearest neighbour data was reflective of social interaction partners each month. No correlation between the two matrices indicated that the two networks (social interaction and nearest neighbour) were not representative of one another.

Tests of reciprocity were undertaken to determine whether dyadic social interactions were reciprocal (i.e., to determine whether the rate of interaction S1 directed towards S2 was correlated with the rate of interaction that S2 directed to S1). No correlation between the matrix and its transpose indicated unidirectional interactions.

3.1. Frequency of Social Interactions

3.1.1. interactions given by individual seals.

All seals engaged in both positive and negative social interactions with all other seals. Seals had equal centrality within both the positive and negative social network (degree centrality score = 3, betweenness centrality score = 0). Within each observation period, seals performed significantly more positive (mean observations ± SD per 1 h observation period; 1.06 ± 1.6) than negative (0.49 ± 1.4) social interactions (W = 18,496, p < 0.001). Across all study months, there were significant differences between seals in frequency of positive ( Figure 2 ) and negative ( Figure 3 ) social interactions.

Frequency of positive social interactions given by each seal ( n = 4) per observation period.

Frequency of negative social interactions given by each seal ( n = 4) per observation period.

Seal 3 engaged in more positive social interactions than seal 1 (0.67 ± 0.22, Z = 3.121, p < 0.01) and seal 2 (0.55 ± 0.21, Z = 2.605, p < 0.05). Seal 4 also engaged in more positive social interactions than seal 1 (0.68 ± 0.22, Z = 3.156, p < 0.01) and seal 2 (0.56 ± 0.21, Z = 2.641, p < 0.05). There were no significant differences between seal 1 and seal 2 ( p > 0.05), or seal 3 and seal 4 ( p > 0.05). Seal 3 and seal 4 engaged in more negative social interactions per observation period than seal 2 (respectively, 1.32 ± 0.36, Z = 3.651, p < 0.01; 0.99 ± 0.37, Z = 2.691, p < 0.05).

Frequency of interactions given by seals differed across the months. Frequency of positive social interactions per sampling period was significantly lower in June than in February (−1.06 ± 0.27, Z = −3.993, p < 0.001), March (−0.84 ± 0.27, Z = −3.082, p < 0.05) and April (−0.94 ± 0.26, Z = −3.609, p < 0.01). Positive social interactions were also lower in July than February (−0.86 ± 0.28, Z = −3.106, p < 0.05), and there was a trend towards them being lower in July than April (−0.73 ± 0.27, Z = −2.713, p = 0.07). Negative interactions were lower in March (−0.88 ± 0.24, Z = −3.715, p < 0.01) and April (−0.82 ± 0.22, Z = −3.650, p < 0.05) than February. There was also a trend towards lower negative interactions in June than February (−0.65 ± 0.24, Z = −2.727, p = 0.06).

3.1.2. Interactions within Seal Dyads

Frequency of social interactions also differed significantly when looked at in terms of dyadic interactions. Dyad 6 (S3 and S4) engaged in more positive interactions per observation period than dyad 1 (S1 and S2; 1.49 ± 0.29, Z = 5.091, p < 0.001), dyad 2 (S1 and S3; 0.91 ± 0.27, Z = 3.354, p < 0.05), dyad 3 (S1 and S4; 1.32 ± 0.29, Z = 4.616, p < 0.001), dyad 4 (S2 and S3; 0.91 ± 0.27, Z = 3.354, p < 0.05) and dyad 5 (S2 and S4; 1.23 ± 0.28, Z = 4.362, p < 0.001). Dyad 6 also engaged in more negative interactions per observation period than dyad 1 (1.68 ± 0.48, Z = 3.510, p < 0.01) and dyad 5 (1.75 ± 0.48, Z = 3.621, p < 0.01).

Frequency of social interactions at the level of individual dyads also differed across the study months ( Figure 4 and Figure 5 ). Positive interactions were lower in June than February (−0.70 ± 0.20, Z = −3.561, p < 0.01) and April (−0.61 ± 0.19, Z = -3.213, p < 0.05). There was a trend towards positive interactions being lower in June than March (−0.54 ± 0.20, Z = −2.686, p = 0.07) and lower in July than February (−0.56 ± 0.20, Z = 2.753, p = 0.06). Negative interactions were less frequent in March (−0.58 ± 0.2, Z = -2.832, p = 0.05) and April (−0.55 ± 0.19, Z = −2.833, p = 0.05) than February.

Frequency of positive social interactions given within each seal dyad ( n = 6; dyad 1: S1 and S2, dyad 2: S1 and S3, dyad 3: S1 and S4, dyad 4: S2 and S3, dyad 5: S2 and S4, dyad 6: S3 and S4) per observation period.

Frequency of negative social interactions given within each seal dyad ( n = 6; dyad 1: S1 and S2, dyad 2: S1 and S3, dyad 3: S1 and S4, dyad 4: S2 and S3, dyad 5: S2 and S4, dyad 6: S3 and S4) per observation period.

3.2. Fluidity in Group-Level Interactions over Time and Dyadic Reciprocity

Fluidity was observed in seal social interactions at a whole-group level. Positive social interaction networks were only consistent in March and May (r = 0.995, p < 0.05). Negative social interactions varied across all comparison points ( p > 0.05). Dyadic reciprocity for positive social interactions was observed during February (r = 0.9376, p < 0.05), March (r = 0.9978, p < 0.05) and April (r = 0.9715, p < 0.05). Reciprocal relationships were not observed for positive social interactions from May to July ( p > 0.05). Negative interactions were unbalanced across all study months ( p > 0.05).

3.3. Nearest Neighbours

An overview of the average percentage of scans that seals were observed in close proximity to others within each observation period is detailed in Table 3 . S1 was most frequently sighted alone (mean percent of time ± SD; 46 ± 24%), S2 was most frequently observed with S3 (34 ± 21% of time), S3 was most frequently observed with S4 (42 ± 22%) and S4 was most frequently observed with S3 (44 ± 23%).

Average percentage of each observation period that seals were observed with others.

Matrices based on nearest neighbours were fluid across all months, apart from February and March (r = 0.8611, p < 0.05) and March and May (r = 0.7427, p < 0.05), when nearest neighbour matrices were consistent. Nearest neighbours were reciprocal for all months (February: r = 0.9673, p < 0.05; March: r = 0.9858, p < 0.05; April: r = 0.8643, p < 0.05; June: r = 0.9887, p < 0.05; July: r = 0.9915, p < 0.05; August: r = 0.9674, p < 0.05), except for May ( p > 0.05). With the exception of May, where nearest neighbour reflected positive interaction partners (r = 0.88, p < 0.05), no correlations were identified between nearest neighbours and positive social interaction partners ( p > 0.05).

4. Discussion

Bachelor groups have been advocated as a zoological management tool which helps to support captive breeding programmes. Bachelor groups provide surplus males opportunities for socialisation [ 35 ], development of social skills [ 11 ] and in the case of younger males, to learn from older conspecifics [ 36 , 37 ], which is important for their welfare. Management of bachelor herds can be complicated by enclosure size/design limitations and behavioural incompatibilities between individuals [ 38 ]. Areas of importance in the successful formation and maintenance of bachelor groups include formation when animals are young in order to optimise group stability, limited inclusion of hand-reared individuals within the social group, exhibit designs which include opportunities for refuge, visual barriers and opportunities for separation of individuals where required [ 11 ]. Evidence-based management of social groups is extremely important in ensuring optimal welfare for zoo animals. This research contributes to our limited knowledge of SAFS social behaviour, and provides a baseline point for future work in this field, which has ramifications for the social management of this species.

4.1. Positive and Negative Social Interactions

Understanding social dynamics at a species-specific level helps to mitigate against management-related social disturbances in zoo-housed animals [ 17 ]. Social and play behaviours are indicative of positive affective states in zoo animals [ 39 ] and their absence may be indicative of problems in pinnipeds [ 9 ]. All individuals engaged in both positive and negative social interactions and were considered to be equal in terms of position within social networks. No extreme negative interactions were observed, and seals consistently engaged in more positive than negative interactions each month. Dyadic reciprocity was observed for frequency of positive social interactions across some but not all months. No dyadic reciprocity was seen in terms of frequency of negative interactions but nearest neighbours were largely reciprocal. In many bachelor groups, social hierarchies are used to mitigate social tension, with individuals engaging in social behaviour appropriate for their social rank [ 35 ]. Research into wild bachelor groups of New Zealand fur seals identified a tendency for non-physical interactions (e.g., vocalisation and ritualised dominance displays), which rarely escalated to aggression [ 40 ]. Relatedness and familiarity have been identified as potential drivers of success in bachelor groups of zoo-housed gorillas ( Gorilla gorilla gorilla ) [ 41 ]. No extreme aggression was seen and there was no evidence of a strong social hierarchy in this group. This may be due to the relatedness of the group and familiarity of members, or it may be that hierarchical behaviours were more subtle (e.g., dominance displays). Looking in greater depth at the types of negative behaviour and circumstances in which they occur in future work would enable a greater understanding of hierarchical structures in this and other groups of SAFSs, which would contribute to management which optimises animal welfare.

4.2. Temporality in Social Relationships

Longitudinal management of bachelor groups supports optimal species management [ 42 ], and the importance of understanding social networks of animals over time has been highlighted [ 18 ]. This research advocates such recommendations in SAFSs. Temporal changes in positive and negative social interactions were observed over the study months. However, the frequency of negative interactions did not increase in relation to the breeding season. Many polygynous species become highly aggressive and territorial during the breeding season [ 43 ]. However, in situ observations suggest that only low levels of interactions occur between non-breeding male SAFSs [ 8 ]. Within this bachelor group, individuals did not need to defend territories or fight over access to females, and this may explain the lack of relationship between time of year and frequency of negative interactions. Similar findings have been reported in zoo-housed western lowland gorilla bachelor groups [ 44 ].

The reasons for the temporal dynamics in relationships observed in this group are unclear. There may have been other confounding variables such as keeper routines, weather, or presence of the public which were beyond the scope of this research. Capturing data on temporal dynamics is important in understanding animal social networks [ 21 ]. Having a baseline understanding of the degree to which one expects seal social relationships to be flexible will enable early identification of deviations beyond the expected norm, which may be indicative of social complications within the group. This could occur as the seals age, or if there are any underlying illnesses or other problems. It is recommended that further research be undertaken in other facilities to determine the degree to which these results reflect other bachelor groups of SAFSs, especially when males are housed with non-kin. Furthermore, comparisons with social behaviour in female-only and mixed-sex groups will allow the development of evidence-based management protocols in SAFSs. Such knowledge could also be expanded into other pinniped species.

4.3. Factors Affecting Observed Social Interactions

In this study, individuals S3 and S4 engaged in more positive interactions than other dyads. S3 and S4 were the youngest members of the social group. S1, the oldest member of the social group, spent more time alone than S3 and S4. Age has been identified as an important driver of social relationships. Age could thus be driving the strong relationship and high frequency of interactions between S3 and S4. In gorillas, frequency of affiliative behaviour is reduced in individuals over 10 years old [ 11 ], and formation of positive long-term relationships is affected principally by familiarity and relatedness [ 41 ]. In wild golden snub-nosed monkeys ( Rhinopithecus roxellana ), similarly aged males that played together in breeding groups formed preferential relationships when they later joined a bachelor group [ 36 ]. In elephants, calves engage in most social interactions [ 45 ] and physical social interactions are reduced in older individuals [ 46 ], especially within bachelor groups [ 47 ].

There is also the potential for individual personalities to be impacting on these relationships. Individual personalities affect the experience of animals in zoo environments [ 48 ]. Personalities have been recognised in pinnipeds [ 49 ] and social preferences have been observed in breeding colonies of wild Galapagos sea lions ( Zalophus wollebaeki ) [ 1 ]. Personality has been identified as a driver of friendships [ 50 ], and it has been linked to sociability [ 51 , 52 ] and compatibility between individuals [ 53 , 54 ]. Age, rearing, relatedness, familiarity, relationship quality and enclosure design may all impact on the success of social groups and valence of social interactions in bachelor groups [ 11 ]. Across multi-sex social groups, age, familiarity and individual personalities have all been identified as having positive effects on individual compatibility [ 50 , 54 , 55 , 56 ]. The EAZA pinniped guidelines recommend consideration of the ‘character’ of male pinnipeds when designing social housing [ 9 ]. Future research should seek to understand factors which drive social relationships in SAFSs, particularly in bachelor groups, to enable the provision of social groups which optimise individual welfare.

4.4. Implications for Management of South American Fur Seals and Other Pinniped Species

Conflict avoidance techniques have been identified in a number of species, including rhesus macaques ( Macaca mulatta ), bonobos ( Pan paniscus ), chimpanzees ( Pan troglodytes ) [ 57 ] and gorillas [ 44 ]. In this study, the two seals who engaged in fewer social interactions also spent longer periods of time alone. There were no female SAFSs housed at Bristol Zoo at the time of this study, nor were there any other pinniped species on site. This means there was an absence of ‘high value’ resources (e.g., females). Research in gorillas indicates that males in bachelor groups engage in more displacement behaviour than males in breeding groups, and males in breeding groups engage in more non-escalated aggression than those in bachelor groups. These differences have been attributed to the lack of females in the bachelor group and employment of conflict reduction techniques in the breeding group [ 44 ]. Similar conflict reduction techniques may be being employed by this bachelor group of SAFSs. Adequate space to avoid others is advocated in bachelor group management [ 58 ] and it is highlighted in the EAZA pinniped management guidelines [ 9 ]. The opportunity to choose to interact with or avoid conspecifics is important for ensuring positive welfare in social animals [ 59 ]. The results of this work highlight the importance of providing environments which enable seals to execute conflict avoidance behaviours, thus ensuring successful SAFS bachelor group management.

Sociability can be identified via both physical interaction and proximity to others. Variability was seen between social interaction partners and nearest neighbours in this group of SAFSs. This variability highlights the need to fully understand social dynamics in SAFSs before making long-term management decisions. Proximity to group members and engagement in social interactions should both be considered in animal behaviour assessments. A sudden reduction in positive social interactions, increased time spent alone or increased negative social interactions, beyond the natural flexibility of the social group, may be a cause for concern [ 60 ]. Creating behavioural profiles which detail baseline social behaviour for individuals will allow early identification of potential problems in management of SAFSs and other pinniped species. Extreme individual behavioural changes, especially increases in negative interactions, not only impact the individual but it could affect the social stability of the entire group. Zoo researchers advocate understanding animal welfare at an individual level [ 59 ] and we support this recommendation in relation to SAFSs and wider pinniped social management.

Finally, it is important to recognise that this research was undertaken on a small sample of SAFSs at a single facility and thus may not be representative of wider SAFS management. However, the group itself was typical of the size of social groups SAFSs are reportedly housed in within ZIMS collections [ 20 ]. The study can thus be considered important in beginning to advance knowledge of social behaviour in this understudied species. To increase the applicability of these findings, it is advocated that future research is undertaken to understand how comparable the behaviour recorded here is to other bachelor groups and other types of socially housed SAFSs (e.g., mixed-sex and female-only groups) to determine the degree to which this social group is representative of other socially housed SAFSs, and how comparable SAFS social behaviour is to other more well-studied species (e.g., primates). If similar concepts are present and similar factors appear to be driving interactions within groups of SAFSs, this would allow wider application of the detailed primate literature. Expansion of this into pinniped species as a whole would be beneficial moving forwards to support in the development of evidence-based pinniped social management guidelines.

5. Conclusions

This research has highlighted the paucity of evidence-based knowledge on South American fur seal and wider pinniped species’ social behaviour within zoos. In the wild, SAFSs engage in complex social behaviour and spend large periods of time in extensive social groups in breeding rookeries. As a polygynous species, there may be a need to house surplus males in bachelor groups and understanding the implications of such management is important in ensuring positive welfare at an individual level. This research found that this group of SAFSs engaged in a range of positive social interactions and negative social interactions remained low throughout. Despite the consistent nature of this zoo social group (i.e., there were no changes in social group members during these observations), social interactions were not static. There were large amounts of individual variation and temporal dynamics were observed in social relationships. We have demonstrated the importance of longitudinal monitoring of social relationships, to establish baseline social behaviour profiles and support evidence-based species management. In particular, we recommend the monitoring of negative social interactions, as these may be indicative of problems developing within the social group. We advocate that this research is extended, to further develop our understanding of SAFS social needs within zoo environments, understand the differences between single-sex and mixed-sex groups and identify the degree to which the extensive research conducted in other polygynous species (e.g., gorillas) is applicable in the social management of these species moving forwards. Enhancing understanding of SAFS social needs will enable the development of evidence-based social management guidelines, which are imperative for ensuring optimal welfare for zoo-housed SAFSs.

Acknowledgments

Special thanks are extended to S.G. and the seal-keeping team at the Bristol Zoological Society, for their ongoing support throughout the study. Thanks are also extended to anonymous reviewers who provided feedback on an earlier version of this manuscript.

Author Contributions

Conceptualisation, C.E.; methodology, C.E.; formal analysis, C.E. and E.W.; investigation, C.E., M.D. and J.P.; data curation, C.E., M.D. and J.P.; writing—original draft preparation, C.E., M.D., J.P. and E.W.; writing—review and editing, C.E., M.D., J.P. and E.W.; visualisation, C.E.; supervision, E.W.; project administration, C.E. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Ethical approval for this study was granted by the Bristol Zoological Society Field Conservation and Science Research Committee (date of approval: 26 October 2018).

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

2.2 Research Methods

Learning objectives.

By the end of this section, you should be able to:

• Recall the 6 Steps of the Scientific Method
• Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysis.
• Explain the appropriateness of specific research approaches for specific topics.

Sociologists examine the social world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study. Planning the research design is a key step in any sociological study. Sociologists generally choose from widely used methods of social investigation: primary source data collection such as survey, participant observation, ethnography, case study, unobtrusive observations, experiment, and secondary data analysis , or use of existing sources. Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use. When you are conducting research think about the best way to gather or obtain knowledge about your topic, think of yourself as an architect. An architect needs a blueprint to build a house, as a sociologist your blueprint is your research design including your data collection method.

When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher wouldn’t stroll into a crime-ridden neighborhood at midnight, calling out, “Any gang members around?”

Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviors, early education, or the Ku Klux Klan. Researchers can’t just stroll into prisons, kindergarten classrooms, or Klan meetings and unobtrusively observe behaviors or attract attention. In situations like these, other methods are needed. Researchers choose methods that best suit their study topics, protect research participants or subjects, and that fit with their overall approaches to research.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviors and opinions, often in the form of a questionnaire or an interview. The survey is one of the most widely used scientific research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

At some point, most people in the United States respond to some type of survey. The 2020 U.S. Census is an excellent example of a large-scale survey intended to gather sociological data. Since 1790, United States has conducted a survey consisting of six questions to received demographical data pertaining to residents. The questions pertain to the demographics of the residents who live in the United States. Currently, the Census is received by residents in the United Stated and five territories and consists of 12 questions.

Not all surveys are considered sociological research, however, and many surveys people commonly encounter focus on identifying marketing needs and strategies rather than testing a hypothesis or contributing to social science knowledge. Questions such as, “How many hot dogs do you eat in a month?” or “Were the staff helpful?” are not usually designed as scientific research. The Nielsen Ratings determine the popularity of television programming through scientific market research. However, polls conducted by television programs such as American Idol or So You Think You Can Dance cannot be generalized, because they are administered to an unrepresentative population, a specific show’s audience. You might receive polls through your cell phones or emails, from grocery stores, restaurants, and retail stores. They often provide you incentives for completing the survey.

Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel, think, and act—or at least how they say they feel, think, and act. Surveys can track preferences for presidential candidates or reported individual behaviors (such as sleeping, driving, or texting habits) or information such as employment status, income, and education levels.

A survey targets a specific population , people who are the focus of a study, such as college athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes. Most researchers choose to survey a small sector of the population, or a sample , a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. As a result, a Gallup Poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people.

After selecting subjects, the researcher develops a specific plan to ask questions and record responses. It is important to inform subjects of the nature and purpose of the survey up front. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument, which is a means of gathering the information.

A common instrument is a questionnaire. Subjects often answer a series of closed-ended questions . The researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question. This kind of questionnaire collects quantitative data —data in numerical form that can be counted and statistically analyzed. Just count up the number of “yes” and “no” responses or correct answers, and chart them into percentages.

Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” or checkbox options. These types of inquiries use open-ended questions that require short essay responses. Participants willing to take the time to write those answers might convey personal religious beliefs, political views, goals, or morals. The answers are subjective and vary from person to person. How do you plan to use your college education?

Some topics that investigate internal thought processes are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of personal explanation is qualitative data —conveyed through words. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of in-depth material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and it is a way of conducting surveys on a topic. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly.

Questions such as “How does society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. The researcher will also benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Surveys often collect both quantitative and qualitative data. For example, a researcher interviewing people who are incarcerated might receive quantitative data, such as demographics – race, age, sex, that can be analyzed statistically. For example, the researcher might discover that 20 percent of incarcerated people are above the age of 50. The researcher might also collect qualitative data, such as why people take advantage of educational opportunities during their sentence and other explanatory information.

The survey can be carried out online, over the phone, by mail, or face-to-face. When researchers collect data outside a laboratory, library, or workplace setting, they are conducting field research, which is our next topic.

Field Research

The work of sociology rarely happens in limited, confined spaces. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people think and behave. It seeks to understand why they behave that way. However, researchers may struggle to narrow down cause and effect when there are so many variables floating around in a natural environment. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables. Indeed, much of the data gathered in sociology do not identify a cause and effect but a correlation .

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

Sociologists have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and fingers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see whether anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a writer, or a sociologist, will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. A researcher might work as a waitress in a diner, experience homelessness for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in analyzing data and generating results.

In a study of small towns in the United States conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in U.S. towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised the purpose of their study.

This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd & Lynd, 1929).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behavior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job.

Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book and describe what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study . To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

The book she wrote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms.

Ethnography

Ethnography is the immersion of the researcher in the natural setting of an entire social community to observe and experience their everyday life and culture. The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a social group.

An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record data, and collate the material into results.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith (1990), institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male- dominated societies and power structures. Smith’s work is seen to challenge sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from the male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada n.d.). Smith’s three major works explored what she called “the conceptual practices of power” and are still considered seminal works in feminist theory and ethnography (Fensternmaker n.d.).

Sociological Research

The making of middletown: a study in modern u.s. culture.

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000) as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minorities or outsiders—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds objectively described what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. As a result, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that Muncie was divided into business and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was an emerging material reality of the 1920s.

As the Lynds worked, they divided their manuscript into six chapters: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

Middletown: A Study in Modern American Culture was not only published in 1929 but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (Caplow, Hicks, & Wattenberg. 2000).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times. Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data was important—and interesting—to the U.S. public.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that while offering depth on a topic, it does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can contribute tremendous insight. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” growth and nurturing. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be obtained by any other method.

Experiments

You have probably tested some of your own personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis.

One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach.

There are two main types of experiments: lab-based experiments and natural or field experiments. In a lab setting, the research can be controlled so that more data can be recorded in a limited amount of time. In a natural or field- based experiment, the time it takes to gather the data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher.

As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens (cause), then another particular thing will result (effect). To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables.

Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group. The experimental group is exposed to the independent variable(s) and the control group is not. To test the benefits of tutoring, for example, the sociologist might provide tutoring to the experimental group of students but not to the control group. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record of a student, for example.

And if a researcher told the students they would be observed as part of a study on measuring the effectiveness of tutoring, the students might not behave naturally. This is called the Hawthorne effect —which occurs when people change their behavior because they know they are being watched as part of a study. The Hawthorne effect is unavoidable in some research studies because sociologists have to make the purpose of the study known. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985).

A real-life example will help illustrate the process. In 1971, Frances Heussenstamm, a sociology professor at California State University at Los Angeles, had a theory about police prejudice. To test her theory, she conducted research. She chose fifteen students from three ethnic backgrounds: Black, White, and Hispanic. She chose students who routinely drove to and from campus along Los Angeles freeway routes, and who had had perfect driving records for longer than a year.

Next, she placed a Black Panther bumper sticker on each car. That sticker, a representation of a social value, was the independent variable. In the 1970s, the Black Panthers were a revolutionary group actively fighting racism. Heussenstamm asked the students to follow their normal driving patterns. She wanted to see whether seeming support for the Black Panthers would change how these good drivers were treated by the police patrolling the highways. The dependent variable would be the number of traffic stops/citations.

The first arrest, for an incorrect lane change, was made two hours after the experiment began. One participant was pulled over three times in three days. He quit the study. After seventeen days, the fifteen drivers had collected a total of thirty-three traffic citations. The research was halted. The funding to pay traffic fines had run out, and so had the enthusiasm of the participants (Heussenstamm, 1971).

Secondary Data Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data analysis . Secondary data does not result from firsthand research collected from primary sources, but are the already completed work of other researchers or data collected by an agency or organization. Sociologists might study works written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines, or organizational data from any period in history.

Using available information not only saves time and money but can also add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behavior and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or social media.

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like the U.S. Bureau of Labor Statistics or the World Health Organization (WHO), publish studies with findings that are useful to sociologists. A public statistic like the foreclosure rate might be useful for studying the effects of a recession. A racial demographic profile might be compared with data on education funding to examine the resources accessible by different groups.

One of the advantages of secondary data like old movies or WHO statistics is that it is nonreactive research (or unobtrusive research), meaning that it does not involve direct contact with subjects and will not alter or influence people’s behaviors. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process.

Using available data does have its challenges. Public records are not always easy to access. A researcher will need to do some legwork to track them down and gain access to records. To guide the search through a vast library of materials and avoid wasting time reading unrelated sources, sociologists employ content analysis , applying a systematic approach to record and value information gleaned from secondary data as they relate to the study at hand.

Also, in some cases, there is no way to verify the accuracy of existing data. It is easy to count how many drunk drivers, for example, are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not survey the topic from the precise angle the researcher seeks. For example, the average salaries paid to professors at a public school is public record. But these figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they’ve been teaching.

When conducting content analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, when Robert S. Lynd and Helen Merrell Lynd gathered research in the 1920s, attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal insights about small U.S. communities. Today, it is an illustration of 1920s attitudes and values.

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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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Home » Designing Social Capital Sensitive Participation Methodologies » Participation Case Study » Focus Groups – Participation Case Study

• Focus Groups – Participation Case Study Part of 2004 Report "Designing Social Capital Sensitive Participation Methodologies"

Focus groups were held in several villages in each project area. They were conducted in late morning and a set time frame of two hours was established and catering was provided. Attention was paid to include all social groups that had been identified for each project. Fifteen people were asked to attend each focus group and the seats were placed in a circle to encourage participation and even power distributions. It was observed that participants did not sit in a circle, the socially dominant members of the group would sit directly in front of the facilitator and the remaining people would move chairs to sit behind or they would sit on the ground behind. The vast majority of participation can from the participants sitting directly in front of the facilitator.

Holding the focus groups in the late morning would exclude economically disadvantaged groups who could not afford the time away from work activities. In terms of project outcomes, this undermines the effectiveness of the project as the input of an important social group is missed. This exclusion further disadvantages poor people as their interests are not represented in project development. In terms of social capital theory, this exclusion represents a missed opportunity for poor people to make bridging connections, to strengthen bonding connections and benefit from information flows. This has a particularly significant impact as social capital is one of poor peoples’ major assets, particularly in terms of minimizing vulnerability.

Underprivileged groups are further disadvantaged by seating patterns. As identified above, seats were arranged in a circle to facilitate participation, however in all project sites, the participants did not sit in a circle. In reality, seating arrangement was determined by social stratification and disadvantaged groups always sat behind and often lower than the socially superior members. This further limited the participation of the disadvantaged groups, which limited the opportunities for building social capital. In this respect the project’s participatory planning failed to include all stakeholder groups.

The potential for building social capital is particularly significant given the effort of the project team to include all social groups identified. Combining members of social groups that are likely to have internal network closure, places the focus group participants in the unique position of being located at various structural holes simultaneously. The most significant benefit of being in this location is information flows, however another benefit – norms of reciprocity – could also be established between social groups, through individual members. This is best understood as empathy. Given the opportunity to hear the views and perspectives of different social groups develops a two way understanding that reduces the perceived gap, thereby initiating the process of forming norms of reciprocity. This process also allows for greater collaboration through identification of opportunities for individual or mutual benefit. In developed countries it is widely acknowledged that one can benefit from ‘networking’ opportunities at conferences, meetings and through interest groups.

The findings above represent social capital benefits that may be realized only under ideal circumstances. In the situation identified in the case study, participation of all members of the focus group did not occur due to power differentials and therefore the opportunity for norms to develop was limited, especially given only a one-off 2 hour focus group. Focus group theory in a developed country context would suggest that as participant numbers reached as high as 15, only some people would participate. In the case study context, people disadvantaged by social standing or gender were precluded from participation. The result was a disequilibrium of power where the views of the higher social classes prevailed and this was evident in the results of the project focus groups. This represents a further failure of the project’s participation plan, which aimed to include all stakeholders in the process.

In the developed country context, gender and social standing generally do not determine the pattern of participation. Other characteristics of the participants tend to be determinants of participation, such as the personality, education and knowledge, level of interest or motivation and attainment (in terms of being invited to attend). A common grouping distinction is ‘social group’ in developing country context and ‘stakeholder group’ in developed countries. The social capital building potential is also much greater in developed countries, particularly in urban areas. Under these circumstances it is unlikely that participants would know each other and the contact would establish weak ties and even in the short term, the establishment of group mores and norms of reciprocity. The role of residential proximity would have an impact of the strength and longevity of the weak ties formed through participation in the focus group and therefore the likelihood of reconnection and strengthening of the ties that represent social capital formation. Based on these findings, social capital building potential in the developed country context could be maximized by holding a series of focus groups with a smaller number of participants from a wide range of interest groups originating from a close residential proximity. This would maximize participation as well as allow time for formation of weak ties and norms of reciprocity that are more likely to endure after the participation process ends. Focus group participants would be located at various structural holes simultaneously as well as have inter- social group norms of reciprocity.

In the case study, participation could have been maximized by holding separate, small, focus groups for different social groups. This would separate disadvantaged groups who are inhibited by power differentials, enabling effective participation of all groups. The project background data identified gender power imbalances, which represents a need for separate focus groups. Although not carried out, it is acknowledged that the project may have been constrained by time or resources. Holding separate focus groups would limit social capital building opportunities in situations with high levels of participation but as discussed above, the project suffered from a lack of participation and thus holding separate focus groups would have little impact on the overall opportunities for generating social capital. It is recommended that providing feedback on issues that were identified to the whole group could enhance social capital building. This could be either through a public meeting or a meeting of all focus group participants. If further social capital building was an objective of the project, a further round of focus groups could then be help, mixing key individuals from different social groups, identified from observation. The familiarity of the topic and process, and increased empathy, should enable more even participation of all focus group members and allow for tie formation and establishment of group norms. Cooperation could be enhanced with benefits for project effectiveness and general civil society.

The key recommendation for the developed country context is that holding a number of focus groups with amble opportunity for networking could maximize social capital building. Although not as significant as in developing countries, giving feedback by way of a public meeting would increase understanding between stakeholder groups. This may have benefits for project effectiveness and social capital through enhanced community cohesion and understanding.

• Dissemination of Information – Participation Case Study
• Public Meetings – Participation Case Study
• Questionnaires – Participation Case Study
• Recommendations – Participation Case Study

Tristan Claridge

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• Open access
• Published: 10 April 2024

“So at least now I know how to deal with things myself, what I can do if it gets really bad again”—experiences with a long-term cross-sectoral advocacy care and case management for severe multiple sclerosis: a qualitative study

• Anne Müller   ORCID: orcid.org/0000-0002-2456-2492 1 ,
• Fabian Hebben   ORCID: orcid.org/0009-0003-6401-3433 1 ,
• Kim Dillen 1 ,
• Veronika Dunkl 1 ,
• Yasemin Goereci 2 ,
• Raymond Voltz 1 , 3 , 4 ,
• Peter Löcherbach 5 ,
• Clemens Warnke   ORCID: orcid.org/0000-0002-3510-9255 2 &
• Heidrun Golla   ORCID: orcid.org/0000-0002-4403-630X 1

on behalf of the COCOS-MS trial group represented by Martin Hellmich

BMC Health Services Research volume  24 , Article number:  453 ( 2024 ) Cite this article

57 Accesses

Metrics details

Persons with severe Multiple Sclerosis (PwsMS) face complex needs and daily limitations that make it challenging to receive optimal care. The implementation and coordination of health care, social services, and support in financial affairs can be particularly time consuming and burdensome for both PwsMS and caregivers. Care and case management (CCM) helps ensure optimal individual care as well as care at a higher-level. The goal of the current qualitative study was to determine the experiences of PwsMS, caregivers and health care specialists (HCSs) with the CCM.

In the current qualitative sub study, as part of a larger trial, in-depth semi-structured interviews with PwsMS, caregivers and HCSs who had been in contact with the CCM were conducted between 02/2022 and 01/2023. Data was transcribed, pseudonymized, tested for saturation and analyzed using structuring content analysis according to Kuckartz. Sociodemographic and interview characteristics were analyzed descriptively.

Thirteen PwsMS, 12 caregivers and 10 HCSs completed interviews. Main categories of CCM functions were derived deductively: (1) gatekeeper function, (2) broker function, (3) advocacy function, (4) outlook on CCM in standard care. Subcategories were then derived inductively from the interview material. 852 segments were coded. Participants appreciated the CCM as a continuous and objective contact person, a person of trust (92 codes), a competent source of information and advice (on MS) (68 codes) and comprehensive cross-insurance support (128 codes), relieving and supporting PwsMS, their caregivers and HCSs (67 codes).

Conclusions

Through the cross-sectoral continuous support in health-related, social, financial and everyday bureaucratic matters, the CCM provides comprehensive and overriding support and relief for PwsMS, caregivers and HCSs. This intervention bears the potential to be fine-tuned and applied to similar complex patient groups.

Trial registration

The study was approved by the Ethics Committee of the University of Cologne (#20–1436), registered at the German Register for Clinical Studies (DRKS00022771) and in accordance with the Declaration of Helsinki.

Peer Review reports

Introduction

Multiple sclerosis (MS) is the most frequent and incurable chronic inflammatory and degenerative disease of the central nervous system (CNS). Illness awareness and the number of specialized MS clinics have increased since the 1990s, paralleled by the increased availability of disease-modifying therapies [ 1 ]. There are attempts in the literature for the definition of severe MS [ 2 , 3 ]. These include a high EDSS (Expanded disability Status Scale [ 4 ]) of ≥ 6, which we took into account in our study. There are also other factors to consider, such as a highly active disease course with complex therapies that are associated with side effects. These persons are (still) less disabled, but may feel overwhelmed with regard to therapy, side effects and risk monitoring of therapies [ 5 , 6 ].

Persons with severe MS (PwsMS) develop individual disease trajectories marked by a spectrum of heterogeneous symptoms, functional limitations, and uncertainties [ 7 , 8 ] manifesting individually and unpredictably [ 9 ]. This variability can lead to irreversible physical and mental impairment culminating in complex needs and daily challenges, particularly for those with progressive and severe MS [ 5 , 10 , 11 ]. Such challenges span the spectrum from reorganizing biographical continuity and organizing care and everyday live, to monitoring disease-specific therapies and integrating palliative and hospice care [ 5 , 10 ]. Moreover, severe MS exerts a profound of social and economic impact [ 9 , 12 , 13 , 14 ]. PwsMS and their caregivers (defined in this manuscript as relatives or closely related individuals directly involved in patients’ care) often find themselves grappling with overwhelming challenges. The process of organizing and coordinating optimal care becomes demanding, as they contend with the perceived unmanageability of searching for, implementing and coordinating health care and social services [ 5 , 15 , 16 , 17 ].

Case management (CM) proved to have a positive effect on patients with neurological disorders and/or patients with palliative care needs [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. However, a focus on severe MS has been missed so far Case managers primarily function as: (1) gatekeeper involving the allocation of necessary and available resources to a case, ensuring the equitable distribution of resources; as (2) broker assisting clients in pursuing their interests, requiring negotiation to provide individualized assistance that aligns as closely as possible with individual needs and (3) advocate working to enhance clients’ individual autonomy, to advocate for essential care offers, and to identify gaps in care [ 25 , 26 , 27 , 28 , 29 ].

Difficulties in understanding, acting, and making decisions regarding health care-related aspects (health literacy) poses a significant challenge for 54% of the German population [ 30 ]. Additionally acting on a superordinate level as an overarching link, a care and case management (CCM) tries to reduce disintegration in the social and health care system [ 31 , 32 ]. Our hypothesis is that a CCM allows PwsMS and their caregivers to regain time and resources outside of disease management and to facilitate the recovery and establishment of biographical continuity that might be disrupted due to severe MS [ 33 , 34 ].

Health care specialists (HCSs) often perceive their work with numerous time and economic constraints, especially when treating complex and severely ill individuals like PwsMS and often have concerns about being blamed by patients when expectations could not be met [ 35 , 36 ]. Our hypothesis is that the CCM will help to reduce time constraints and free up resources for specialized tasks.

To the best of our knowledge there is no long-term cross-sectoral and outreaching authority or service dedicated to assisting in the organization and coordination of the complex care concerns of PwsMS within the framework of standard care addressing needs in health, social, financial, every day and bureaucratic aspects. While some studies have attempted to design and test care programs for persons with MS (PwMS), severely affected individuals were often not included [ 37 , 38 , 39 ]. They often remain overlooked by existing health and social care structures [ 5 , 9 , 15 ].

The COCOS-MS trial developed and applied a long-term cross-sectoral CCM intervention consisting of weekly telephone contacts and monthly re-assessments with PwsMS and caregivers, aiming to provide optimal care. Their problems, resources and (unmet) needs were assessed holistically including physical health, mental health, self-sufficiency and social situation and participation. Based on assessed (unmet) needs, individual care plans with individual actions and goals were developed and constantly adapted during the CCM intervention. Contacts with HCSs were established to ensure optimal care. The CCM intervention was structured through and documented in a CCM manual designed for the trial [ 40 , 41 ].

Our aim was to find out how PwsMS, caregivers and HCSs experienced the cross-sectoral long-term, outreaching patient advocacy CCM.

This study is part of a larger phase II, randomized, controlled clinical trial “Communication, Coordination and Security for people with severe Multiple Sclerosis (COCOS-MS)” [ 41 ]. This explorative clinical trial, employing a mixed-method design, incorporates a qualitative study component with PwsMS, caregivers and HCSs to enrich the findings of the quantitative data. This manuscript focuses on the qualitative data collected between February 2022 and January 2023, following the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines [ 42 ].

Research team

Three trained authors AM, KD and FH (AM, female, research associate, M.A. degree in Rehabilitation Sciences; KD, female, researcher, Dr. rer. medic.; FH, male, research assistant, B.Sc. degree in Health Care Management), who had no prior relationship with patients, caregivers or HCSs conducted qualitative interviews. A research team, consisting of clinical experts and health services researchers, discussed the development of the interview guides and the finalized category system.

Theoretical framework

Interview data was analyzed with the structuring content analysis according to Kuckartz. This method enables a deductive structuring of interview material, as well as the integration of new aspects found in the interview material through the inductive addition of categories in an iterative analysis process [ 43 ].

Sociodemographic and interview characteristics were analyzed descriptively (mean, median, range, SD). PwsMS, caregivers and HCSs were contacted by the authors AM, KD or FH via telephone or e-mail after providing full written informed consent. Participants had the option to choose between online interviews conducted via the GoToMeeting 10.19.0® Software or face-to-face. Peasgood et al. (2023) found no significant differences in understanding questions, engagement or concentration between face-to-face and online interviews [ 44 , 45 ]. Digital assessments were familiar to participants due to pandemic-related adjustments within the trial.

Out of 14 PwsMS and 14 caregivers who were approached to participate in interviews, three declined to complete interviews, resulting in 13 PwsMS (5 male, 8 female) and 12 caregiver (7 male, 5 female) interviews, respectively (see Fig.  1 ). Thirty-one HCSs were contacted of whom ten (2 male, 8 female) agreed to be interviewed (see Fig.  2 ).

Flowchart of PwsMS and caregiver participation in the intervention group of the COCOS-MS trial. Patients could participate with and without a respective caregiver taking part in the trial. Therefore, number of caregivers does not correspond to patients. For detailed inclusion criteria see also Table  1 in Golla et al. [ 41 ]

Flowchart of HCSs interview participation

Setting and data collection

Interviews were carried out where participants preferred, e.g. at home, workplace, online, and no third person being present. In total, we conducted 35 interviews whereof 7 interviews face-to-face (3 PwsMS, 3 caregivers, 1 HCS).

The research team developed a topic guide which was meticulously discussed with research and clinical staff to enhance credibility. It included relevant aspects for the evaluation of the CCM (see Tables  1 and 2 , for detailed topic guides see Supplementary Material ). Patient and caregiver characteristics (covering age, sex, marital status, living situation, EDSS (patients only), subgroup) were collected during the first assessment of the COCOS-MS trial and HCSs characteristics (age, sex, profession) as well as interview information (length and setting) were collected during the interviews. The interview guides developed for this study addressed consistent aspects both for PwsMS and caregivers (see Supplementary Material ):

For HCSs it contained the following guides:

Probing questions were asked to get more specific and in-depth information. Interviews were carried out once and recorded using a recording device or the recording function of the GoToMeeting 10.19.0® Software. Data were pseudonymized (including sensitive information, such as personal names, dates of birth, or addresses), audio files were safely stored in a data protection folder. The interview duration ranged from 11 to 56 min (mean: 23.9 min, SD: 11.1 min). Interviews were continued until we found that data saturation was reached. Audio recordings were transcribed verbatim by an external source and not returned to participants.

Data analysis

Two coders (AM, FH) coded the interviews. Initially, the first author (AM) thoroughly reviewed the transcripts to gain a sense of the interview material. Using the topic guide and literature, she deductively developed a category system based on the primary functions of CM [ 25 , 26 , 27 , 28 , 29 ]. Three interviews were coded repeatedly for piloting, and inductive subcategories were added when new themes emerged in the interview material. This category system proved suitable for the interview material. The second coder (FH) familiarized himself with the interview material and category system. Both coders (AM, FH) independently coded all interviews, engaging in discussions and adjusting codes iteratively. The finalized category system was discussed and consolidated in a research workshop and within the COCOS-MS trial group and finally we reached an intercoder agreement of 90% between the two coders AM and FH, computed by the MAXQDA Standard 2022® software.

We analyzed sociodemographic and interview characteristics using IBM SPSS Statistics 27® and Excel 2016®. Transcripts were managed and analyzed using MAXQDA Standard 2022®.

Participants were provided with oral and written information about the trial and gave written informed consent. Ethical approvals were obtained from the Ethics Committee of the University of Cologne (#20–1436). The trial is registered in the German Register for Clinical Studies (DRKS) (DRKS00022771) and is conducted under the Declaration of Helsinki.

Characteristics of participants and interviews

PwsMS participating in an interview were mainly German (84.6%), had a mean EDSS of 6.8 (range: 6–8) and MS for 13.5 years (median: 14; SD: 8.1). For detailed characteristics see Table  3 .

Most of the interviewed caregivers (9 caregivers) were the partners of the PwsMS with whom they lived in the same household. For further details see Table  3 .

HCSs involved in the study comprised various professions, including MS-nurse (3), neurologist (2), general physician with further training in palliative care (1), physician with further training in palliative care and pain therapist (1), housing counselling service (1), outpatient nursing service manager (1), participation counselling service (1).

Structuring qualitative content analysis

The experiences of PwsMS, caregivers and HCSs were a priori deductively assigned to four main categories: (1) gatekeeper function, (2) broker function, (3) advocacy function [ 25 , 26 , 27 , 28 , 29 ] and (4) Outlook on CCM in standard care, whereas the subcategories were developed inductively (see Fig.  3 ).

Category system including main and subcategories of the qualitative thematic content analysis

The most extensive category, housing the highest number of codes and subcodes, was the “ Outlook on CCM in standard care ” (281 codes). Following this, the category “ Advocacy Function ” contained 261 codes. The “ Broker Function ” (150 codes) and the “ Gatekeeper Function ” (160 codes) constituted two smaller categories. The majority of codes was identified in the caregivers’ interviews, followed by those of PwsMS (see Table  4 ). Illustrative quotes for each category and subcategory can be found in Table  5 .

Persons with severe multiple sclerosis

In the gatekeeper function (59 codes), PwsMS particularly valued the CCM as a continuous contact person . They appreciated the CCM as a person of trust who was reliably accessible throughout the intervention period. This aspect, with 41 codes, held significant importance for PwsMS.

Within the broker function (44 codes), establishing contact was most important for PwsMS (22 codes). This involved the CCM as successfully connecting PwsMS and caregivers with physicians and therapists, as well as coordinating and arranging medical appointments, which were highly valued. Assistance in authority and health and social insurance matters (10 codes) was another subcategory, where the CCM encompassed support in communication with health insurance companies, such as improving the level of care, assisting with retirement pension applications, and facilitating rehabilitation program applications. Optimized care (12 codes) resulted in improved living conditions and the provision of assistive devices through the CCM intervention.

The advocacy function (103 codes) emerged as the most critical aspect for PwsMS, representing the core of the category system. PwsMS experienced multidimensional, comprehensive, cross-insurance system support from the CCM. This category, with 43 statements, was the largest within all subcategories. PwsMS described the CCM as addressing their concerns, providing help, and assisting with the challenges posed by the illness in everyday life. The second-largest subcategory, regaining, maintaining and supporting autonomy (25 codes), highlighted the CCM’s role in supporting self-sufficiency and independence. Reviving personal wellbeing (17 codes) involved PwsMSs’ needs of regaining positive feelings, improved quality of life, and a sense of support and acceptance, which could be improved by the CCM. Temporal relief (18 codes) was reported, with the CCM intervention taking over or reducing tasks.

Within the outlook on CCM in standard care (84 codes), eight subcategories were identified. Communications was described as friendly and open (9 codes), with the setting of communication (29 codes) including the frequency of contacts deemed appropriate by the interviewed PwsMS, who preferred face-to-face contact over virtual or telephone interactions. Improvement suggestions for CCM (10 codes) predominantly revolved around the desire for the continuation of the CCM beyond the trial, expressing intense satisfaction with the CCM contact person and program. PwsMS rarely wished for better cooperation with the CCM. With respect to limitations (7 codes), PwsMS distinguished between individual limitations (e.g. when not feeling ready for using a wheelchair) and overriding structural limitations (e.g. unsuccessful search for an accessible apartment despite CCM support). Some PwsMS mentioned needing the CCM earlier in the course of the disease and believed it would beneficial for anyone with a chronic illness (6 codes).

In the gatekeeper function (75 codes), caregivers highly valued the CCM as a continuous contact partner (33 codes). More frequently than among the PwsMS interviewed, caregivers valued the CCM as a source of consultation/ information on essential individual subjects (42 codes). The need for basic information about the illness, its potential course, treatment and therapy options, possible supportive equipment, and basic medical advice/ information could be met by the CCM.

Within the broker function (63 codes), caregivers primarily experienced the subcategory establish contacts (24 codes). They found the CCM as helpful in establishing and managing contact with physicians, therapists and especially with health insurance companies. In the subcategory assistance in authority and health and social insurance matters (22 codes), caregivers highlighted similar aspects as the PwsMS interviewed. However, there was a particular emphasis on assistance with patients' retirement matters. Caregivers also valued the optimization of patients’ care and living environment (17 codes) in various life areas during the CCM intervention, including improved access to assistive devices, home modification, and involvement of a household support and/ or nursing services.

The advocacy function, with 115 codes, was by far the broadest category . The subcategory multidimensional, comprehensive, cross-insurance system support represented the largest subcategory of caregivers, with 70 statements. In summary, caregivers felt supported by the CCM in all domains of life. Regaining, maintaining and supporting autonomy (11 codes) and reviving personal wellbeing (8 codes) in the form of an improved quality of life played a role not only for patients but also for caregivers, albeit to a lower extend. Caregivers experienced temporal relief (26 codes) as the CCM undertook a wide range of organizational tasks, freeing up more needed resources for their own interests.

For the Outlook on CCM in standard care , caregivers provided various suggestions (81 codes). Similar to PwsMS, caregivers felt that setting (home based face-to-face, telephone, virtual) and frequency of contact were appropriate (10 codes, communication setting ) and communications (7 codes) were recognized as open and friendly. However, to avoid conflicts between caregiver and PwsMS, caregivers preferred meeting the CCM separately from the PwsMS in the future. Some caregivers wished the CCM to specify all services it might offer at the beginning, while others emphasized not wanting this. Like PwsMS, caregivers criticized the CCM intervention being (trial-related) limited to one year, regardless of whether further support was needed or processes being incomplete (13 codes, improvement suggestions ). After the CCM intervention time had expired, the continuous contact person and assistance were missed and new problems had arisen and had to be managed with their own resources again (9 codes, effects of CCM discontinuation ), which was perceived as an exhausting or unsolvable endeavor. Caregivers identified analogous limitations (8 codes), both individual and structural. However, the largest subcategory, was the experienced potential of CCM (27 codes), reflected in extremely high satisfaction with the CCM intervention. Like PwsMS, caregivers regarded severe chronically ill persons in general as target groups for a CCM (7 codes) and would implement it even earlier, starting from the time of diagnosis. They considered a CCM to be particularly helpful for patients without caregivers or for caregivers with limited (time) resources, as it was true for most caregivers.

Health care specialists

In the gatekeeper function (26 codes) HCSs particularly valued the CCM as a continuous contact partner (18 codes). They primarily described their valuable collaboration with the CCM, emphasizing professional exchange between the CCM and HCSs.

Within the broker function (43 codes), the CCM was seen as a connecting link between patients and HCSs, frequently establishing contacts (18 codes). This not only improved optimal care on an individual patient level (case management) but also at a higher, superordinate care level (care management). HCSs appreciated the optimized care and living environment (18 codes) for PwsMS, including improved medical and therapeutic access and the introduction of new assistive devices. The CCM was also recognized as providing assistance in authority and health and social matters (7 codes) for PwsMS and their caregivers.

In the advocacy function (43 codes), HCSs primarily reported temporal relief through CCM intervention (23 codes). They experienced this relief, especially as the CCM provided multidimensional, comprehensive, and cross-insurance system support (15 codes) for PwsMS and their caregivers. Through this support, HCSs felt relieved from time intensive responsibilities that may not fall within their area of expertise, freeing up more time resources for their actual professional tasks.

The largest category within the HCSs interviews was the outlook on CCM in standard care (116 codes). In the largest subcategory, HCSs made suggestions for further patient groups who could benefit (38 codes) from a CCM. Chronic neurological diseases like neurodegenerative diseases (e.g. amyotrophic lateral sclerosis), typical and atypical Parkinson syndromes were mentioned. HCSs considered the enrollment of the CCM directly after the diagnosis of these complex chronic diseases. Additionally, chronic progressive diseases in general or oncological diseases, which may also run chronically, were regarded worthwhile for this approach. HCSs also provided suggestions regarding improvement (21 codes). They wished e.g. for information or contact when patients were enrolled to the CCM, regular updates, exchange and collaborative effort. On the other hand, HCSs reported, that their suggestions for improvement would hardly be feasible due to their limited time resources. Similar to patients and caregivers, HCSs experienced structural limits (13 codes), which a CCM could not exceed due to overriding structural limitations (e.g. insufficient supply of (household) aids, lack of outreach services like psychotherapists, and long processing times on health and pension insurers' side). HCSs were also asked about their opinions on financial resources (14 codes) of a CCM in standard care. All interviewed HCSs agreed that CCM would initially cause more costs for health and social insurers, but they were convinced of cost savings in the long run. HCSs particularly perceived the potential of the CCM (20 codes) through the feedback of PwsMS, highlighting the trustful relationship enabling individualized help for PwsMS and their caregivers.

Persons with severe multiple sclerosis and their caregivers

The long-term cross-sectoral CCM intervention implemented in the COCOS-MS trial addressed significant unmet needs of PwsMS and their caregivers which previous research revealed as burdensome and hardly or even not possible to improve without assistance [ 5 , 6 , 9 , 10 , 33 , 35 , 46 ]. Notably, the CCM service met the need for a reliable, continuous contact partner, guiding patients through the complexities of regulations, authorities and the insurance system. Both, PwsMS and their caregivers highly valued the professional, objective perspective provided by the CCM, recognizing it as a source of relief, support and improved care in line with previous studies [ 37 , 47 ]. Caregivers emphasized the CCM’s competence in offering concrete assistance and information on caregiving and the fundamentals of MS, including bureaucratic, authority and insurances matters. On the other hand, PwsMS particularly appreciated the CCMs external reflective and advisory function, along with empathic social support tailored to their individual concerns. Above all, the continuous partnership of trust, available irrespective of the care sector, was a key aspect that both PwsMS and their caregivers highlighted. This consistent support was identified as one of the main components in the care of PwsMS in previous studies [ 5 , 33 , 35 ].

As the health literacy is inadequate or problematic for 54% of the German population and disintegration in the health and social care system is high [ 30 , 31 , 32 ], the CCM approach serves to enhance health literacy and reduce disintegration of PwsMS and their caregivers by providing cross-insurance navigational guidance in the German health and social insurance sector on a superordinate level. Simultaneously PwsMS and caregivers experienced relief and gained more (time) resources for all areas of life outside of the disease and its management, including own interests and establishing biographical continuity. This empowerment enables patients to find a sense of purpose beyond their illness, regain autonomy, and enhance social participation, reducing the feeling of being a burden to those closest to them. Such feelings are often experienced as burdensome and shameful by PwsMS [ 6 , 48 , 49 , 50 ]. Finding a sense of purpose beyond the illness also contributes to caregivers perceiving their loved ones not primarily as patient but as individuals outside of the disease, reinforcing valuable relationships such as partners, siblings, or children, strengthening emotional bonds. These factors are also highly relevant and well-documented in a suicide-preventive context, as the suicide rate is higher in persons diagnosed with neurological disorders [ 19 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ] and the feeling of being a burden to others, loss of autonomy, and perceived loss of dignity are significant factors in patients with severe chronic neurological diseases for suicide [ 50 , 57 ].

The temporal relief experienced by the CCM was particularly significant for HCSs and did not only improve the satisfaction of HCSs but also removed unfulfilled expectations and concerns about being blamed by patients when expectations could not be met, which previous studied elaborated [ 35 , 36 ]. Moreover, the CCM alleviated the burden on HCSs by addressing patients’ concerns, allowing them to focus on their own medical responsibilities. This aspect probably reduced the dissatisfaction that arises when HCSs are expected to address issues beyond their medical expertise, such as assistive devices, health and social insurance, and the organization and coordination of supplementary therapies, appointments, and contacts [ 35 , 36 , 61 ]. Consequently, the CCM reduced difficulties of HCSs treating persons with neurological or chronical illnesses, which previous research identified as problematic.

HCSs perceive their work as increasingly condensed with numerous time and economic constraints, especially when treating complex and severely ill individuals like PwsMS [ 36 ]. This constraint was mentioned by HCSs in the interviews and was one of the main reasons why they were hesitant to participate in interviews and may also be an explanation for a shorter interview duration than initially planned in the interview guides. The CCM’s overarching navigational competence in the health and social insurance system was particularly valued by HCSs. The complex and often small-scale specialties in the health and social care system are not easily manageable or well-known even for HCSs, and dealing with them can exceed their skills and time capacities [ 61 ]. The CCM played a crucial role in keeping (temporal) resources available for what HCSs are professionally trained and qualified to work on. However, there remains a challenge in finding solutions to the dilemma faced by HCSs regarding their wish to be informed about CCM procedures and linked with each other, while also managing the strain of additional requests and contact with the CCM due to limited (time) resources [ 62 ]. Hudon et al. (2023) suggest that optimizing time resources and improving exchange could involve meetings, information sharing via fax, e-mail, secure online platforms, or, prospectively, within the electronic patient record (EPR). The implementation of an EPR has shown promise in improving the quality of health care and time resources, when properly implemented [ 63 , 64 ]. The challenge lies ineffective information exchange between HCSs and CCM for optimal patient care. The prospect of time saving in the long run and at best for a financial incentive, e.g., when anchoring in the Social Security Code, will help best to win over the HCSs.If this crucial factor can be resolved, there is a chance that HCSs will thoroughly accept the CCM as an important pillar, benefiting not only PwsMS but also other complex patient groups, especially those with long-term neurological or complex oncological conditions that might run chronically.

Care and case management and implications for the health care system

The results of our study suggest that the cross-sectoral long-term advocacy CCM in the COCOS-MS trial, with continuous personal contacts at short intervals and constant reevaluation of needs, problems, resources and goals, is highly valued by PwsMS, caregivers, and HCSs. The trial addresses several key aspects that may have been overlooked in previous studies which have shown great potential for the integration of case management [ 17 , 47 , 62 , 65 , 66 ]. However, they often excluded the overriding care management, missed those patient groups with special severity and complexity who might struggle to reach social and health care structures independently or the interventions were not intended for long-term [ 22 , 37 ]. Our results indicate that the CCM intervention had a positive impact on PwsMS and caregivers as HCSs experienced them with benefits such as increased invigoration, reduced demands, and enhanced self-confidence. However, there was a notable loss experienced by PwsMS and caregivers after the completion of the CCM intervention, even if they had stabilized during the intervention period. The experiences of optimized social and health care for the addressed population, both at an individual and superordinate care level, support the integration of this service into standard care. Beyond the quantitatively measurable outcomes and economic considerations reported elsewhere [ 16 , 20 , 21 ], our results emphasize the importance of regaining control, self-efficacy, self-worth, dignity, autonomy, and social participation. These aspects are highlighted as preventive measures in suicidal contexts, which is particularly relevant for individuals with severe and complex illnesses [ 19 , 50 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. Our findings further emphasize the societal responsibilities to offer individuals with severe and complex illnesses the opportunity to regain control and meaningful aspects of life, irrespective of purely economic considerations. This underscores the need for a comprehensive evaluation that not only takes into account quantitative measures but also the qualitative aspects of well-being and quality of life when making recommendations of a CCM in standard care.

The study by J. Y. Joo and Huber (2019) highlighted that CM interventions aligned with the standards of the Case Management Society of America varied in duration, ranging from 1 month to 15.9 years, and implemented in community- or hospital-based settings. However, they noted a limitation in understanding how CM processes unfold [ 67 ]. In contrast, our trial addressed this criticism by providing transparent explanations of the CCM process, which also extends to a superordinate care management [ 40 , 41 ]. Our CCM manual [ 40 ] outlines a standardized and structured procedure for measuring and reevaluating individual resources, problems, and unmet needs on predefined dimensions. It also identifies goals and actions at reducing unmet needs and improving the individual resources of PwsMS and caregivers. Importantly, the CCM manual demonstrates that the CCM process can be structured and standardized, while accounting for the unique aspects of each individual’s serious illness, disease courses, complex needs, available resources, and environmental conditions. Furthermore, the adaptability of the CCM manual to other complex chronically ill patient groups suggests the potential for a standardized approach in various health care settings. This standardized procedure allows for consistency in assessing and addressing the individual needs of patients, ensuring that the CCM process remains flexible while maintaining a structured and goal-oriented framework.

The discussion about the disintegration in the social and health care system and the increasing specialization dates back to 2009 [ 31 , 32 ]. Three strategies were identified to address this issue: (a) “driver-minimizing” [Treiberminimierende], (b) “effect-modifying” [Effektmodifizierende] and (c) “disintegration-impact-minimizing” [Desintegrationsfolgenminimierende] strategies. “Driver-minimizing strategies” involve comprehensive and radical changes within the existing health and social care system, requiring political and social pursuit. “Disintegration-impact-minimizing strategies” are strategies like quality management or tele-monitoring, which are limited in scope and effectiveness. “Effect-modifying strategies”, to which CCM belongs, acknowledges the segmentation within the system but aims to overcome it through cooperative, communicative, and integrative measures. CCM, being an “effect-modifying strategy”, operates the “integrated segmentation model” [Integrierte Segmentierung] rather than the “general contractor model” [Generalunternehmer-Modell] or “total service provider model” [Gesamtdienstleister-Modell] [ 31 , 32 ]. In this model, the advantage lies in providing an overarching and coordinating service to link different HCSs and services cross-sectorally. The superordinate care management aspect of the CCM plays a crucial role in identifying gaps in care, which is essential for future development strategies within the health and social care system. It aims to find or develop (regional) alternatives to ensure optimal care [ 17 , 23 , 24 , 68 , 69 ], using regional services of existing health and social care structures. Therefore, superordinate care management within the CCM process is decisive for reducing disintegration in the system.

Strengths and limitations

The qualitative study results of the explorative COCOS-MS clinical trial, which employed an integrated mixed-method design, provide valuable insights into the individual experiences of three leading stakeholders: PwsMS, caregivers and HCSs with a long-term cross-sectoral CCM. In addition to in-depth interviews, patient and caregiver reported outcome measurements were utilized and will be reported elsewhere. The qualitative study’s strengths include the inclusion of patients who, due to the severity of their condition (e.g. EDSS mean: 6.8, range: 6–8, highly active MS), age (mean: 53.9 years, range: 36–73 years) family constellations, are often underrepresented in research studies and often get lost in existing social and health care structures. The study population is specific to the wider district region of Cologne, but the broad inclusion criteria make it representative of severe MS in Germany. The methodological approach of a deductive and inductive structuring content analysis made it possible to include new findings into an existing theoretical framework.

However, the study acknowledges some limitations. While efforts were made to include more HCSs, time constraints on their side limited the number of interviews conducted and might have biased the results. Some professions are underrepresented in the interviews. Complex symptoms (e.g. fatigue, ability to concentrate), medical or therapeutic appointments and organization of the everyday live may have been reasons for the patients’ and caregivers’ interviews lasting shorter than initially planned.

The provision of functions of a CCM, might have pre-structured the answers of the participants.

At current, there is no support system for PwsMS, their caregivers and HCSs that addresses their complex and unmet needs comprehensively and continuously. There are rare qualitative insights of the three important stakeholders: PwsMS, caregivers and HCSs in one analysis about a supporting service like a CCM. In response to this gap, we developed and implemented a long-term cross-sectoral advocacy CCM and analyzed it qualitatively. PwsMS, their caregivers and HCSs expressed positive experiences, perceiving the CCM as a source of relief and support that improved care across various aspects of life. For patients, the CCM intervention resulted in enhanced autonomy, reviving of personal wellbeing and new established contacts with HCSs. Caregivers reported a reduced organizational burden and felt better informed, and HCSs experienced primarily temporal relief, allowing them to concentrate on their core professional responsibilities. At a higher level of care, the study suggests that the CCM contributed to a reduction in disintegration within the social and health care system.

The feedback from participants is seen as valuable for adapting the CCM intervention and the CCM manual for follow-up studies, involving further complex patient groups such as neurological long-term diseases apart from MS and tailoring the duration of the intervention depending on the complexity of evolving demands.

Availability of data and materials

Generated and/or analyzed datasets of participants are available from the corresponding author on reasonable request to protect participants. Preliminary partial results have been presented as a poster during the EAPC World Congress in June 2023 and the abstract has been published in the corresponding abstract booklet [ 70 ].

Abbreviations

Amyotrophic lateral sclerosis

• Care and case management

Case management

Central nervous system

Communication, Coordination and security for people with multiple sclerosis

Consolidated criteria for reporting qualitative research

German register for clinical studies

Extended disability status scale

Electronic patient record

Quality of life

Multiple sclerosis

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Acknowledgements

We would like to thank all the patients, caregivers and health care specialists who volunteered their time to participate in an interview and the trial, Carola Janßen for transcribing the interviews, Fiona Brown for translating the illustrative quotes and Beatrix Münzberg, Kerstin Weiß and Monika Höveler for data collection in the quantitative study part.

COCOS-MS Trial Group

Anne Müller 1 , Fabian Hebben 1 , Kim Dillen 1 , Veronika Dunkl 1 , Yasemin Goereci 2 , Raymond Voltz 1,3,4 , Peter Löcherbach 5 , Clemens Warnke 2 , Heidrun Golla 1 , Dirk Müller 6 , Dorthe Hobus 1 , Eckhard Bonmann 7 , Franziska Schwartzkopff 8 , Gereon Nelles 9 , Gundula Palmbach 8 , Herbert Temmes 10 , Isabel Franke 1 , Judith Haas 10 , Julia Strupp 1 , Kathrin Gerbershagen 7 , Laura Becker-Peters 8 , Lothar Burghaus 11 , Martin Hellmich 12 , Martin Paus 8 , Solveig Ungeheuer 1 , Sophia Kochs 1 , Stephanie Stock 6 , Thomas Joist 13 , Volker Limmroth 14

1 Department of Palliative Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

2 Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

3 Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), University of Cologne, Cologne, Germany

4 Center for Health Services Research (ZVFK), University of Cologne, Cologne, Germany

5 German Society of Care and Case Management e.V. (DGCC), Münster, Germany

6 Institute for Health Economics and Clinical Epidemiology (IGKE), Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

7 Department of Neurology, Klinikum Köln, Cologne, Germany

8 Clinical Trials Centre Cologne (CTCC), Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

9 NeuroMed Campus, MedCampus Hohenlind, Cologne, Germany

10 German Multiple Sclerosis Society Federal Association (DMSG), Hannover, Germany

11 Department of Neurology, Heilig Geist-Krankenhaus Köln, Cologne, Germany

12 Institute of Medical Statistics and Computational Biology (IMSB), Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

13 Academic Teaching Practice, University of Cologne, Cologne, Germany

14 Department of Neurology, Klinikum Köln-Merheim, Cologne, Germany

Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Innovation Funds of the Federal Joint Committee (G-BA), grant number: 01VSF19029.

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Department of Palliative Medicine, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

Anne Müller, Fabian Hebben, Kim Dillen, Veronika Dunkl, Raymond Voltz & Heidrun Golla

Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany

Yasemin Goereci & Clemens Warnke

Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf (CIO ABCD), University of Cologne, Cologne, Germany

Raymond Voltz

Center for Health Services Research, University of Cologne, Cologne, Germany

German Society of Care and Case Management E.V. (DGCC), Münster, Germany

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• Anne Müller
• , Fabian Hebben
• , Kim Dillen
• , Veronika Dunkl
• , Yasemin Goereci
• , Raymond Voltz
• , Peter Löcherbach
• , Clemens Warnke
• , Heidrun Golla
• , Dirk Müller
• , Dorthe Hobus
• , Eckhard Bonmann
• , Franziska Schwartzkopff
• , Gereon Nelles
• , Gundula Palmbach
• , Herbert Temmes
• , Isabel Franke
• , Judith Haas
• , Julia Strupp
• , Kathrin Gerbershagen
• , Laura Becker-Peters
• , Lothar Burghaus
• , Martin Hellmich
• , Martin Paus
• , Solveig Ungeheuer
• , Sophia Kochs
• , Stephanie Stock
• , Thomas Joist
•  & Volker Limmroth

Contributions

HG, KD, CW designed the trial. HG, KD obtained ethical approvals. HG, KD developed the interview guidelines with help of the CCM (SU). AM was responsible for collecting qualitative data, developing the code system, coding, analysis of the data and writing the first draft of the manuscript, thoroughly revised and partly rewritten by HG. FH supported in collecting qualitative data, coding and analysis of the interviews. KD supported in collecting qualitative data. AM, FH, KD, VD, YG, RV, PL, CW, HG discussed and con-solidated the finalized category system. AM, FH, KD, VD, YG, RV, PL, CW, HG read and commented on the manuscript and agreed to the final version.

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Correspondence to Anne Müller .

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Clemens Warnke has received institutional support from Novartis, Alexion, Sanofi Genzyme, Janssen, Biogen, Merck and Roche. The other authors declare that they have no competing interests.

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Müller, A., Hebben, F., Dillen, K. et al. “So at least now I know how to deal with things myself, what I can do if it gets really bad again”—experiences with a long-term cross-sectoral advocacy care and case management for severe multiple sclerosis: a qualitative study. BMC Health Serv Res 24 , 453 (2024). https://doi.org/10.1186/s12913-024-10851-1

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Received : 23 November 2023

Accepted : 11 March 2024

Published : 10 April 2024

DOI : https://doi.org/10.1186/s12913-024-10851-1

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