research psychologists often work with self report data

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The Use of Self-Report Data in Psychology

• Disadvantages
• Other Data Sources

How to Create a Self-Report Study

In psychology, a self-report is any test, measure, or survey that relies on an individual's own report of their symptoms, behaviors, beliefs, or attitudes. Self-report data is gathered typically in paper-and-pencil or electronic format or sometimes through an interview.

Self-reporting is commonly used in psychological studies because it can yield valuable and diagnostic information to a researcher or a clinician.

This article explores examples of how self-report data is used in psychology. It also covers the advantages and disadvantages of this approach.

Examples of Self-Reports

To understand how self-reports are used in psychology, it can be helpful to look at some examples. Some many well-known assessments and inventories rely on self-reporting to collect data.

One of the most commonly used self-report tools is the  Minnesota Multiphasic Personality Inventory (MMPI) for personality testing . This inventory includes more than 500 questions focused on different areas, including behaviors, psychological health, interpersonal relationships, and attitudes. It is often used as a mental health assessment, but it is also used in legal cases, custody evaluations, and as a screening instrument for some careers.

The 16 Personality Factor (PF) Questionnaire

This personality inventory is often used as a diagnostic tool to help therapists plan treatment. It can be used to learn more about various individual characteristics, including empathy, openness, attitudes, attachment quality, and coping style.

Myers-Briggs Type Indicator (MBTI)

The MBTI is a popular personality measure that describes personality types in four categories: introversion or extraversion, sensing or intuiting, thinking or feeling, and judging or perceiving. A letter is taken from each category to describe a person's personality type, such as INTP or ESFJ.

Personality inventories and psychology assessments often utilize self-reporting for data collection. Examples include the MMPI, the 16PF Questionnaire, and the MBTI.

Advantages of Self-Report Data

One of the primary advantages of self-reporting is that it can be easy to obtain. It is also an important way that clinicians diagnose their patients—by asking questions. Those making the self-report are usually familiar with filling out questionnaires.

For research, it is inexpensive and can reach many more test subjects than could be analyzed by observation or other methods. It can be performed relatively quickly, so a researcher can obtain results in days or weeks rather than observing a population over the course of a longer time frame.

Self-reports can be made in private and can be anonymized to protect sensitive information and perhaps promote truthful responses.

Disadvantages of Self-Report Data

Collecting information through a self-reporting has limitations. People are often biased when they report on their own experiences. For example, many individuals are either consciously or unconsciously influenced by "social desirability." That is, they are more likely to report experiences that are considered to be socially acceptable or preferred.

Self-reports are subject to these biases and limitations:

• Honesty : Subjects may make the more socially acceptable answer rather than being truthful.
• Introspective ability : The subjects may not be able to assess themselves accurately.
• Interpretation of questions : The wording of the questions may be confusing or have different meanings to different subjects.
• Rating scales : Rating something yes or no can be too restrictive, but numerical scales also can be inexact and subject to individual inclination to give an extreme or middle response to all questions.
• Response bias : Questions are subject to all of the biases of what the previous responses were, whether they relate to recent or significant experience and other factors.
• Sampling bias : The people who complete the questionnaire are the sort of people who will complete a questionnaire. Are they representative of the population you wish to study?

Self-Report Info With Other Data

Most experts in psychological research and diagnosis suggest that self-report data should not be used alone, as it tends to be biased. Research is best done when combining self-reporting with other information, such as an individual’s behavior or physiological data.

This “multi-modal” or “multi-method” assessment provides a more global, and therefore more likely accurate, picture of the subject.

The questionnaires used in research should be checked to see if they produce consistent results over time. They also should be validated by another data method demonstrating that responses measure what they claim they measure. Questionnaires and responses should be easy to discriminate between controls and the test group.

If you are creating a self-report tool for psychology research, there are a few key steps you should follow. First, decide what type of data you want to collect. This will determine the format of your questions and the type of scale you use. 

Next, create a pool of questions that are clear and concise. The goal is to have several items that cover all the topics you wish to address. Finally, pilot your study with a small group to ensure it is valid and reliable.

When creating a self-report study, determine what information you need to collect and test the assessment with a group of individuals to determine if the instrument is reliable.

Self-reporting can be a useful tool for collecting data. The benefits of self-report data include lower costs and the ability to collect data from a large number of people. However, self-report data can also be biased and prone to errors.

Levin-Aspenson HF, Watson D. Mode of administration effects in psychopathology assessment: Analyses of gender, age, and education differences in self-rated versus interview-based depression . Psychol Assess. 2018;30(3):287-295. doi:10.1037/pas0000474

Tarescavage AM, Ben-Porath YS. Examination of the feasibility and utility of flexible and conditional administration of the Minnesota Multiphasic Personality Inventory-2-Restructured Form . Psychol Assess. 2017;29(11):1337-1348. doi:10.1037/pas0000442

Warner CH, Appenzeller GN, Grieger T, et al. Importance of anonymity to encourage honest reporting in mental health screening after combat deployment . Arch Gen Psychiatry . 2011;68(10):1065-1071. doi:10.1001/archgenpsychiatry.2011.112

Devaux M, Sassi F. Social disparities in hazardous alcohol use: Self-report bias may lead to incorrect estimates . Eur J Public Health . 2016;26(1):129-134. doi:10.1093/eurpub/ckv190

Althubaiti A. Information bias in health research: Definition, pitfalls, and adjustment methods . J Multidiscip Healthc . 2016;9:211-217. doi:10.2147/JMDH.S104807

Hopwood CJ, Good EW, Morey LC. Validity of the DSM-5 Levels of Personality Functioning Scale-Self Report . J Pers Assess. 2018;100(6):650-659. doi:10.1080/00223891.2017.1420660

By Kristalyn Salters-Pedneault, PhD  Kristalyn Salters-Pedneault, PhD, is a clinical psychologist and associate professor of psychology at Eastern Connecticut State University.

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Open Access

Peer-reviewed

Research Article

Beyond Self-Report: Tools to Compare Estimated and Real-World Smartphone Use

* E-mail: [email protected]

Affiliation Division of Psychology, Nottingham Trent University, Nottingham, United Kingdom

Affiliations Department of Psychology, Lancaster University, Lancaster, United Kingdom, School of Psychology, University of Lincoln, Lincoln, United Kingdom

Affiliation School of Psychology, University of Lincoln, Lincoln, United Kingdom

Affiliation Faculty of Business and Law, University of the West of England, Bristol, United Kingdom

• Sally Andrews, 
• David A. Ellis, 
• Heather Shaw, 
• Lukasz Piwek

• Published: October 28, 2015
• https://doi.org/10.1371/journal.pone.0139004
• Reader Comments

Psychologists typically rely on self-report data when quantifying mobile phone usage, despite little evidence of its validity. In this paper we explore the accuracy of using self-reported estimates when compared with actual smartphone use. We also include source code to process and visualise these data. We compared 23 participants’ actual smartphone use over a two-week period with self-reported estimates and the Mobile Phone Problem Use Scale. Our results indicate that estimated time spent using a smartphone may be an adequate measure of use, unless a greater resolution of data are required. Estimates concerning the number of times an individual used their phone across a typical day did not correlate with actual smartphone use. Neither estimated duration nor number of uses correlated with the Mobile Phone Problem Use Scale. We conclude that estimated smartphone use should be interpreted with caution in psychological research.

Citation: Andrews S, Ellis DA, Shaw H, Piwek L (2015) Beyond Self-Report: Tools to Compare Estimated and Real-World Smartphone Use. PLoS ONE 10(10): e0139004. https://doi.org/10.1371/journal.pone.0139004

Editor: Jakob Pietschnig, Universitat Wien, AUSTRIA

Received: June 24, 2015; Accepted: September 8, 2015; Published: October 28, 2015

Copyright: © 2015 Andrews 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 relevant data are within the paper and its Supporting Information files.

Funding: This work was funded by RIF2014-31 - Research Investment Fund (University of Lincoln) ( http://www.lincoln.ac.uk/home/research/researchsupport/researchinvestment/ ). 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

Around 2 billion people use smartphones across the globe, with over half the population in developed countries relying on them daily [ 1 ]. This ubiquity means that there is the potential for objective smartphone data to be used to address research questions in the real world [ 2 ]. Indeed, there has been a rapid increase in the number of publications examining the relationship between smartphone use, personality, cognition, health, and behaviour e.g. [ 3 – 8 ]. Despite this, smartphones themselves have yet to become a standard item in the psychologist’s research toolbox, and little is known about the validity of self-reported estimates of smartphone use.

Miller recently [ 9 ] highlighted how important it is for social science researchers to be current with new developments in smartphone research methods. Perhaps the biggest barrier to exploring the objective (actual) use of smartphone data includes developing suitable apps and the appropriate tools for processing, analysing and visualising big-data sets [ 10 ]. Whereas open source software to create Android apps is freely available for those with no programming experience [ 11 ], there remains no open source software for analysing and visualising the resulting data.

While self-report data can be collected successfully in situations where it is difficult to obtain objective data, this may not be an appropriate measure when it comes to estimating smartphone use. It remains possible that estimates are sufficient for some research questions. but much of the cognitive literature on time-perception suggests we are poor at estimating such durations [ 12 ]. Any subjective estimate is also likely to ignore rapid, yet pervasive, checking behaviours [ 13 ].

Here we propose that a simple measure—recording when the phone is in use—can provide a vast array of information about an individual's daily routine. We describe and explore different metrics for objective evaluation of smartphone data, and what this can reveal about smartphone use. We include source code for processing, visualising and analysing objective smartphone data, which can be used by those with little to no programming knowledge. As an applied example, we then explore the claim that people engage in habitual smartphone checking behaviours, by correlating self-report smartphone use estimates with actual smartphone use and standardised measures of problem mobile phone use [ 14 ]. We finally consider other research questions that could be explored with this methodology.

Participants

Twenty-nine participants were recruited (17 female, mean age = 22.52, range = 18–33). All participants owned Android smartphones and consisted of staff and students at the University of Lincoln. A priori calculations suggest this number to be adequate for finding a moderate correlation between actual and self-reported use, so we stopped collecting after this number was reached. The study conformed to the recommendations of the Declaration of Helsinki. All participants provided written and oral informed consent after being advised of the purpose of the study, and the type of data being collected. Approval for the project was obtained from the School of Psychology Research Ethics Committee at the University of Lincoln. All participants were reimbursed a small fee (£10) for their time. Two participants were excluded as they had technological problems partway through the study, while four additional participants were excluded from the analysis for not providing all self-report estimates.

Smartphone Application: We developed an Android smartphone app using Funf in a Box [ 11 ]. Apps collecting data from Android devices are generated by selecting sensors, and specifying sampling frequency. We selected the screen on/off option, resulting in a small app that records a timestamp when a use starts and ends. Data is encrypted and uploaded to a server over Wi-Fi (for more details see [ 11 ]). Our app simply recorded a timestamp when the phone became active, and a second when this interaction ended (typically screen use, although this also includes processor intensive activities including calls and playing music).

Mobile Phone Problem Use Scale (MPPUS): This questionnaire consists of 27 items, which have previously demonstrated positive correlations with self-reported mobile phone use [ 14 ]. The MPPUS remains a highly cited scale across health and psychological research [ 15 – 19 ], and has been used as an additional means of measuring mobile phone use more generally [ 6 , 20 , 21 ]) (Cronbach's alpha = .89 for standardised items in our sample).

On arrival at the lab, a smartphone application was installed on participants' smartphones. They were then sent a standardised SMS that they were asked to relay back to the experimenter, to determine the length of time taken to check a message. Time taken was recorded from the notification tone until the message had been relayed. Participants were asked to record an estimate each evening of how long they used their phone that day, for the next 14 consecutive days. We asked participants to only estimate their phone use during periods where their phone screen was switched on, as the Funf on-off sensor was advertised as measuring screen state. However, during testing, it was discovered that the on-off sensor actually measured whether the phone was in an interactive state, which included activities such as phone calls and listening to music, commonly done with the screen switched off. While we did not analyse the diary data further, it is possible that the process influenced participants’ later estimations of their phone use. When participants returned to the lab after 14 days, they were asked to estimate how much they used their phone on average each day (including calls and listening to music). This measure was used in subsequent analyses of subjective estimates. They were then asked to estimate how many times they use their phone each day (number of uses), and finally were asked to complete the MPPUS. The app was then uninstalled from their device.

Data from the app were converted into a comma separate values file using Funf processing scripts. This file was further processed using source code to calculate descriptive statistics and barcode visualisations (as shown in Fig 1 ; see S1 Appendix for source code). The scripts allow the user to explore different times of day (morning, afternoon, evening, and night), and to explore different metrics associated with checking behaviours of different durations (N.B. the source code requires Matlab 2014b or later). These can be calculated separately for each day, or across the entire duration of a study. We use descriptive statistics for the first 14 days of the study throughout. Some timestamps showed very long single use durations (i.e. > 5 hours). Another limitation to the application is that when the phone switches off, the app does not record the screen turning off. When the phone is turned on again, it also does not record the screen turning on. This results in a seemingly long ‘on’ duration, when the phone itself was actually turned off. It was therefore unclear whether long durations during the day were as a result of the phone being in use (e.g. listening to music, or watching a film), or whether the phone was turned off. As it is impossible to be sure that all long durations were because of this, we retain these data in all analyses, and use median values when calculating an average values for each day, as this is a more accurate summary of the average use length. We then use these values to calculate the mean use length for each participant. We established that occasions when this occurred overnight were the result of the phone being turned off. The included source code ( S1 Appendix ) enables the visualisation of the data for each participant across all days, or to create an ‘average heatmap’ of one day, seven days, or weekdays and weekends (not shown here).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Black areas indicate times where the phone was in use and Saturdays are indicated with a red dashed line. Weekday alarm clock times (and snoozing) are clearly evident.

https://doi.org/10.1371/journal.pone.0139004.g001

Objective Data

The mean daily number of uses and the mean length of these durations (including a median length for all the durations in a day) and a mean daily duration of phone use (total daily duration) were calculated for each participant. Participants used their phones a mean of 84.68 times each day (SD = 55.23) and spent 5.05 hours each day using their smartphone (SD = 2.73). Length of use was, unsurprisingly, highly skewed, with 55% of all uses less than 30 seconds in duration (see Fig 2 ).

This illustrates the highly skewed nature of smartphone usage.

https://doi.org/10.1371/journal.pone.0139004.g002

We classify ‘checks’ as uses up to 15 seconds in duration. To explore these behaviours more closely, we analysed the percentage of phone interactions with durations under 15 seconds. These showed three distinct periods of increased use; from 1-3s, 5-6s, and 10–11 seconds. Fig 3 shows a histogram of such checks (in 0.5 second bins). In the lab, mean time taken to unlock the phone and read a short message was 8.42s (SD = 1.53). With added distractions outside the lab, the 10-11s time bin is likely to reflect the time taken to read a short message, check the time or other notifications. We explored whether any of these durations could result from the display turning itself off, after a period of being idle. However, results indicated that these default times did not explain any spike in use (default display off times: mean LOCKED = 274.88s, SD LOCKED = 842.85s; mean UNLOCKED = 282.06s, SD UNLOCKED = 524.33s).

Three spikes of checking duration are visible.

https://doi.org/10.1371/journal.pone.0139004.g003

We also compared phone use at different times of day; night (00:00–06:00), morning (06:00–12:00), afternoon (12:00–18:00), and evening (18:00–24:00), as shown in Fig 4 . In this comparison we calculate median duration length—i.e. the median amount of time a user engaged with their phone before turning the display off—for each participant. Finally, we explored the total duration spent using the phone at each time of day. For the purposes of this analysis, phone uses that spanned two time windows (e.g. commencing in the morning and ending in the afternoon) was allocated to the time period in which it originated.

Error bars show 1 SE from the mean.

https://doi.org/10.1371/journal.pone.0139004.g004

Three one-way repeated measures ANOVAs (Time of Day; morning, afternoon, evening, night) were calculated separately for total daily duration, use length, and number of uses. Data from one participant was removed from total daily duration and median use length analyses, as they had no data from the night time period. There was a significant difference in the number of phone uses at different times of day ( F (3, 78) = 34.62, p < .001, η ρ 2 = .571). Tukey's LSD comparisons revealed more individual uses in the afternoon and evening than in the morning and at night (all p s < .001), that there were more uses in the morning than at night ( p < .001), but that there were no differences in the number of uses between afternoon and evening ( p = .083). Fig 4a shows these differences. There were no significant differences in total daily duration at different times of day ( F (3, 78) = .94, p = .414, η ρ 2 = .036; see Fig 4b , nor in median use length ( F (3, 78) = 2.33, p = .081, η ρ 2 = .082; see Fig 4c ).

Comparison of objective and subjective measures of smartphone use

We conducted paired-samples t-tests and Pearson correlations to compare actual and estimated smartphone use (see Table 1 ). For number of phone uses, there were far more actual phone uses (84.68) than were estimated (37.20; t (23) = 3.93, p < .001), and no significant correlation between the two ( r (21) = .11, p = .610) indicating that estimated number of phone uses does not reflect actual number of uses. For total daily duration there was no significant difference between actual (5.05 hours) and estimated use (4.12 hours; t (22) = 1.78, p = .086) and there was a moderate positive correlation between the two ( p = .02). This suggests that estimated duration of use may have reasonable relative validity.

https://doi.org/10.1371/journal.pone.0139004.t001

We finally compared scores on the MPPUS with objective and estimated smartphone use and checks using Pearson's correlations (see Table 1 ). None of these analyses revealed any significant relationships ( p s > .15). Ten participants scored more than 2SD greater than Bianchi & Phillips’ [ 14 ] mean, indicating problem use.

Estimated levels of smartphone use have previously been related to sleep, interpersonal relationships, driving safety, and personality [ 5 , 7 , 22 , 23 ]. Here we observe that self-reported estimates of phone use relate moderately to actual behavior in such situations. Conversely, estimated number of checks showed no clear relationship with actual uses; indeed, actual uses amounted to more than double the estimated number. It is possible that our limited sample size obscured a larger effect size. Nevertheless, we suggest that estimated use may not be sufficient if a higher resolution of data are required, but that estimates of total use are likely to be adequate for many research designs. However, for exploring checking behaviours, estimated number of uses show little reliability for measuring actual uses.

The quantity of short checking behaviours we observed are comparable with those found by Oulasvirta and colleagues [ 24 ], who collected data in 2009. Smartphone use has become much more prevalent in the intervening six years, and it would be easy to assume that smartphone use would increase accordingly. However, our data indicate that checking behaviours are no more prevalent now than they were six years ago. It is interesting to note that people have little awareness of the frequency with which they check their phone. Oulasvirta and colleagues made this claim in 2012, however this is the first paper to demonstrate that rapid mobile phone interactions are habitual [ 25 ]. While phone interactions under thirty seconds have previously been classified as 'checking behaviours', our data suggest that habitual goal-and reward-based actions are likely to be less than 15 seconds in duration when it comes to checking the time or message notifications.

In our study, the MPPUS did not correlate with any measure of phone use—actual or estimated. The MPPUS is used not only as a measure of problem phone use, but also as an additional measure of phone use more generally. To determine validity of the MPPUS for this purpose, we correlated objective phone use with MPPUS scores. This is not to say that the MPPUS lacks validity, but rather that people use smartphones for a variety of reasons [ 26 ], and that increased use does not necessitate a problem in itself [ 27 ]. It may seem reasonable to assume that those who spend a long time on their phone have problem mobile phone use. However, heavy users are not necessarily the same as problem users. While it is easy to conflate heavy use with problem use, research into smartphone use should identify heavy use and problem use independently of one another (e.g. [ 8 ]).

Examining how much people actually use their smartphone can be useful for a variety of applications. For example, all except one of our participants used their phone as an alarm clock, and most reported that they always use their phone last thing before sleeping. These usage patterns therefore provide a non-invasive indication of sleep length, which has the potential to augment sleep diary data [ 28 ]. Furthermore, while we have considered usage patterns across the day, a further extension to this analysis would be to consider how these patterns across different days of the week. This is likely to have additional social and occupational consequences [ 29 ].

Trull and Ebner-Priemer [ 9 ] and Miller [ 10 ] argue that smartphone data have a great deal to offer as a research tool in psychology, yet comparatively little research utilises objective smartphone data. Here we show that estimates of smartphone use have a place within current research, but we caution that its validity is limited and should be complimented by measurements of real behaviour. We also provide the first method to automatically sample and easily visualise the frequency of smartphone use with a simple background app. We hope that methods described in this paper will help overcome some barriers to accessing smartphone data for research in psychology and that it will form a foundation to build upon in the coming years.

Supporting Information

S1 appendix. source code for analysing smartphone use data..

Source code, example screenprobe . csv data file, and README . txt for processing, visualising and analysing smartphone use data. csv2data . m converts ScreenProbe . csv to usable data, while barcode . m allows visualisations to be generated. descriptives . m generates descriptive statistics that can be used for quantitative analysis. Source code requires Matlab version 2014b or later, but does not require any specific toolboxes.

https://doi.org/10.1371/journal.pone.0139004.s001

Author Contributions

Conceived and designed the experiments: SA HS DAE. Performed the experiments: HS. Analyzed the data: SA. Contributed reagents/materials/analysis tools: SA. Wrote the paper: SA DAE HS LP.

• 1. McIlroy T. Planet of the phones. The Economist. 2015. Available: http://www.economist.com/news/leaders/21645180-smartphone-ubiquitous-addictive-and-transformative-planet-phones
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• 26. Smith A. U.S. Smartphone Use in 2015. Pew Research Center: Internet, Science & Tech. 2015. Available: http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/

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5. Self-Report Measures

• Published: September 2007
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Chapter 5 explores self-report (SR) measures in treatment research. It discusses types of SRs, quality of SRs (reliability, validity, sensitivity and specificity in classification, utility), selecting SR measures for outcome research, and response distortions.

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The Science of Self-Report

BETHESDA, MARYLAND-The accuracy and reliability of reports of one’s own behavior and physical state are at the root of effective medical practice and valid research in health and psychology. To address this crucial element of research, the National Institutes of Health (NIH) held an informative conference here in November, “The Science of Self-report: Implications for Research & Practice,” at which more than 500 researchers and policymakers learned about many of the critical limits of “self-report” as a research tool as well as some of the latest techniques to enhance its effectiveness.

Sponsored by the Office of Behavioral and Social Science Research (OBSSR), the symposium drew participants from virtually every area of health and medicine policy, practice, and research. The issue of self-report as a primary tool in research and as an unavoidable component in health care is of central concern to medical and social science researchers and medical and psychology practitioners, and many other scientists.

Drawing on the expertise of disciplines ranging from anthropology to sociology, the conference’s 32 speakers and introducers featured 10 APS members, including the following NIH staff: Wendy Baldwin (NIH Office of Extramural Research deputy director), Norman Anderson (OBSSR director), Virginia Cain (OBSSR), Howard Kurtzman (National Institute of Mental Health), and Jaylan Turkkan (Program Co-Chair).

Value, Limits, and Improvements

”’The issue we have to consider regarding self-report data is not that it should be replaced by external measurements but that we will always need self-report about many behaviors that are simply going to be unobservable by anyone else. We’re going to need it because the interpretation of events may be important, and only the individual can provide those interpretations,” said Baldwin in the opening session initiating the two-day conference. But assessing patient compliance with medical regimens and eliciting medical histories are just two of the particularly important areas in which self-report data is routinely, and perhaps blindly, accepted as reliable in many current medical contexts.

“Consequently, the effort should be placed on improving the self-report measures, as opposed to just looking for weaknesses or how they can be replaced by external measures,” Baldwin emphasized in her comments that set the tone for the exceptionally practical conference. In fact, all speakers at the conference emphasized the invaluable nature of self-report measurements and called for a continual effort to improve their utility. “Where we have other validation, that’s great! But we have a very important job ahead of us to make sure that we can learn why self-report either works well or doesn’t, and when it works well, and when it doesn’t,” said Baldwin. Observational and experimental studies have shown that there are barriers to accuracy at every stage of the autobiographical report process- perception of the state of the self, encoding and storage of memory, understanding the question being asked, recalling the facts, and judging how and what to answer. And one intention of the conference was to systematical1y review the documented problems across several research and medical contexts.

Reporting Symptoms and Physiology

Psychologist James Pennebaker, Southern Methodist University, presented data from studies on the ability to perceive one’s own physical symptoms and other aspects of physiology such as heart rate.

“People are generally not good at this,” he finds, “but there are interesting sex differences.” In laboratory settings, men are better at perceiving their inner physiological states than are women, but the difference is largely erased when the studies are conducted in a more natural environment. This is because men and women emphasize different sources of information, when asked to define their internal states: Men rely more directly on internal bodily cues, while women rely more on situational cues. There is, of course, a lack of normal situational cues in the laboratory setting. One practical application of the skill of defining one’s internal state is that diabetics must be trained to monitor their own blood glucose levels. Having instead to resort to chemical testing for glucose is often impractical.

Reporting Pain

Pain is not a simple sensory event and is not proportional to tissue damage, reported APS Member Francis Keefe, of Duke University Medical Center’s Pain Management Program. In his discussion of the perception of pain, Keefe explained that pain is influenced by psychological, social and cultural factors, all of which act via a gating mechanism in the spinal cord, to influence the perception of pain. Also, the intensity of pain is separate from the degree of unpleasant affect associated with it, and this difference is reflected in pharmacology: While the drug fentanyl reduces the intensity of pain, diazepam reduces its unpleasantness.

Affect, in turn, modifies pain tolerance: A negative mood decreases tolerance for experimental pain in the cold pressor test, and affect at the time of pain influences the later recall of the intensity of the pain. Keefe says some pain specialists have advocated training patients with chronic pain (i.e., cancer patients) to be more emphatic and expressive in describing their pain to their doctors, in order to help ensure that adequate pain relief is prescribed. However, he says, many pain control techniques are effective because they influence affect and mood, more than they influence the intensity of the pain per se.

Reporting Data Through High- Tech “Diaries”

In his presentation on high-tech techniques to obtain self-report data, APS Member Saul Shiffman of the University of Pittsburgh’s Department of Psychology indicated that written daily or weekly diaries have not proven themselves very good for accurate recording of simple objective events like smoking. In fact, people often fail even to accurately enter many simple events into their memory, let alone document them on paper. To avoid the problem, he describes the technique of Ecological Momentary Assessment (EMA). EMA requires the subject/patient to carry a custom-designed palm-top computer, which prompts him throughout the day to answer a question (e.g., “Are you smoking right now?”). The question is posed according to the desired sampling, which can be purely random over time or contingent upon various other behaviors (like drinking coffee). By avoiding recall completely, this method can provide a very revealing picture of the subject’s pattern of behavior. It also generates great quantities of data, but the analysis of that data poses unique and controversial statistical problems, because they do not fit into the standard definitions of repeated measures.

Reporting Temporal Frequencies of Behavior From Memory

Several presenters stressed the problems posed by aspects of the mechanisms of memory encoding and recall. Norman Bradburn of the National Opinion Research Center and the University of Chicago was the first of many speakers to note that remembering is very definitely a reconstructive task. It typically suffers from several distortions, including the bundling of events, and the tendency to “telescope” events. or bring them forward in the past when remembering.

Rounding errors are frequent when self-reported time intervals approach conventional discrete units of time (e.g., an hour, a week, a month, a year). Events six or eight days ago tend to be remembered as “one week” ago, and whatever the unit of time (e.g., an hour, a week, a month, a year). Events six or eight days ago tend to be remembered as “one week” ago, and whatever the unit of time appropriate to the interval, errors are made in whole unit chunks rather than in parts of units. “We are more likely to think in terms of three weeks, than 20 days,” said Bradburn. “Many people do not enumerate events, even when we might expect the question to lead them to do so. Rather, they estimate the number of events on the basis of some rule.”

Sex Differences Reporting Temporal Facts

And, just as many have thought, women do remember dates better than men. To help the respondent reconstruct the past, the interviewer or questionnaire should ask questions that are structured according to the way in which the events are likely to be encoded. Memories are rarely linked to calendar dates but rather to notable life events (e.g., graduation from college). Roger Tourangeau of the National Opinion Research Center further analyzed the distinction between questions designed to encourage estimation and questions designed to encourage recall of individual events.

Decompositional Approach

And, Geeta Menon of New York University’s Department of Marketing has analyzed the role of the decompositional question in eliciting recall of regular versus sporadic behaviors. Should we simply ask the open-ended question “How many times did you do X last week?” Or, should we ask the same thing using a decompositional approach? For example, “How many times did you do X while driving? While sitting at home? While working? … ”

Menon’s research indicates that the open-ended question (“How many times did you do X in the last month?”) tends to encourage the subject to answer by referring to a “rule,” or an estimate of frequency. For regularly occurring behaviors this elicits accurate answers with the minimum of mental effort. For behaviors that are more sporadic, on the other hand, it is better to ask decompositional questions (i.e., to help the respondent by breaking the problem up into chunks). For irregular behaviors, a rule is less useful, and it is desirable to encourage the subject to recal1 each instance, using an enumeration strategy.

False and Forgotten Memories

Demonstrations that there can be both false negatives and false positives in memories of events that occurred long ago (or did not occur at all) have a particular relevance to the problem of sexual abuse of children. Speaking on the subject of false positives in memory, APS Fellow Elizabeth Loftus of the Psychology Department at the University of Washington presented findings—demonstrated in many experiments- that it is possible to create false memories. Such “memories” can be induced either by: (I) simply having the subject imagine a scenario vividly, and then later asking them to recount “memories” of similar events, or (2) by frankly telling a subject that a specific event happened and then reinforcing the associated “memory” by attempting to convince the subject of the authenticity of the event (e.g., by coaxing the subject with the question “Can’t you try to remember the time you got lost at the shopping mall?”).

People can import true memories from other events, thereby giving their false event memories seeming credibility, people can forget the source of a memory by wrongly attributing the memory of a fantasy to memory of a real event, and people make up completely unfounded facts, as well. The confidence one feels in the validity of one’s recall also has little correlation with its accuracy.

Linda Williams of the University of New Hampshire’s Family Research Laboratory has documented the other side of this issue, the false negative for a documented event. In these studies, children who were seen at hospitals for instances of sexual abuse were asked, many years later, to recall any such events. A substantial minority of the children, including those who had findings on physical exam that confirmed the abuse, failed to recall the instances. Interestingly, the forgetting was not correlated with the use of force or coercion by their abuser. The children were, however, more likely to forget abuse at the hands of individuals closest to them (i.e., in terms of familial relation, familiarity, or friendship).

Prolong the Pain

Psychologist Daniel Kahneman of the Woodrow Wilson School at Princeton University studies the memory of pain, as in painful medical procedures. Do we remember the quantity of pain as something like its intensity multiplied by its duration? Not at all. We remember an average of the moment of peak intensity and the pain at the end of the procedure. This has applications to colonoscopy, which is distinctly unpleasant, and for which one would like the subject to return for a repeat test every ten years. Strangely, Kahneman suggested, his research findings may mean that in order to make the long-term memory of the pain less severe, one should extend the time of the procedure, by keeping the colonoscope inserted, but not moving it. The pain is less for those last few minutes, even though we have added several minutes of diminished pain to the end of a painful experience.

Mood and Memory

APS Fellow John Kihlstrom of the Department of Psychology at Yale University took a logical and deductive approach to the problem of the influence of affect on memory. Although some experimenters have failed to find a link, he says, others have. There are some robust paradigms of mood-dependent memory. Because memory is reconstructive, not merely a readout of data, it is a cognitive task. Performance on other cognitive tasks is affected by mood, and so we should expect recall to be influenced by mood. For example, many mental patients report being abused in childhood. Is this a causal association or an example of preferential recall of mood congruent memories? What is needed to untangle this link, he says, are prospective studies.

Sensitive Topics

Nora Cate Shaeffer of the Department of Sociology at the University of Wisconsin-Madison addressed the problem of self-report in sensitive topics, such as sexual behavior or drug abuse. People will tend to present themselves in a positive light, sometimes to look good, and sometimes to “please” the researcher. The more serious an illegal behavior (e.g., the “harder” the drug), the less likely people are to report their recent use of it, while events in the distant past are less sensitive, and consequently are less likely to be concealed. Men tend to exaggerate their sexual histories, while women tend to understate them. But in any individual case, one doesn’t know how accurate a source is. Not only do people calculate the risk of revealing sensitive information (e.g., they may ask themselves “Will my spouse find out?” “Will the police find out?”), but they may even reinterpret the question, so as to allow themselves to answer evasively. (For example, a respondent may reason as follows: “Well, I did have that abortion, but I’m really not ‘the kind of person’ who would do that normally, so I’ll say “never.'” Or, “This interviewer has a hell of a nerve; it’s none of his business, ergo I don’t feel dishonest lying about this.”)

Medical Compliance

Cynthia Rand of the Johns Hopkins University Asthma and Allergy Center discussed the problem of medical noncompliance. This generates a problem for research as well as practice. If everyone in a study takes half as many pills as they say they did, the FDA-approved and officially sanctioned dosage will be twice as high as the dosage that most people reported worked best. (Yes, this suggests that to avoid an overdose of medication, it may be best to be no more compliant than the average participant in the clinical trial that determined the proper dose!) What can be done to increase the honesty of responses? For starters, a physician’s question such as “You’re taking the pills the way I prescribed, aren’t you?” is not likely to uncover any problems with compliance. It is important to discuss the patient’s experience with the regimen in more detail, to reveal possible problems or hidden issues.

Ethics in Self-report

APS Fellow Donald Bersoff of the Villanova University School of Law addressed the knotty problems arising from ethical considerations in asking sensitive questions. If a subject reports self-destructive behavior, should the researcher intervene? Does that violate confidentiality and thereby compromise the autonomy of the subject?

Bersoff implores researchers to at least address these issues before beginning research studies. For example, before undertaking a study on the attitudes of teenagers toward dangerous behaviors, researchers should consider what they will do if they find out that a teenager is contemplating suicide, or is using heroin. “Have a plan, have a policy, discuss the pros and cons of breaking confidentiality before the issue comes up,” said Bersoff. ”Too many researchers of sensitive topics don ‘t even think about what they will do, until they have in hand the information, and then they must agonize over their choices.”

Ethnic and Cultural Considerations

In many cases, the accuracy of a subject’s response depends on the understanding of the question. Spero Manson of the Department of Psychiatry at the University of Colorado’s School of Medicine has rewritten surveys specifically for Native American populations, and, with sensitivity to cross-cultural issues is able to raise the consistency of the scores very significantly. He cites one particular Indian culture in which it is considered very important never to give voice to certain negative thoughts; consequently, questions about suicidal ideation are either simply skipped by respondents at very high rates or are not answered frankly.

Efficient Screen for Depression

Ronald Kessler of Harvard Medical School’s Department of Health Care Policy has been developing a short screening test for major depression. A psychiatrist asks questions until he knows the answers he is seeking, but screening tests must be designed for administration by non-specialists, with minimal preparation. Kessler’s test, intended for screening large populations and subject to severe budget constraints, is an extreme version of this problem. The screen must not yield many false positives, it must be understandable by people of widely varying literacy and cultural backgrounds; 75% of the general population should score zero on the test, meaning that it is sensitive to only the serious cases. Interestingly, out of scores of possible questions, he has been able to narrow the survey to six very robust questions! They will be made available on the worldwide web at URLs http://www.umich.edu/~icpe/ or www.umich.edu/~ncsum/.

If You Can’t Beat Them, Join Them

Douglas Massey of the University of Pennsylvania’s Population Studies Center presented a novel approach to securing sensitive or personal data in his presentation titled “When surveys fail,” addressing the fact that many such research efforts simply demand that the researcher abandon the traditionally administered surveyor questionnaire. For a detailed study of undocumented workers from Mexico, for example, he has combined ethnography and surveys into an approach, called “ethnosurvey,” in which anthropologists get to personally know the members of a Mexican town and then travel to a town in the United States where many of the workers go to work.

By demonstrating their involvement in the community and their knowledge of its members and worker’s relatives, Massey and colleagues are able to establish trust, over a period of years, and to get answers about the laborers’ experiences, documenting answers to non-standardized questions in an extensive data recording sheet. But, of course, even ethnosurveys are plagued by the same problems of faulty recall and encoding that researchers using more standard surveys encounter.

Practical Implications for Symptoms, Illness, & Health

Linking the findings from self-report research directly to medical practice , speaker Arthur Barsky of Harvard Medical School’s Division of Psychiatry at Brigham and Women’s Hospital pointed out that there is a very poor correlation between the patient’s report of the seriousness of his symptoms, the medical findings of the presence of a pathological condition, and the patient’s utilization of health care.

Why, then, given the flawed nature of self-report of symptoms, is history-taking so important in medical practice? Several speakers reaffirmed the dogma that hi story-taking must come first. The implication would seem to be that the real skill of history-taking is in the ability to get useful information about the patient, despite the fact that his/her self-report is probably riddled with factual errors. As other speakers stated repeatedly during these two days, the respondent is always telling us something important. It just isn’t always the answer to the question we thought we were asking!

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Sometimes, we would like insight into participants' thoughts and opinions when conducting research. In these cases, we can use self-report techniques . These  data collection methods rely on the information given by participants rather than gathered through observation; this can provide researchers with insight into their participants' internal processes.

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Sometimes, we would like insight into participants' thoughts and opinions when conducting research. In these cases, we can use self-report techniques . These data collection methods rely on the information given by participants rather than gathered through observation; this can provide researchers with insight into their participants' internal processes.

• In this explanation, we will take a look at self-report techniques in psychology research.
• We will focus on the use of questionnaires and interviews in research. For each self-report technique, we will look at the advantages and disadvantages of these.
• To finish off, we will look at the evaluating points for self-report techniques, including the strengths and criticisms of self-report techniques.

Self-Report Techniques Psychology

Several self-report techniques are used in psychology , such as questionnaires and interviews, to collect data. These techniques aim to allow researchers to get more information about a phenomenon from the source directly.

Self-report techniques involve getting information directly from the source without experimenter interference . Diary entries, questionnaires and interviews are examples of self-report techniques. The questions, if asked, are usually pre-set to prevent bias issues .

As the researcher collects information from the source, these techniques are known as primary data sources.

Self-Report Questionnaire

Questionnaires typically consist of a series of questions or prompts given to participants. They can be distributed and completed in various ways, such as physical sheets of paper, online forms, or other methods. Participants typically provide written responses, but there are also question types that don't require writing, such as scales.

Thanks to modern technology, questionnaires don't have to be completed in person; this makes them relatively easy to conduct, cheap, and efficient.

There are two types of questions - open (qualitative, allowing a wide range of responses, so they are rich in detail) and closed (participants must respond in a specific way as directed, so it is easier to analyse, although more rigid).

An example of an open question would be, 'Why did you choose to unsubscribe from our mailing list?' while an example of a closed question would be, 'Tick all boxes that apply'.

Self-Report Measures: Questionnaires

There are a variety of types of closed questionnaires. Here are a few examples.

Questionnaires: Likert Scale

Likert scales provide a statement, and the participant has to tick a box showing the extent to which they agree or disagree. This is a simple way to collect qualitative data for easy analysis. This is because, rather than just asking a yes or no question, it allows for degrees of agreement.

A weakness of the Likert scale is that some people may have no opinion on a statement, and it can be difficult for researchers to interpret this information.

Questionnaires: Ranked/Rating Scale

Ranked/rating scales are questions that ask you to fill out, for example, boxes from one to ten indicating satisfaction with a product. It allows researchers to gather a lot of quantifiable information that can assist in the creation of valuable data.

For example, it would be easy to create a data representation of the popularity of a TV show using the information that a ranked scale collects.

A weakness of a ranked scale is that people's ideas of what is, for example, a 6/10 rating, may differ across participants. This will affect the validity, as results are inconsistent despite getting the same or similar answers.

Questionnaires: Multiple/Fixed C hoices

Multiple choice questions have various pre-selected answers to choose from; this allows researchers to gather quantitative data easily. The responses and their proportionality can be easily represented.

A disadvantage of this method is that the predetermined questions limit participants' responses. If participants feel that none of the responses applies to them, they typically can't respond. Some researchers can account for this by adding an option ' other' or allowing for an extended response.

But, this can be hard to represent visually, and again; there's no real way to quantitate what 'other' actually means.

Questionnaires: Sem antic Differential Scale

Semantic differential scales give participants a scale on which they can fill the boxes that correspond to their preferences or level of agreement. It is similar to the rating scale.

These methods can gather nominal, interval, or ratio data. Nominal data refers to categorised data (think 'nominal = named').

A questionnaire might collect information on the hair or eye colours of participants and this data could be represented as something like a pie chart or combined with other data to find correlations, etc.

Interval data is data that can be categorised and ranked with equal distances between each point. Interval data does not have a true zero point.

A typical example is temperature, as there is no true zero. Below zero degrees Celsius, there is minus one degree, minus two degrees, etc.

Ratio data is the same as interval data, except there is a true zero.

Some common examples are height and weight; zero is absolute - you can't be minus one centimetre tall.

Interval and ratio data allow us to gather more information compared to nominal. For example, unlike nominal data, which might explore whether people like football or not, interval data allows us to explore to what extent people do or do not like football.

Self-Report Questionnaire: Evaluation

Let us look at the strengths and weaknesses of the questionnaires.

Strengths of Questionnaires

Questionnaires are very cheap to conduct; this makes them an attractive method for researchers aiming to collect data on a large population, as they are great for statistical analysis.

Questionnaires are typically straightforward and can even be done online, which is convenient for both the researcher and the participant.

The anonymity and lack of face-to-face engagement offered by questionnaires may result in more honest answers than in interviews or studies set in social situations.

Weaknesses of Questionnaires

Questionnaires often don't allow for much detail compared to interviews so they may lack validity.

Questionnaires such as the ranked scale may lack validity; someone's 6/10 may be equal to someone else's 7/10. There is no objective standard for what a 6/10 should be, and it may be too subjective. I t does not allow the researcher to learn about the entirety of the individual thoughts and behaviour s, which also presents issues with reliability.

Social desirability bias : depending on the question, a participant may not answer honestly as it will make them look 'bad', say if they were asked about their drinking habits. People may lie to make themselves look better.

Response bias : participants may choose one answer as they progress through the questions, which affects the results. They may get bored or feel like one response has been consistently applicable to them. Hence, they rush through the rest and automatically check the chosen response off, reducing the study's validity.

Self-Report Design: Interviews

Interviews consist of discussions between interviewers and interviewees.

They can be conducted in a variety of ways, and these include:

• Face-to-face.
• Over the phone.
• Online using services such as Skype.

Interviews are unique because they allow two-way interaction between the researcher and the participant, opening more opportunities to get in-depth, individual responses and any clarifying information that may be needed.

There are three types of interviews; unstructured , semi-structured and structured . Let's take a look at each of them.

Self-Report Design: Unstructured Interviews

Unstructured interviews are conducted in a way that doesn't seem like an interview and resembles a more casual conversation. However, information is still being gathered by the researcher.

This method's casual nature allows the interviewer to take control and conduct the interview as they see fit, including changing their strategy or the subject in response to new information; this improves validity.

However, since the interview is not structured and details such as questions asked may differ, this method lacks reliability.

Self-Report Design: Semi-structured Interviews

Semi-structured interviews are the halfway point between unstructured and structured interviews. They have an informal element but also contain some structured questions like a structured interview would.

This interview style has both the advantages and disadvantages of unstructured and structured interviews, and finding the right balance can be difficult.

Self-Report Design: Structured Interviews

Structured interviews are the most formal type of interview. The interviewer asks a set of predetermined questions in order. There is no conversation, as each interview is designed to be the same so that results can easily be compared.

Because they are tightly structured and planned, structured interviews are reliable. However, they may lack validity due to their rigid nature.

Sometimes, an exam task might be to design an interview or explain what you should consider when designing an interview:

• When conducting an interview, there should always be a standardised process to avoid interviewer biases and increase reliability.
• It should have a schedule with a list of questions you want to cover. Everyone should be asked the same questions so that the answers can be compared.
• When interviewing a person, establishing some rapport beforehand is always helpful, creating a harmonious setting.
• Remember always to remind the participants of the ethical concerns, first and foremost, e.g. that they can withdraw at any time.

Self-Report Design: Evaluating Interviews

Let us take a look at the strengths and weaknesses of interviews.

Strengths of Interviews

Interviews allow researchers to collect far more information than questionnaires, often in greater detail. This increases the validity of the data they collect.

Interviews can, to an extent, be tailored to the participant. Different approaches may be needed when approaching certain subjects or certain types of participants. For example, a researcher may take a more casual approach to an interview with a younger group of participants.

Structured interviews offer standardised procedures, so the process is easy to replicate, and unstructured interviews offer flexibility. Participants are free to answer as they please, increasing the validity of the results.

Interviews can direct the participant to give responses that they may otherwise struggle to articulate. For example, when police use cognitive interviews to assess crime witnesses, they can often trigger memories in the participant that they would otherwise forget.

Weaknesses of Interviews

Interviews take much longer than questionnaires. Researchers may get more qualitative information from them than they would from questionnaires. Still, it would take very long to acquire data on large populations compared to the speed at which questionnaires can do so.

Structured interviews are pretty rigid, and if a participant has an interesting answer, the inability to explore this response may prove frustrating.

Unstructured interviews are challenging to analyse reliably sometimes, as responses can vary dramatically, so attaining consistent responses across multiple interviews is difficult. Standardised schedules help with this but do not solve the issue.

Interviews can be costly, as they typically require an interviewer to have some training or qualification. The interviewee may need to be compensated for their time and travel costs.

Social desirability bias : if a question is complex or sensitive, participants may not want to answer honestly, affecting the results' validity. Building rapport may help alleviate this issue, but it may not solve it.

Self-Report Examples: Real-Life Application

Some real-life applications in research of self-report techniques are as follows:

Bandura et al. (1961) used a questionnaire to record the aggression levels of nursery school children according to their teachers.

The Patient Health Questionnaire (PHQ) is a Likert scale that has been used and updated since the 1990s.

Freud (1909) conducted unstructured interviews in his research on little Hans.

Brown (1986) used semi-structured interviews in his work to ask patients about their life experiences, any symptoms of depression , their view of themselves, and what support systems they had.

These examples highlight the utility of self-report techniques in research!

Advantages of Self-Report Measures

Self-report methods allow researchers to see into the minds of their participants. This gives them more information than observation alone and can contextualise data collected from experiments, making the data gathered more valid.

Self-report methods are pretty inexpensive and don't require much time or effort. Due to this, it is easy to gather data with interviews and questionnaires from a large sample size, making it easier to generalise results.

Self-report encompasses many different methods; this makes them versatile, to the benefit of researchers who can use various methods when needed.

Limitation of Self-Report Techniques in Psychology

Methods such as questionnaires can easily be misunderstood, and participants may also give inaccurate answers, leading to invalid data.

In self-report methods such as structured interviews, participants may feel uncomfortable or nervous, altering their responses. In unstructured interviews, participants may like or feel intimidated by the interviewer, leading to acquiescence bias. This happens when participants agree with the statements more than they normally would.

In the case of more extensive questionnaires, especially if conducted over the internet, it can be challenging to ensure participants' demographic information and contact them for follow-ups if needed.

Self-Report Techniques - Key takeaways

• Self-report techniques are data techniques aimed at allowing researchers to get more information about a phenomenon directly from the source.
• The two main methods are questionnaires and interviews.
• Questionnaires can have open or closed questions.
• There are many types of closed questions. Likert scales, ranked scales, semantic differential scales and multiple-choice questionnaires are all used.
• Interviews can be unstructured, semi-structured or structured.

Frequently Asked Questions about Self-Report Techniques

--> what is a limitation of self-report technique.

Self-report methods such as questionnaires are open to social desirability bias. This means that participants may not answer honestly, so they do not appear 'bad'.

--> What is a self report technique in psychology?

Self-report techniques involve getting information directly from the source without experimenter interference. Diary entries, questionnaires and interviews are examples of self-report methods. The questions, if asked, are usually pre-set to prevent bias issues. 

--> Why do psychologists use self-report techniques?

Psychologists use self-report techniques because they allow them to gather more information than just observations. It gives an insight into the personal thoughts and feelings of the participants.

--> What is an example of a self-report measure?

An example of a self-report measure would be using a questionnaire to gather participants' opinions.

--> Are self reports qualitative or quantitative?

Self-reports can be qualitative or quantitative, depending n the method used. Qualitative data can be obtained from open-ended questions and interviews, and quantitative data can be obtained from questionnaires with closed questions.

Test your knowledge with multiple choice flashcards

Self-report scales involve the researcher both asking direct questions to a person and completing the questions themselves. True or false? 

Ranking questions are where the responder must answer whether they: Strongly agree, Agree, Unsure, Disagree, Strongly disagree. True or false? 

Which is not a type of self-report measure? 

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What are self-report techniques?

Self-report techniques are methods of data collection that take information given by participants.

Why are self-report techniques useful?

Self-report techniques allow researchers to contextualise information gathered through observation, and allow them to gather data that may not be available from experiments, such as the emotions and opinions of participants.

What are the two main types of self-report techniques?

The two main types of self-report techniques are interviews and questionnaires

What is the difference between an open and closed question?

Open questions are open-ended and allow a variety of responses, while closed questions allow only one response or a number of predetermined responses.

What are the different types of closed question questionnaires?

Likert scales, ranked scales, semantic difference scales and multiple choice questionnaires are used by researchers.

What are the different types of interviews?

There are unstructured, semi-structured and structured interviews.

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