a to z of research

The Reinvention Centre

Research skills a to z.

This A to Z of Research Skills has been compiled to help undergraduate researchers understand some of the key concepts they need to be familiar with when producing research. The A to Z is very much 'work in progress' and any suggested amendments or additions would be very welcome from students or from staff. Please email Pete Smith at prsmith at brookes dot ac dot uk

Analysis: examining data to detect patterns and regularities

Analysis is the process of examining a set of data in order to detect any patterns or regularities that will address the research objective . Even the smallest set of data will consist of a great deal of unsorted information and analysis is sometimes referred to as ‘data reduction’ or ‘data editing’ in that it picks out the elements that are important to the topic. For example, in a study of recreation a host of different kinds of data may be collected to try and explain the reasons why some students take more exercise than others. If a set of quantitative data has been collected, then the analysis will involve the techniques of descriptive statistics and correlation . If a set of qualitative data has been collected, then the analysis should pick out key words from the text. These key words are referred to as codes and you should then examine the codes and see whether they can be sensibly grouped into categories that can be a given a more general name. It is sometimes appropriate to transform qualitative data in order to make use of quantitative techniques and this is known as content analysis. Whatever kind of data you are dealing with there will be a number of subjective decisions to be made about how the analysis is carried out and it is therefore important to think about its reliability . The direction of your analysis should always be guided by the conceptual framework and the results of the analysis can then be used to then modify that framework.

Baseline: a yardstick against which the results of an analysis can be gauged

A baseline is a set of information against which you can gauge the result from an analysis of data. For example, a study on recreation at a university may have established the level of participation in sport by students by calculating, for example, some descriptive statistics . It would be useful to know whether this figure was high or low and so you should attempt to compare it with other universities which may have carried out their own research. The figures from these other universities would be called baseline figures and it is, of course, important that the figures have been collected in the same kind of way. A research project should try to collect as many different kinds of baseline figures as possible and another kind of baseline would be the recreation levels at the same university in previous years. It would also be useful to establish baseline data for other groups in the population besides students and likewise against baselines consisting of regional and national averages for levels of participation in sport.

Case: an individual for which data is collected

A case is an individual for which data is collected on some of their characteristics or attributes. A case will always be one of a set of several cases so that, for example, in a study of student recreation a set of several students will be selected and data would be collected on some of their characteristics to do with recreational behaviour. A case will often be a human individual but may also be an object, a place, or an organisation so the recreational study could collect data on, for instance, a set of sports centres, a set of towns or a set of universities. If quantitative data is collected, then it is likely that a relatively large sample of cases will be chosen and the characteristics of the cases will be known as variables . If qualitative data is collected, then a much smaller sample of cases will probably be used. Whatever the type of data it is important that the cases are given a clear definition in order that it is quite clear which ones should be included. For example, in the study of student recreation the researcher needs to know whether to include part-time students as well as full-time students, graduate students as well as undergraduate students.

Case study: an in-depth investigation using a range of data

A case study involves carrying out an in-depth piece of research on a small number of cases and sometimes only one case. For example, a study of the level of participation in sport may have identified a particular group of students who have found difficulties in making use of university facilities. A research project could therefore set out to find the reasons for this by carrying out an in-depth examination of a small sample of the students involved. A case study will use every available kind of data and is sometimes called a mixed method approach because it will invariably include both quantitative data and qualitative data . The collective value of several kinds of data will be increased if they can somehow be linked together and this is sometimes called triangulation. For example, a questionnaire could be conducted and analysed and used to help generate the topics to be covered in some in-depth interviews.

Causality: a process in which X influences Y

Causality refers to the process where a cause leads to an effect. If quantitative data is being used, then it is said that a causal variable (for short, X) leads to an effect variable (for short, Y). For example, it is likely that the amount of study you do will influence the mark that you achieve in a module. Here the amount of study is the X variable and your module mark is the Y variable. It is important to correctly set out the logic of this relationship and label the variables accordingly so that X is followed by Y, as in the alphabet. To decide which is X and which is Y, think of which variable occurs first and which variable occurs second. Which is X and which is Y depends entirely on the two variables you are thinking about. So, for example, level of motivation may influence amount of study so here amount of study is now the Y with level of motivation the X. Several variables can be linked together to show a number of causal relationships and it is useful to show all of these in a flowchart, which is sometimes called a causal system. This flowchart or causal system can function as a conceptual framework where the variable at the conclusion features in the objective of the project. When some data has been collected an analysis can be carried out to establish whether there is any evidence to support the hypothesis that there is a relationship between an X and a Y. The technique of analysis used is correlation but great care should be taken to ensure that a strong correlation truly indicates a strong causal relationship.

Concept: an abstract notion which is an important part of a topic

A concept is an abstract and wide-ranging idea or notion which is considered to be a key element of the topic being researched. A topic will invariably contain several concepts and they will need to be organised into some kind of conceptual framework in order to supply the project with a sense of direction. In a study of students at university a concept of interest might be ‘the student experience'. Other examples of concepts are 'quality of life’, 'deprivation' and ‘accessibility’. A concept is often too vague and abstract to be observed or measured and must therefore be defined it is going to be used in research. When a concept is defined it is often found to have several dimensions or components so that, for instance, the student experience may have the dimensions of academic studies, residential accommodation and leisure. These components convey rather more about what might be meant by the student experience but they will themselves need further definition to produce variables for which quantitative data can be collected. In the collection of qualitative data not all researchers would subscribe to the advance definition of concepts and would instead use the data collection itself to develop a sense of how concepts are perceived. For example, in the investigation of the student experience the researcher would ask the students themselves to talk about how this experience is defined.

Conceptual framework: a structure for the subject matter in a research topic

A conceptual framework (or theoretical framework or, simply, a theory) is a structure for systematically organising all of the concepts in the topic that is being investigated. Importantly, the framework will need to include the concept that is the focus of the research. So, for example, if the objective of your project is to explain amounts of student recreation, then this should feature in the framework together with the concepts which might influence amounts of recreation. A conceptual framework would here show how different concepts are linked together in a process of causality and should be based on a thorough literature review to represent current understanding of a particular subject area. The framework should be used to guide the collection of data and the subsequent analysis , which can then be used to modify the framework. Eventually the framework can help to place the results in a context when writing up the final conclusion .

Conclusion: drawing together the results of a project

A conclusion will always be the final chapter in a research report and will draw together everything that the study has achieved. Importantly, the chapter of conclusions should only cover material that has already been presented in greater detail in an earlier part of the report and should not introduce new ideas. The conclusion should address the original objective and should be placed in a context by making use of the conceptual framework . You should appraise the internal validity and the external validity of your findings and draw attention to the flaws that you are aware of but, at the same time, do not overlook the lessons you can pass on about how to do research. Wherever possible you should make suggestions on how any shortcomings could have been addressed and indicate further research which could be carried out. More often than not, the conclusions will be one of the first chapters that a reader will visit and you should strive to try and create a good first impression. However, the conclusions will be almost the last chapter you write so be sure to leave yourself time before the deadline to find the right words.

Content analysis

See Analysis

Correlation: analysing the statistical connection between two variables

Correlation is a technique of analysis which is applied to a set of quantitative data to establish whether the values of two variables display a statistical connection. For example, a set of student data may contain the number of hours a week spent on academic study and the percentage module marks. If these two variables showed a correlation close to 1.0, then the variables would be very be strongly correlated. In other words, students who studied a lot would also have high marks and students who studied little would have low marks. If, on the other hand, the correlation is close to 0.0, then the variables would not be correlated and it would not be possible to describe any connection between study and marks. The interpretation of a correlation must be mindful of the process of causality and which variable is the cause X and which is the effect Y. In addition, any correlation between X and Y needs to be interpreted by writing a sentence which logically explains the connection. For example, by spending more time on academic study X a student will gain a better grasp of the subject and produce a submission which gains a high module mark Y.

Descriptive statistics: numerical calculations such as averages and percentages

Descriptive statistics are calculated in the analysis of a set of quantitative data . An example of a descriptive statistic is the average which could be calculated for a variable such as the age of a group of students. You would simply add up the ages of all the students and then divide by the number of students in the group. The kind of descriptive statistic that is appropriate depends on the scale of measurement of the variable. An average can be used for age because the variable is on a numerical scale. In contrast, gender is a category and it would make no sense to calculate the average of males and females. Instead you should count up how many students are male and female and then calculate the percentages of each. Descriptive statistics are calculated for one variable at a time and are produced in order to create a profile of the cases in a set of data. Thus, the age and gender of students would be only two of several variables for which descriptive statistics would be calculated in order to understand what kinds of students are in the group. Once descriptive statistics have been calculated it is usual to go on and use correlation to find out whether there are any connections between pairs of variables.

Ethics: principles to avoid exposing human participants to the risk of harm

Attention to ethics is imperative in the collection of either quantitative data or qualitative data where human participants are directly or indirectly involved. For example, in a study of university recreation you might recruit some students to complete a questionnaire or to take part in an interview. Alternatively, you might invite them to undergo a trial using some gym equipment or carry out some observations in a sports centre. In all such kinds of data collection you must be mindful of the twin ethical principles of DO GOOD and DO NO HARM. The first of these principles requires that research needs to be conducted for some useful purpose and by a trained researcher. The second principle requires that the project should avoid subjecting participants to coercion, intrusion, deception, loss of confidentiality and anonymity, and physical or psychological harm. The ethical requirement is that any harm that participants may be exposed to can be justified by the good that may come from the research. In other words, the ethics of data collection is not judged against absolute standards but by examining whether any potential harm is warranted by the potential good. For example, an experiment may not be justified to research a new cosmetic but it might be justified to research a treatment for a serious illness. The ethics of data collection is assessed through a process of ethics review.

External validity: the extent to which generalisations can be supported

The external validity of a research study is the level of support which is provided for claims that the results can be generalised beyond the cases that have actually been investigated. For example, if a study of student recreation establishes that males spend longer on physical exercise than females, then this finding might be extended to students on other campuses, or to students in the future, or to other kinds of people who live in the town. Generalisations (or inferences) of this kind are an important feature of academic research but they can only be made if it can be demonstrated that the sample of cases that has been researched is representative of further cases that have not been researched. Thus, to generalise the findings on student recreation, it is necessary that the students where the study has been carried out are genuinely typical of students in other universities. Assessing external validity only makes logical sense if you have first assessed internal validity . Even if it is difficult to establish the external validity of your work you should always try to discuss this aspect of your research in the final conclusion .

See Objective

See Variable

See External validity

See Qualitative data

Internal validity: the extent to which evidence supports a finding

Internal validity is the extent to which the evidence in a research report supports the findings that have been produced for the cases examined or, in other words, the cases that are internal to the research. For example, in a study of student recreation you may conclude that students with more than the average number of modules in their programme are likely to spend fewer hours on physical exercise. Establishing the internal validity of this finding involves asking some searching questions about the way in the finding was arrived at. Did the conceptual framework allow all of the possible explanations for levels of exercise to be tested? Were the definitions of the concepts and variables appropriate and consistent with established theory and practice? Was the quantitative data and/or qualitative data the appropriate way of recording student behaviour? Were the techniques of analysis appropriate and does their interpretation deliver a convincing result? If the internal validity of a project is sound, then the researcher should go on to examine its external validity .

Key informant: an individual who can supply extensive information

A key informant is an individual who through their job or their position in a community can supply a good deal of information or data that would otherwise have to be gained from several different sources. For example, in a study of student recreation it may be useful to contact the person who manages the university sports centre as they would clearly be able to tell you a lot about the centre and the people who use it. It is likely that a key informant should be accessed at an early stage in a project to help guide the collection of both quantitative data and qualitative data . However, you should make good use of the key informant’s time and should certainly have worked up a conceptual framework and examined any published data that is available on your topic.

Knowledge: an understanding of how the real world works

Knowledge consists of an understanding of how the real world works and the production of new knowledge is the over-arching objective of research. It is important to realise that facts or data alone do not constitute knowledge and the expression ‘theoretical knowledge’ is sometimes used to emphasise that research needs to establish that facts conform to some kind of theoretical pattern. A research project therefore needs to examine data in the light of a conceptual framework (or theoretical framework or theory) and such frameworks often consist of some process of causality explaining how things happen. For example, you may have accurate data on the levels of use of the university sports centre but knowledge only starts to be established when there is an understanding of the reasons for the different levels. It should be emphasised that this account of knowledge is very much derived from a scientific view research, which invariably makes use of quantitative data . Some social researchers have resisted this approach with the counter that human phenomena can only be explained with the use of qualitative data . The most extreme point of view is that each human phenomenon is unique and therefore cannot be made the subject of a general theory. For example, every individual student may have their own personal reasons for how much physical exercise they do. Furthermore, knowledge itself may be seen differently by different people. A student may feel that they do not have enough time for physical exercise because of the amount of work they have been set. A tutor may take the view that the student does not find the time for exercise because of poor time management skills. Thus, knowledge can be regarded as not what is true but, rather, what an individual believes to be true. These kinds of issues are very much the subject of epistemology, which is the study of what are the appropriate ways of establishing knowledge.

Mixed method

See Case study

Objective: a statement of the purpose of the research

An objective sets out what a research project is intending to find out in terms of establishing some new knowledge about the topic you are interested in. The most useful knowledge that can be established by research is to find the reasons that have given rise to a particular problem or concern or issue. The objective of research is probably best expressed as a question so that in a study of student recreation, for example, the objective question might be ‘What are the reasons for the amount of physical exercise taken by students?’ The reasons for students’ levels of physical exercise would combine for a process of causality and this process which would be developed as the conceptual framework to guide the collection of quantitative data and qualitative data . In this way the objective is the starting point of a research project and, if neglected, the study will lack focus and direction. It is important that an objective is seen to be worth pursuing and supported by authoritative sources, most obviously by previous research that has drawn attention to gaps in knowledge. In the conclusion of a project it is crucial that the objective is explicitly addressed together with indications of shortcomings in the work that require further research.

Observation

See Quantitative data

Qualitative data: empirical information recorded as text or images

Qualitative data is usually in the form of written text but, more recently, images have been recognised as a useful way of recording information. Qualitative data contrasts with quantitative data in that it is usually collected for a small sample of cases for an in-depth examination. For example, in a study on student recreation you might speak to someone so that they can describe how and why they take part in physical exercise. The written text of this description would be keyed in to an electronic file for an analysis that picks out the essentials of what the person said. In small-scale research projects, the most common method of collecting qualitative data is through an interview, which importantly should allow the interviewee to supply information in their own words. In advance of the interview, an interview guide should be prepared consisting of a list of topics to be covered and the session should be recorded electronically and/or by taking notes. Interviews require a good deal of skill and tact on the part of the researcher and it is important that the work is subjected to a process of ethics review. More experienced researchers may use alternative methods of qualitative data collection such as focus group discussions and participant observation. When a range of different types of data are collected in the same project, it is said that a case study approach is being taken.

Quantitative data: empirical information recorded as numbers

Quantitative data (also called statistical data or numerical data) contains numerical values that are recorded on a scale of measurement for a set of variables . Quantitative data contrasts with qualitative data in that it is usually collected for a large sample of cases for a breadth of mainly factual information. For example, a study on student recreation might collect some data on variables such as amount of exercise, amount of academic study, age, and attitude to exercise. This data would be assembled in an electronic file and examined using a technique of analysis such as descriptive statistics or correlation . Quantitative data can be collected from a published source, especially on the internet, as government or similar agencies routinely collect a vast amount of such data. These sources are extremely valuable in providing data over long periods of time and/or for large and numerous geographical areas. Published sources should always be thoroughly reviewed before you consider collecting your own data as this is time-consuming and requires you to consider questions of ethics . However, generating your own data may be necessary where you want information about particular small groups or about a narrow topic of interest. In small-scale research projects, the most common methods of collecting quantitative data is through a questionnaire or by direct observation. For example, you could create a questionnaire to ask students about their recreation and you could observe the the use of a sports centre by carrying out a pedestrian count of visitors to the centre. For both of these sorts of exercise a survey form should be created to record the information and a pilot survey should be carried out to check for problems. In particular, it is important that a survey is designed to maximise the response rate .

Questionnaire

Random sampling, reliability: the degree to which a source of evidence is consistent.

If a method of processing data is repeated a second time and produces a different result from the first time, then the method is said to be unreliable. Reliability is an issue in both the collection of quantitative data and qualitative data and in the analysis of data. For example, an interviewer might ask an interviewee how often they visit a sports centre. If a second and third interviewer asked the same question and got a quite different reply, then the information could not be relied on and this might be for a number of reasons. Perhaps the interviewee tends to be inconsistent or perhaps the interviewers presented the question in quite different ways. Likewise, if the text of an interview is summarised by several researchers, then they would need to produce similar results for the method of analysis to be reliable. It is, of course, rare for either data collection or analysis to be conducted more than once and it is therefore the responsibility of the researcher to be mindful of the possibility of unreliability and to report any concerns in their conclusions . If any part of the method of research is not reliable, then this will compromise the internal validity of the study.

Response rate: the proportion of respondents who take part in a survey

A response rate is the proportion of people who take part in a method of quantitative data collection with those who do not take part being referred to as non-respondents. For example, if a questionnaire survey selects a sample of 120 students and 80 students reply (so there are 40 non-respondents), then the response rate would be 80 divided by 120 which is 0.75. This figure is usually multiplied by 100 to be expressed as a percentage so here it would be 75%. The problem with non-response is that the data will inevitably be biased so that the analysis will be distorted. It is therefore important to try and maximise the response rate by encouraging people to reply but without coercing them in ways that would contravene principles of ethics . A good response rate is more likely to be achieved if respondents are fully informed about the survey and empathise with the subject of the study. It is important that any bias from non-response is reported in the conclusion .

Sample: a set of individuals selected from a larger group

A sample is a set of cases which is selected from a much larger set of cases, often known as a population. For example, in a study of recreation you may select a sample of students from the much larger group of all the students on the university campus. The advantage of a sample is that it reduces the amount of data to be collected but then presents the problem of establishing whether the results of the analysis can be generalised to the complete population. If quantitative data is being collected, then it is usual to use random sampling to pick out a reasonably large sample of 30 or more cases. A large random sample should ensure it is representative of the population but the practical difficulty is that a low response rate then introduces bias. If qualitative data is being collected, then a much smaller sample is selected by using non-random sampling to hand-pick particular cases of interest.

Scale of measurement: the range of values for which a variable is recorded

A scale of measurement shows the range of values which a variable can display for the cases that feature in a set of quantitative data . There are three main types of scale: a numerical scale, an ordinal scale and a nominal scale. A numerical scale (or interval/ratio scale) is the kind of scale we use to measure everyday things using a ruler or a tape measure. In a study on recreation, the kinds of variables that we might record on a numerical scale could be the number of hours spent on physical exercise and the number of kilometres someone lives from a sports centre. For a variable on a numerical scale it makes sense to carry out analysis which involves simple arithmetic and allows the calculation of descriptive statistics such as an average. The other two scales of measurement, the ordinal scale and the nominal scale, both contain values that are categories. On an ordinal scale the categories can be ranked or ordered so, for example, someone’s attitude to physical exercise could be placed on an ordinal scale by asking them to select a category such as very interested, interested, neutral, disinterested, and very disinterested. This five-point opinion scale is quite common in research and is sometimes called a Likert scale. The scale allows the researcher to identify whether one person’s opinion of exercise is higher or lower than another person’s but does not allow the calculation of the amount by which their opinions differ. Thus arithmetic cannot be used on the values and the calculation of descriptive statistics is not appropriate. A nominal scale is where a case can be placed into one of a number of categories that cannot be rank ordered. If you asked someone to select their favourite sports centre, you could offer a list of centres from which they could choose. Each centre is here a category but it would not be possible to say that one person’s favourite centre was higher or lower than another. When values are recorded on a category scale (either ordinal or nominal) the analysis involves calculating the number and percentage of cases who selected each category. A variable is often derived by defining a broader concept and the definition should usually imply the scale of measurement.

Theoretical framework

See Conceptual framework

Triangulation

See Internal validity and External validity

Variable: a characteristic which differs from one individual to the next

A variable features in a set of quantitative data and is a characteristic of a set of cases which is different from one case to another. For example, in a study of recreation it may be found that different students spend different amounts of time on physical recreation and so physical recreation is a variable. A variable is recorded on a scale of measurement and is often derived by defining a broader concept . If a concept produces several variables, then these variables are often referred to as indicators. The analysis of a set of data will usually begin with the calculation of descriptive statistics for each of the variables. Further analysis would involve the correlation of different pairs of variables and this requires a distinction to be made between the independent variable (or X-variable) and the dependent variable (or Y-variable).

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Organizing Your Social Sciences Research Paper

Glossary of research terms.

• Purpose of Guide
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• Independent and Dependent Variables
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
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• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
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This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.

• Acculturation -- refers to the process of adapting to another culture, particularly in reference to blending in with the majority population [e.g., an immigrant adopting American customs]. However, acculturation also implies that both cultures add something to one another, but still remain distinct groups unto themselves.
• Accuracy -- a term used in survey research to refer to the match between the target population and the sample.
• Affective Measures -- procedures or devices used to obtain quantified descriptions of an individual's feelings, emotional states, or dispositions.
• Aggregate -- a total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc. that comprise the county. As a verb, it refers to total data from smaller units into a large unit.
• Anonymity -- a research condition in which no one, including the researcher, knows the identities of research participants.
• Baseline -- a control measurement carried out before an experimental treatment.
• Behaviorism -- school of psychological thought concerned with the observable, tangible, objective facts of behavior, rather than with subjective phenomena such as thoughts, emotions, or impulses. Contemporary behaviorism also emphasizes the study of mental states such as feelings and fantasies to the extent that they can be directly observed and measured.
• Beliefs -- ideas, doctrines, tenets, etc. that are accepted as true on grounds which are not immediately susceptible to rigorous proof.
• Benchmarking -- systematically measuring and comparing the operations and outcomes of organizations, systems, processes, etc., against agreed upon "best-in-class" frames of reference.
• Bias -- a loss of balance and accuracy in the use of research methods. It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
• Case Study -- the collection and presentation of detailed information about a particular participant or small group, frequently including data derived from the subjects themselves.
• Causal Hypothesis -- a statement hypothesizing that the independent variable affects the dependent variable in some way.
• Causal Relationship -- the relationship established that shows that an independent variable, and nothing else, causes a change in a dependent variable. It also establishes how much of a change is shown in the dependent variable.
• Causality -- the relation between cause and effect.
• Central Tendency -- any way of describing or characterizing typical, average, or common values in some distribution.
• Chi-square Analysis -- a common non-parametric statistical test which compares an expected proportion or ratio to an actual proportion or ratio.
• Claim -- a statement, similar to a hypothesis, which is made in response to the research question and that is affirmed with evidence based on research.
• Classification -- ordering of related phenomena into categories, groups, or systems according to characteristics or attributes.
• Cluster Analysis -- a method of statistical analysis where data that share a common trait are grouped together. The data is collected in a way that allows the data collector to group data according to certain characteristics.
• Cohort Analysis -- group by group analytic treatment of individuals having a statistical factor in common to each group. Group members share a particular characteristic [e.g., born in a given year] or a common experience [e.g., entering a college at a given time].
• Confidentiality -- a research condition in which no one except the researcher(s) knows the identities of the participants in a study. It refers to the treatment of information that a participant has disclosed to the researcher in a relationship of trust and with the expectation that it will not be revealed to others in ways that violate the original consent agreement, unless permission is granted by the participant.
• Confirmability Objectivity -- the findings of the study could be confirmed by another person conducting the same study.
• Construct -- refers to any of the following: something that exists theoretically but is not directly observable; a concept developed [constructed] for describing relations among phenomena or for other research purposes; or, a theoretical definition in which concepts are defined in terms of other concepts. For example, intelligence cannot be directly observed or measured; it is a construct.
• Construct Validity -- seeks an agreement between a theoretical concept and a specific measuring device, such as observation.
• Constructivism -- the idea that reality is socially constructed. It is the view that reality cannot be understood outside of the way humans interact and that the idea that knowledge is constructed, not discovered. Constructivists believe that learning is more active and self-directed than either behaviorism or cognitive theory would postulate.
• Content Analysis -- the systematic, objective, and quantitative description of the manifest or latent content of print or nonprint communications.
• Context Sensitivity -- awareness by a qualitative researcher of factors such as values and beliefs that influence cultural behaviors.
• Control Group -- the group in an experimental design that receives either no treatment or a different treatment from the experimental group. This group can thus be compared to the experimental group.
• Controlled Experiment -- an experimental design with two or more randomly selected groups [an experimental group and control group] in which the researcher controls or introduces the independent variable and measures the dependent variable at least two times [pre- and post-test measurements].
• Correlation -- a common statistical analysis, usually abbreviated as r, that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. Also, a non-cause and effect relationship between two variables.
• Covariate -- a product of the correlation of two related variables times their standard deviations. Used in true experiments to measure the difference of treatment between them.
• Credibility -- a researcher's ability to demonstrate that the object of a study is accurately identified and described based on the way in which the study was conducted.
• Critical Theory -- an evaluative approach to social science research, associated with Germany's neo-Marxist “Frankfurt School,” that aims to criticize as well as analyze society, opposing the political orthodoxy of modern communism. Its goal is to promote human emancipatory forces and to expose ideas and systems that impede them.
• Data -- factual information [as measurements or statistics] used as a basis for reasoning, discussion, or calculation.
• Data Mining -- the process of analyzing data from different perspectives and summarizing it into useful information, often to discover patterns and/or systematic relationships among variables.
• Data Quality -- this is the degree to which the collected data [results of measurement or observation] meet the standards of quality to be considered valid [trustworthy] and  reliable [dependable].
• Deductive -- a form of reasoning in which conclusions are formulated about particulars from general or universal premises.
• Dependability -- being able to account for changes in the design of the study and the changing conditions surrounding what was studied.
• Dependent Variable -- a variable that varies due, at least in part, to the impact of the independent variable. In other words, its value “depends” on the value of the independent variable. For example, in the variables “gender” and “academic major,” academic major is the dependent variable, meaning that your major cannot determine whether you are male or female, but your gender might indirectly lead you to favor one major over another.
• Deviation -- the distance between the mean and a particular data point in a given distribution.
• Discourse Community -- a community of scholars and researchers in a given field who respond to and communicate to each other through published articles in the community's journals and presentations at conventions. All members of the discourse community adhere to certain conventions for the presentation of their theories and research.
• Discrete Variable -- a variable that is measured solely in whole units, such as, gender and number of siblings.
• Distribution -- the range of values of a particular variable.
• Effect Size -- the amount of change in a dependent variable that can be attributed to manipulations of the independent variable. A large effect size exists when the value of the dependent variable is strongly influenced by the independent variable. It is the mean difference on a variable between experimental and control groups divided by the standard deviation on that variable of the pooled groups or of the control group alone.
• Emancipatory Research -- research is conducted on and with people from marginalized groups or communities. It is led by a researcher or research team who is either an indigenous or external insider; is interpreted within intellectual frameworks of that group; and, is conducted largely for the purpose of empowering members of that community and improving services for them. It also engages members of the community as co-constructors or validators of knowledge.
• Empirical Research -- the process of developing systematized knowledge gained from observations that are formulated to support insights and generalizations about the phenomena being researched.
• Epistemology -- concerns knowledge construction; asks what constitutes knowledge and how knowledge is validated.
• Ethnography -- method to study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through immersion into the culture or group. Research is completed through various methods but, since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research.
• Expectancy Effect -- any unconscious or conscious cues that convey to the participant in a study how the researcher wants them to respond. Expecting someone to behave in a particular way has been shown to promote the expected behavior. Expectancy effects can be minimized by using standardized interactions with subjects, automated data-gathering methods, and double blind protocols.
• External Validity -- the extent to which the results of a study are generalizable or transferable.
• Factor Analysis -- a statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.
• Field Studies -- academic or other investigative studies undertaken in a natural setting, rather than in laboratories, classrooms, or other structured environments.
• Focus Groups -- small, roundtable discussion groups charged with examining specific topics or problems, including possible options or solutions. Focus groups usually consist of 4-12 participants, guided by moderators to keep the discussion flowing and to collect and report the results.
• Framework -- the structure and support that may be used as both the launching point and the on-going guidelines for investigating a research problem.
• Generalizability -- the extent to which research findings and conclusions conducted on a specific study to groups or situations can be applied to the population at large.
• Grey Literature -- research produced by organizations outside of commercial and academic publishing that publish materials, such as, working papers, research reports, and briefing papers.
• Grounded Theory -- practice of developing other theories that emerge from observing a group. Theories are grounded in the group's observable experiences, but researchers add their own insight into why those experiences exist.
• Group Behavior -- behaviors of a group as a whole, as well as the behavior of an individual as influenced by his or her membership in a group.
• Hypothesis -- a tentative explanation based on theory to predict a causal relationship between variables.
• Independent Variable -- the conditions of an experiment that are systematically manipulated by the researcher. A variable that is not impacted by the dependent variable, and that itself impacts the dependent variable. In the earlier example of "gender" and "academic major," (see Dependent Variable) gender is the independent variable.
• Individualism -- a theory or policy having primary regard for the liberty, rights, or independent actions of individuals.
• Inductive -- a form of reasoning in which a generalized conclusion is formulated from particular instances.
• Inductive Analysis -- a form of analysis based on inductive reasoning; a researcher using inductive analysis starts with answers, but formulates questions throughout the research process.
• Insiderness -- a concept in qualitative research that refers to the degree to which a researcher has access to and an understanding of persons, places, or things within a group or community based on being a member of that group or community.
• Internal Consistency -- the extent to which all questions or items assess the same characteristic, skill, or quality.
• Internal Validity -- the rigor with which the study was conducted [e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and was not measured]. It is also the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore. In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.
• Life History -- a record of an event/events in a respondent's life told [written down, but increasingly audio or video recorded] by the respondent from his/her own perspective in his/her own words. A life history is different from a "research story" in that it covers a longer time span, perhaps a complete life, or a significant period in a life.
• Margin of Error -- the permittable or acceptable deviation from the target or a specific value. The allowance for slight error or miscalculation or changing circumstances in a study.
• Measurement -- process of obtaining a numerical description of the extent to which persons, organizations, or things possess specified characteristics.
• Meta-Analysis -- an analysis combining the results of several studies that address a set of related hypotheses.
• Methodology -- a theory or analysis of how research does and should proceed.
• Methods -- systematic approaches to the conduct of an operation or process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
• Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories. It is also referred to as blended methods, combined methods, or methodological triangulation.
• Modeling -- the creation of a physical or computer analogy to understand a particular phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model can be shown to account for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon.
• Models -- representations of objects, principles, processes, or ideas often used for imitation or emulation.
• Naturalistic Observation -- observation of behaviors and events in natural settings without experimental manipulation or other forms of interference.
• Norm -- the norm in statistics is the average or usual performance. For example, students usually complete their high school graduation requirements when they are 18 years old. Even though some students graduate when they are younger or older, the norm is that any given student will graduate when he or she is 18 years old.
• Null Hypothesis -- the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. Investigators usually hope that the data will demonstrate some effect from the intervention, thus allowing the investigator to reject the null hypothesis.
• Ontology -- a discipline of philosophy that explores the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality.
• Panel Study -- a longitudinal study in which a group of individuals is interviewed at intervals over a period of time.
• Participant -- individuals whose physiological and/or behavioral characteristics and responses are the object of study in a research project.
• Peer-Review -- the process in which the author of a book, article, or other type of publication submits his or her work to experts in the field for critical evaluation, usually prior to publication. This is standard procedure in publishing scholarly research.
• Phenomenology -- a qualitative research approach concerned with understanding certain group behaviors from that group's point of view.
• Philosophy -- critical examination of the grounds for fundamental beliefs and analysis of the basic concepts, doctrines, or practices that express such beliefs.
• Phonology -- the study of the ways in which speech sounds form systems and patterns in language.
• Policy -- governing principles that serve as guidelines or rules for decision making and action in a given area.
• Policy Analysis -- systematic study of the nature, rationale, cost, impact, effectiveness, implications, etc., of existing or alternative policies, using the theories and methodologies of relevant social science disciplines.
• Population -- the target group under investigation. The population is the entire set under consideration. Samples are drawn from populations.
• Position Papers -- statements of official or organizational viewpoints, often recommending a particular course of action or response to a situation.
• Positivism -- a doctrine in the philosophy of science, positivism argues that science can only deal with observable entities known directly to experience. The positivist aims to construct general laws, or theories, which express relationships between phenomena. Observation and experiment is used to show whether the phenomena fit the theory.
• Predictive Measurement -- use of tests, inventories, or other measures to determine or estimate future events, conditions, outcomes, or trends.
• Principal Investigator -- the scientist or scholar with primary responsibility for the design and conduct of a research project.
• Probability -- the chance that a phenomenon will occur randomly. As a statistical measure, it is shown as p [the "p" factor].
• Questionnaire -- structured sets of questions on specified subjects that are used to gather information, attitudes, or opinions.
• Random Sampling -- a process used in research to draw a sample of a population strictly by chance, yielding no discernible pattern beyond chance. Random sampling can be accomplished by first numbering the population, then selecting the sample according to a table of random numbers or using a random-number computer generator. The sample is said to be random because there is no regular or discernible pattern or order. Random sample selection is used under the assumption that sufficiently large samples assigned randomly will exhibit a distribution comparable to that of the population from which the sample is drawn. The random assignment of participants increases the probability that differences observed between participant groups are the result of the experimental intervention.
• Reliability -- the degree to which a measure yields consistent results. If the measuring instrument [e.g., survey] is reliable, then administering it to similar groups would yield similar results. Reliability is a prerequisite for validity. An unreliable indicator cannot produce trustworthy results.
• Representative Sample -- sample in which the participants closely match the characteristics of the population, and thus, all segments of the population are represented in the sample. A representative sample allows results to be generalized from the sample to the population.
• Rigor -- degree to which research methods are scrupulously and meticulously carried out in order to recognize important influences occurring in an experimental study.
• Sample -- the population researched in a particular study. Usually, attempts are made to select a "sample population" that is considered representative of groups of people to whom results will be generalized or transferred. In studies that use inferential statistics to analyze results or which are designed to be generalizable, sample size is critical, generally the larger the number in the sample, the higher the likelihood of a representative distribution of the population.
• Sampling Error -- the degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection of respondent and the corresponding reduction in reliability.
• Saturation -- a situation in which data analysis begins to reveal repetition and redundancy and when new data tend to confirm existing findings rather than expand upon them.
• Semantics -- the relationship between symbols and meaning in a linguistic system. Also, the cuing system that connects what is written in the text to what is stored in the reader's prior knowledge.
• Social Theories -- theories about the structure, organization, and functioning of human societies.
• Sociolinguistics -- the study of language in society and, more specifically, the study of language varieties, their functions, and their speakers.
• Standard Deviation -- a measure of variation that indicates the typical distance between the scores of a distribution and the mean; it is determined by taking the square root of the average of the squared deviations in a given distribution. It can be used to indicate the proportion of data within certain ranges of scale values when the distribution conforms closely to the normal curve.
• Statistical Analysis -- application of statistical processes and theory to the compilation, presentation, discussion, and interpretation of numerical data.
• Statistical Bias -- characteristics of an experimental or sampling design, or the mathematical treatment of data, that systematically affects the results of a study so as to produce incorrect, unjustified, or inappropriate inferences or conclusions.
• Statistical Significance -- the probability that the difference between the outcomes of the control and experimental group are great enough that it is unlikely due solely to chance. The probability that the null hypothesis can be rejected at a predetermined significance level [0.05 or 0.01].
• Statistical Tests -- researchers use statistical tests to make quantitative decisions about whether a study's data indicate a significant effect from the intervention and allow the researcher to reject the null hypothesis. That is, statistical tests show whether the differences between the outcomes of the control and experimental groups are great enough to be statistically significant. If differences are found to be statistically significant, it means that the probability [likelihood] that these differences occurred solely due to chance is relatively low. Most researchers agree that a significance value of .05 or less [i.e., there is a 95% probability that the differences are real] sufficiently determines significance.
• Subcultures -- ethnic, regional, economic, or social groups exhibiting characteristic patterns of behavior sufficient to distinguish them from the larger society to which they belong.
• Testing -- the act of gathering and processing information about individuals' ability, skill, understanding, or knowledge under controlled conditions.
• Theory -- a general explanation about a specific behavior or set of events that is based on known principles and serves to organize related events in a meaningful way. A theory is not as specific as a hypothesis.
• Treatment -- the stimulus given to a dependent variable.
• Trend Samples -- method of sampling different groups of people at different points in time from the same population.
• Triangulation -- a multi-method or pluralistic approach, using different methods in order to focus on the research topic from different viewpoints and to produce a multi-faceted set of data. Also used to check the validity of findings from any one method.
• Unit of Analysis -- the basic observable entity or phenomenon being analyzed by a study and for which data are collected in the form of variables.
• Validity -- the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure. A method can be reliable, consistently measuring the same thing, but not valid.
• Variable -- any characteristic or trait that can vary from one person to another [race, gender, academic major] or for one person over time [age, political beliefs].
• Weighted Scores -- scores in which the components are modified by different multipliers to reflect their relative importance.
• White Paper -- an authoritative report that often states the position or philosophy about a social, political, or other subject, or a general explanation of an architecture, framework, or product technology written by a group of researchers. A white paper seeks to contain unbiased information and analysis regarding a business or policy problem that the researchers may be facing.

Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.

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An A to Z of Research Literacy

2019, Dictionary Contribution

Learning the terminology and jargon of a field also means understanding the concepts represented by these terms and understanding how they are interlinked and interrelated. These 226 terms and their respective definitions represent the collective knowledge of 43 teachers from 17 countries. An A to Z of Research Literacy is the first resource developed by the research literacy team at Dubai Men's College in the UAE. It is part of a collection of resources designed to increase teacher research engagement and to help teachers increase their knowledge about research. These resources are specifically designed for teachers with all levels of research literacy especially those interested in becoming more involved in the research process. Other research tools and resources include: • Lessons for teaching classroom research-Designed by teachers for teachers, this ebook of lesson plans provides teachers with ready-made activities that can be used to focus on different aspects of the research process. • Research literacy quiz-Designed by testing specialists, this online quiz will help teachers determine their current levels of research knowledge. Every attempt has been made to include definitions that are readable and easy to understand. It is hoped that this resource will be an important part in your ongoing journey to become a more research literate teacher.

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Zuzanna Zbróg

Development and Validation of a Quantitative Research Literacy Questionnaire

Alireza Zaker

Simultaneous with the emergence of numerous new concerns and techniques in carrying out research in English language teaching (ELT) contexts, conducting ELT research has turned into a multi-dimensional process, and ELT researchers seem to require advanced research skills in order to address different pedagogical issues. Focusing on quantitative research, the present study was undertaken to develop and validate a quantitative research literacy (QRL) instrument for English as a foreign language (EFL) teacher training contexts. To this objective, a four component model of QRL, encompassing developing research topic knowledge, research design knowledge, procedural knowledge, and data analysis knowledge was developed based on experts’ opinion and an extensive review of the related literature. Testing of the tentative model through exploratory and confirmatory data analyses on a sample of 1180 EFL teachers across three education levels indicated that a 4-factor model of QRL with 9 sub-factors and 50 items could best explain QRL.

Angela K Murray , Tracey Sulak

Currently, the Montessori community is increasing its focus on the importance of research. You may have noticed that Montessori Life is featuring more research-related articles and that there are “How to conduct research” sessions at AMS conferences. The good news is that you don’t have to have a PhD to be a discerning consumer of research! The purpose of this article is to provide you with some background to help you be a more critical reader of research related to Montessori education and to give you the tools to implement the findings in your own Montessori work. Research articles are generally organized in a way that allows the reader to easily follow the logical flow of the research process. You will typically see an introduction followed by information on research methods. Research articles usually conclude with a discussion of results and practical implications. The sections that follow outline key considerations and questions you should ask yourself in each of these areas.

Oluchukwu David OKOLI

This study assessed the research literacy levels among pre-service teachers. The study adopted a descriptive design and sampled 297 preservice teachers from two Nigerian South-eastern federal universities. Five research questions guided the study. Data was collected using a semistructured question guide and a self-developed Research Literacy Test (RLT). Rasch model analysis established the unidimensionality and reliability of the research literacy test (RLT). Moreover, mean scores, standard deviations, ttests and content analysis were used to answer the study research questions. The study findings showed that pre-service teachers' research literacy level was at the information literacy level. Significant differences were not found between males and females in their research literacy components except at the information level; males were generally more research literate than females. Finally, pre-service teachers indicated that they believe their faculties hold a positive disposition towards their acquisition of research literacy.

Research Papers in Education

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Introduction Since the beginning of the formalized education, research has been used to improve education and to determine in a wide range of situations. Through various research methods, teachers hope to obtain reliable and accurate information about important issue and problems that face the educational community. Knowledge of research is an essential as well as integral component of professional preparation for attaining skills and competence for all teachers. A must be knowledgeable about multifarious approaches of research to promote creative, innovative and sound solutions to learning and teaching issues. Weirsma and Jurs (2009) explain that research has become such a prevailing phenomenon of our civilization that all of us are influenced by it. He explains that the situation of an average elementary or high school teacher is not different than a graduate who face the issues in learning. Therefore, is carried out for the purpose of explaining and predicting phenomenon and part...

Support for Learning

Rafiq Islam

Research used to be an integral part of education until the Roman Empire's adoption of Christianity as the state religion. Even though the word 'religion' was removed in the modern era, Dogmatic cognition continued in all aspects of education. The disconnection between research and education was accentuated by the introduction of compartmentalization of different disciplines and conversion of education into indoctrination, training and skill development. Information age has opened up an opportunity to bring back true education (the root word educere meaning 'bringing forth'). Information age promised us transparency through superflux of data. However, the same information age also risks making the system opaque by making it easy to inundate the media with falsehoods, branding them as facts. Unless a person is equipped with the faculty to discern between truth and falsehood and process data through the deductive cognition process, having volumes of data actually will render a person more ignorant, albeit with a touch of arrogance. As such, the information age can make an education program entirely implosive. The new curriculum makes the best use of the information age that offers us a unique opportunity in the form of 1) transparency (arising from monitoring space and time); 2) infinite productivity (due to inclusion of intangibles, zero-waste, and transparency); and 3) custom-designed solutions (due to transparency and infinite productivity). The new curriculum involves the introduction of research questions for every subject. This immediately eliminates the problem of plagiarism and opens up opportunities of maximizing new discoveries, thus pushing the boundary of knowledge. The function of the teachers/instructors then becomes that of the 'wondering child' or 'lone researcher'-the way Piaget visualized children as. Teachers give out the questions to the students, and by asking the questions they become integrated to the research endeavour. In addition, by asking questions, teachers become conscious of the knowledge gap, thus helping students and themselves get a head start on filling the knowledge gap. In this book, three key disciplines, namely, Engineering, Law and Medical science, are addressed. First, underlying premises behind the dominant theories are highlighted in order to expose the implosive nature of these theories and 'laws'. Then, questions are asked in order to reconstruct theories that are logical and can pass the sustainability test. This chapter shows how the step-by-step procedure helped develop key research monographs on a wide range of topics, using the sustainability theme. Then, it uses the procedure to outline the research-based curriculum for the three

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This book will help you to plan, design and conduct quality research within the specific context of education and educational studies. An impressive cast of contributors discuss the reality of conducting research in different educational settings and provide practical advice for both undergraduate and postgraduate students and early career researchers doing research in education. The book discusses key philosophical issues such as understanding research paradigms, ethics and selecting appropriate methodologies but remains grounded in the practical experience of the researcher. It has comprehensive coverage of the whole research process from start to finish, is easy to navigate and helps develop key skills such as: •Time management •Creating good research questions and hypotheses •Constructing the literature review •Structuring a project •Writing a proposal •Managing data •Analysing data •Writing for specific audiences Packed full of learning features and showcasing a wide range of voices and opinions this book is an ideal guide for anyone conducting research in education or educational studies.

Christine E Bottrell

Pre-service teacher education students, in most courses, are required to undertake some study of education research methods, to provide them with skills to read and make use of education research. However, the field of education research is a complex and difficult area. Perhaps focusing upon the most frequently used research methods could be a useful starting point. So, what type of research methods are most favoured in education? This article describes the authors’ attempts to answer this question, and the unexpected outcomes of the quest.

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The A-Z of Social Research A Dictionary of Key Social Science Research Concepts

• Robert Lee Miller - Queen's University Belfast, UK
• John D Brewer - Trinity College, Ireland
• Description

Have you ever wondered what a concise, comprehensive book providing critical guidance to the whole expanse of social science research methods and issues might look like? The A-Z is a collection of 94 entries ranging from qualitative research techniques to statistical testing and the practicalities of using the Internet as a research tool. Alphabetically arranged in accessible, reader-friendly formats, the shortest entries are 800 words long and the longest are 3000. Most entries are approximately 1500 words in length and are supported by suggestions for further reading. The book:

- Answers the demand for a practical, fast and concise introduction to the key concepts and methods in social research

- Supplies students with impeccable information that can be used in essays, exams and research projects

- Demystifies a field that students often find daunting

This is a refreshing book on social research methods, which understands the pressures that modern students face in their work-load and seeks to supply an authoritative study guide to the field. It should fulfil a long-standing need in undergraduate research methods courses for an unpatronising, utterly reliable aid to making sense of research methods.   Abduction and Retroduction   Action Research A Case Study

`I think the book is likely to appeal to a wider audience than students, as medical colleagues and those less familiar with social research attempt to find out more about the terms and concepts they encounter' - Medical Sociology News

An innovative and engage approach to social research

I like this book. There are some interesting sections e.g. Grounded theory. However the layout is not easy yo navigate - topics are not necessarily where you would expect to find them.

This text provides concise definitions of terminology encountered by students undertaking research. The text would benefit from updating some of the suggestions for further reading.

this book is not up to date in my opinion...needs a revised edition

A very useful reference point for students and novice researchers.

This is fantastic. If a student feels lost, confused, bemused this clear A-Z of social science research methods and issues will stop the terrified tremors of 'I just don't understand'. Practical and demystifying.

Terminology that ALL researchers need!

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SAGE Research Methods is a research methods tool created to help researchers, faculty and students with their research projects. SAGE Research Methods links over 175,000 pages of SAGE’s renowned book, journal and reference content with truly advanced search and discovery tools. Researchers can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and more.

With SAGE Research Methods, researchers can explore their chosen method across the depth and breadth of content, expanding or refining their search as needed; read online, print, or email full-text content; utilize suggested related methods and links to related authors from SAGE Research Methods' robust library and unique features; and even share their own collections of content through Methods Lists. SAGE Research Methods contains content from over 720 books, dictionaries, encyclopedias, and handbooks, the entire “Little Green Book,” and "Little Blue Book” series, two Major Works collating a selection of journal articles, and specially commissioned videos.

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April 30, 2024 | Olivia Drake - UConn College of Engineering

College of Engineering Launches New Collaboratory for Biomedical and Bioengineering Innovation

Biomedical and bioengineering researchers at UConn and UConn Health will work together to invent, develop, and establish new interdisciplinary collaborations outside their existing research networks

The Collaboratory for Biomedical and Bioengineering Innovation opened April 2 with a symposium and poster session. Faculty from several engineering disciplines attended to learn about the interdisciplinary relationships related to biomedical and bioengineering research and technology innovation. (Chris LaRosa/UConn)

A new initiative in the College of Engineering will serve as the nexus for bio-based technology at UConn.

The Collaboratory for Biomedical and Bioengineering Innovation fosters a vibrant and unified environment where biomedical and bioengineering researchers work together to invent, develop, and adapt existing biotechnologies to solve new problems in the biological sciences.

“There seems to be an artificial divide between researchers who focus on biomedical studies and those working on other biological problems,” says Leslie Shor , associate dean for research and graduate education and co-director of Collaboratory for Biomedical and Bioengineering Innovation. “This is especially strange for engineers, because we are often leading the technical aspects of the work, and an enabling technology such as a novel sensor or new imaging technology works the same regardless of the biological application.”

The Collaboratory, however, aims to help researchers establish new interdisciplinary collaborations outside their existing research networks.

“By promulgating emerging technologies across fields, we enhance the value of the emerging technology and simultaneously unlock new areas of inquiry and accelerate new discoveries,” Shor explains.

Bio-based technology, or biotechnology innovation refers to the development and advancement of technologies that are based on biological systems or use biological materials. This can include a wide range of innovations such as biomedical devices (prosthetics, medical imaging equipment, drug delivery systems); bio-systems (biofuels production, bioremediation of pollutants, agricultural biotechnology); and bio-computation (bioinformatics for analyzing genetic data, computational modeling of biological systems, or machine learning algorithms for drug discovery).

Members of the Collaboratory are nationally and internationally-renowned faculty.

Thanh Nguyen , associate professor of mechanical engineering and biomedical engineering, works at the interface of biomedicine, materials and nano/micro technology. He’s already collaborating with researchers on campus and UConn Health for vaccine, drug, tissue-engineering and biomaterials research, but expects the Collaboratory for Biomedical and Bioengineering Innovation will help strengthen those relationships and allow him to explore more research opportunities.

“UConn is already a collaborative and terrific environment for interdisciplinary research. But this initiative makes biomedical and engineering research from different groups much more visible to all researchers at UConn.” Nguyen says. “The Collaboratory also could eventually lead to more impactful studies and grant funding.”

Like Nguyen, Sabato Santaniello , associate professor of biomedical engineering, is interested in potential collaborations with UConn Health and other medical centers in the region. His work in neuromodulation of the cerebellum is primarily targeted to clinical neuroscience—providing new ways of probing the diseased brain and improving treatments of patients affected by movement disorders.

“My work has potential to translate into new, patentable products down the road, but now, my program can benefit the initiative by intercepting the needs of clinicians, especially neurologists and neurosurgeons,” he says.

Santaniello describes the Collaboratory “as a unique platform” that will regionally advertise the many cutting-edge biomedical technologies that UConn faculty develop and better intercept the needs that come from the healthcare industry and the clinical research.

“It will benefit greatly those PIs at UConn who are looking for new, exciting applications for the tools that are developed in their labs,” he says.

The group aims to promote bio-based technologies through collaborative research; boost economic growth in Connecticut by creating new bio-based products and businesses; train students for biotech careers by involving them in research and innovation; and establish UConn as a global leader in bio-based technology innovation.

“Our goals are to drive research, investment, and possibilities in Connecticut,” explains Guoan Zheng , associate professor of biomedical engineering and co-director of the Collaboratory for Biomedical and Bioengineering Innovation. “By advancing technology, we believe we can make a significant impact on scientific discovery and its applications driving socially impactful research and benefiting Connecticut’s economy and workforce.”

Shor, who’s also Centennial Professor of Chemical and Biomolecular Engineering, leads the Engineered Microhabitats Research Group at UConn, where she mentors an interdisciplinary team focusing on biotechnology for sustainability. “My lab simply adapted established microfluidics or ‘lab-on-a-chip’ technologies to a completely different field of biology: soil microbes living near plant roots. This approach directly led to new understanding about soil moisture regulation by bacteria and fungi and a new appreciation for how soil protists can be used to promote more sustainable food production. I want to see the same interchange of approaches advance all types of biological sciences to advance a healthy and sustainable future,” she said.

The Collaboratory is seeking student, faculty, and corporate partners. For more information, contact the UConn Collaboratory .

The Collaboratory for Biomedical and Bioengineering Innovation celebrated its launch April 2 with a networking symposium and poster session. Faculty from several engineering disciplines attended to learn about the interdisciplinary relationships related to biomedical and bioengineering research and technology innovation. Photos of the event are below and in this UConn College of Engineering Flickr album . (Chris LaRosa/UConn)

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• Published: 23 April 2024

Down-to-earth drought resistance

Nature Plants volume  10 ,  pages 525–526 ( 2024 ) Cite this article

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• Plant breeding

Drought is a serious threat to global food security. In upstream research, crop drought-tolerant traits are often studied under extreme drought conditions, which can seem irrelevant in the eyes of breeders.

Although wildfire may have positive ecological function (as we discussed in our February editorial 1 ), drought — its related, but seemingly lesser, stressor — is harmful or even devastating, particularly to agricultural ecosystems. Drought develops gradually and its start or end can be difficult to identify, but its effects are often long-term and catastrophic. Climate change is predicted to lead to more frequent and severe droughts in many parts of the world. Last year was one of the hottest and driest in historical record, and people in the Horn of Africa suffered particularly badly 2 ; a record that is likely to be surpassed all too soon. Breeding drought-resilient crops is often proposed as a solution for mitigating the negative outcomes of drought and has become an important and urgent goal for global research communities. But this endeavour is impeded by the gap between basic research and breeding practice.

A Comment published in Nature 3 in September 2023 highlighted that many previous publications have oversold the effects of their reported genes in yield gain. Out of 1,671 reported yield-increasing genes, only one showed constant yield benefits in maize across years and locations in a large-scale field trial. Without close collaborations between molecular biologists (or geneticists) and breeders, unrealistic field trials have overestimated the agronomic effects of tested genes. The authors proposed five criteria for evaluating yield gain in field trials, including standardized definitions of yield, and multiple-location and multiyear experiments.

Drought resistance is also a complex trait that is defined differently under different scenarios, and is greatly affected by the environment. This complexity causes a similar disconnect between genetic studies and the breeding of drought resistance. Multiple breeding programmes have been undertaken worldwide by large research units such as the International Rice Research Institute (IRRI) and the International Maize and Wheat Improvement Center (CIMMYT) in pursuit of drought-resistant crops. At the same time, molecular biologists and geneticists continue to report the cloning of genes with drought resistance or tolerance traits, but these genes are rarely beneficial to crop breeders. As drought resistance expert Lijun Luo said at a recent conference in Sanya last month, “out of the over 300 rice functional genes claimed to increase drought resistance, none of them has been successfully applied in breeding!”

The main problem, according to Luo, is that these molecular studies focus on ‘drought tolerance traits’ rather than ‘yield under drought’. There is a well-established trade-off between stress tolerance and the productivity of plants; many wild relatives of crops exhibit strong stress tolerance but poor yield potentials. Conversely, upland rice varieties, such as IRAT109, that display stable yield under drought tend to have very poor drought tolerance (according to Luo). Improving the drought tolerance of crops without considering yield in the field is shooting at the wrong target.

If IRAT109 is not drought tolerant, then the question arises of what guarantees its yield stability under drought. The answer is its elite drought avoidance. It has long been realized that drought resistance can be achieved by multiple traits that are broadly classifiable into three main types: drought escape (by short life duration), drought avoidance (by deeper root distribution) and drought tolerance 4 . Scientists who use model plants such as Arabidopsis and rice to study drought resistance mechanism often focus on drought tolerance traits — such as the ability of plants to survive drought when dehydration has already occurred in the plant tissues — using water deprivation or polyethylene glycol treatment to screen for resistance. The resultant phenotypes often bestow a higher survival rate of the plants under drought or a higher recovery rate during rehydration, but not necessarily a higher yield. Without deciding beforehand the specific drought-resistant trait that is needed to improve the productivity of the specific crops in the target environment, laboratory-based studies can become aimless and futile.

Knowledge about environments is also important. According to the levels of yield loss (from 85% to 40%) under drought, Kumar et al. classified drought stresses as very severe, severe, moderate and mild 5 . Henry and Torres in the IRRI tested the performance of several rice varieties and found that the varieties that are adapted to mild and moderate drought with stable yield are different from the varieties adapted to more severe drought stress 6 . As mild drought stress affects a large proportion of drought-prone rice-growing areas in the world, a laboratory experiment that applies severe stress treatment can hardly be expected to identify genes that are useful in most drought-affected areas. In addition, droughts can be of different durations (short or long), different frequencies (continuous, intermittent or once per season) or occur at different growth periods of the crop. Crops use different drought-resistant traits or mechanisms to adapt to these types of droughts. Purely laboratory-based research can oversimplify drought stress treatments and so fail to understand the severity or types of droughts that are agriculturally relevant 7 .

In a paper published in 2021, Xiong et al. 8 reported that climate change has increased the ranking changes of wheat varieties in breeding trials over the past four decades. In other words, the relative performance of crop varieties is becoming less easy for breeders to predict. However, breeding trials targeted to drought or heat stress environments have not been affected. Breeding trials would also benefit from precisely targeted agronomically relevant stress environments.

To better cope with future droughts, drought-related crop research needs precision. Molecular biologists must cooperate with — or at least consult — agronomists to better understand their needs. It is certainly informative to study a drought avoidance trait such root architectures or a drought tolerance trait such leaf rolling 9 , but it is also crucial to monitor yield under drought. Moreover, high-yielding and widely planted varieties make a more appropriate genetic background than poor-yielding model genotypes when testing for drought resistance in the real world.

The natural variations of crops held in their wild relatives or in adapted landraces (such as upland rice) provide a valuable genetic resource to help to balance yield and drought resistance. The increasing availability of their genomes provide opportunities for researchers to identify the genes or quantitative trait loci that are most likely to complement the current breeding pool for drought resistance. Better evaluation of these materials, followed by their utilization in precision drought research, will hasten the development of resilient crops.

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Down-to-earth drought resistance. Nat. Plants 10 , 525–526 (2024). https://doi.org/10.1038/s41477-024-01686-z

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Mathematical Foundations of the Golden Rule. II. Dynamic Case

• Mathematical Game Theory and Applications
• Published: 12 October 2018
• Volume 79 , pages 1929–1952, ( 2018 )

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• V. I. Zhukovskiy 1 ,
• L. V. Smirnova 2 &
• A. S. Gorbatov 1  

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This paper extends the earlier research of the Golden Rule in the static case [2] to the dynamic one. The main idea is to use the Germeier convolution of the payoff functions of players within the framework of antagonistic positional differential games in quasi motions and guiding control.

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Solving the Problem of Approach of Controlled Objects in Dynamic Game Problems

Differential games for neutral-type systems: an approximation model.

Method of Resolving Functions in the Theory of Conflict—Controlled Processes

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Moscow State University, Moscow, Russia

V. I. Zhukovskiy & A. S. Gorbatov

Razumovsky State University of Technologies and Management (the First Cossack University), Moscow, Russia

L. V. Smirnova

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Correspondence to V. I. Zhukovskiy .

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Original Russian Text © V.I. Zhukovskiy, L.V. Smirnova, A.S. Gorbatov, 2016, published in Matematicheskaya Teoriya Igr i Ee Prilozheniya, 2016, No. 1, pp. 27–62.

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Zhukovskiy, V.I., Smirnova, L.V. & Gorbatov, A.S. Mathematical Foundations of the Golden Rule. II. Dynamic Case. Autom Remote Control 79 , 1929–1952 (2018). https://doi.org/10.1134/S0005117918100156

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Received : 11 November 2015

Published : 12 October 2018

Issue Date : October 2018

DOI : https://doi.org/10.1134/S0005117918100156

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