1.2 Methods Psychological Research


Ch 2: Types of research: Descriptive, evaluative, diagnostic and prognostic; Methods of Research: Survey, observation, case-study and experiments; Characteristics of experimental design and non-experimental design, Quasi-experimental designs; Focused group discussions, brain storming, grounded theory approach.

Ch 3: Major steps in Psychological research (problem statement, hypothesis formulation, research designs, sampling, tools of data collection, analysis and interpretation and report writing) Fundamental versus applied research; Methods of data collection (interview, observation, questionnaire); Research designs (ex-post facto and experimental); Application of statistical technique (t -test, two way ANOVA correlation, regression and factor analysis); Item response theory.

Previous Years’ Questions


Q. How can confounding variables invalidate the apparent results of an experiment? 10 marks [2016]

Q. Discuss with examples, the key characteristics of within-group and between-group designs. 15 marks [2016]


Q. How far is it correct to state that most of the problems of psychology can be addressed more adequately by adopting quasi-experimental designs. 10 marks [2015]

Q. Under what kind of research conditions does the use of factor analysis become necessary? Discuss. 20 marks [2015]

Q. It is believed that non-experimental designs are more relevant for explaining the emerging issues like social evils that are seen prominently in India. Discuss. 20 marks [2015]

Q. With suitable examples, discuss the logic behind following the systematic steps in conducting psychological research. 15 marks [2015]


Q. Describe the uses of factor analysis in Psychological research and indicate different types of rotations used in it. 10 marks [2014]

Q. In what ways does within-factorial design differ from between-factorial design? 10 marks [2014]

Q. What are multi-variate techniques used in Psychological research? Indicate their uses. 15 marks [2014]

Q. What are various kinds of threats to validity of experimental research? Illustrate your answer with the help of examples. 20 marks [2014]


Q. What do you understand by ‘effect size’ and ‘statistical power’? Explain their significance. 15 marks [2013]

Q. Describe the basic elements of observation and bring out the implications of the dimension of participation in observational research. 15 marks [2013]

Q. What are the requirements to be met by psychological assessment tools for offering accurate and useful measure of psychological constructs? 15 marks [2013]


Q. Discuss the criteria of question writing in a survey research 12 marks [2012]

Q. Bring out the differences between sampling error and error in sampling. How sampling error is detrimental to scientific study? 12 marks [2012]

Q. Discuss the three basic conditions for using t-test of significance. Describe at least five different uses of t-test, with examples. 20 marks [2012]

Q. In which way Item Response Theory is an improvement over classical tests clearly? Compare the two approaches and critically evaluate Rasch’s model of IRT. 30 marks [2012]

Q. Compare LISREL program with that of SPASS in the analysis of multivariate data. 12 marks [2012]


Q. Suggest a plan of an experimental study to evaluate the effect of compensatory education on academic achievement of school- going students from low income group. 10 marks [2011]

Q.Critically evaluate internal consistency and stability coefficients as indices of reliability. 20 marks [2011]

Q. Examine the concerns for control, measurement and artifacts, and also indicate the threats they pose to the development of scientific psychology. 30 marks [2011]


Q. Under what conditions would a researcher prefer to use focused group discussion over interviewing? 10 marks [2010]

Q. In what ways does an experimental desgn differ from a quasi-experimental design ? 10 marks [2010]

Q. What are the problems a researcher is likely to face in making causal inferences if the researcher were to use a single - group pre-test-post-test design ? 30 marks [2010]


Q. With suitable examples distinguish between exploratory type and confirmatory type factor analysis. How do you examine the significance level of factors loadings? 20 marks [2009]

Q. Discuss the use(s) of SPSS program in psychological testing. 20 marks [2009] 


Q. Grounded theory takes a case rather than variable perspective. Elaborate this statement. 20 marks [2008]

Q. Can Item Response theory be called a latent trade theory? Describe the mathematical functions that are used in this theory and explain the various models related to the theory. 60 marks [2008]

Types of research

1.Descriptive/Statistical/Generative Research: type of research that describes the what of a situation, not how or why or what caused it. Purpose is to observe, describe and document aspects of a situation as it naturally occurs and sometimes to serve as a staring point for hypothesis generation theory development. e.g. noting down case study details thoroughly, or naturalistic observation of a football stadium crowd etc.

It is used to describe characteristics of a population or phenomenon being studied - mainly done when a researcher wants to gain a better understanding of a topic. It does NOT answer questions about how/when/why the characteristics occurred.

2. Evaluative Research: testing/assessing or evaluating existing solutions (theories) to see if it meets the demands.

3. Diagnostic/Correlational Research: Researcher tries to venture into root causes of a problem. She tries to describe the factors responsible for the problematic situation. It is a problem solving research design and consists mainly of

Even if correlation is established, causation cannot be established. To establish causation, the researcher should be able to say that the result is the outcome of the observed variable and not something else. Correlation is not causation.

4. Prognostic/Predictive Research: refers to any scientific investigation in which the main and stated purpose is to predict the future operation of the factors investigated, so that inevitable things that must be done may be controlled intelligently on the basis of knowledge about the analyzed trend of their occurrence over a definitely selected period of time.

Fundamental vs applied research

"It is probably a mistake to view the basic-vs-applied distinction solely in terms of whether a study has practical applications, because this difference often simply boils down to a matter of time. Applied findings are of use immediately. However, there is nothing so practical as a general and accurate theory." -  Keith E. Stanovich (Emeritus Professor of Applied Psychology and Human Development, University of Toronto and former Canada Research Chair of Applied Cognitive Science)

Major steps in Psychological research

Problem Statement - Hypothesis Formulation - Choosing a research design - sampling- data collection - analyzing data - report writing

  1. Problem statement

  1. Hypothesis formulation

Types of hypothesis
Simple Hypothesis
Complex Hypothesis
Empirical Hypothesis
Null Hypothesis
Alternative Hypothesis
Logical Hypothesis
Statistical Hypothesis

Simple Hypothesis

Simple hypothesis is that one in which there exists relationship between two variables one is called independent variable or cause and other is dependent variable or effect. For example
Smoking leads to Cancer
The higher ratio of unemployment leads to crimes.

Complex Hypothesis

Complex hypothesis is that one in which as relationship among variables exists. In this type dependent as well as independent variables are more than two. For example
Smoking and other drugs leads to cancer, tension chest infections etc.
The higher ration of unemployment poverty, illiteracy leads to crimes like dacoit, Robbery, Rape, prostitution & killing etc.

Empirical Hypothesis

Working hypothesis is that one which is applied to a field. During the formulation it is an assumption only but when it is pat to a test become an empirical or working hypothesis. 

Null Hypothesis

Null hypothesis is contrary to the positive statement of a working hypothesis. According to null hypothesis there is no relationship between dependent and independent variable. It is denoted by ‘HO”.

Alternative Hypothesis

Firstly many hypotheses are selected then among them select one which is more workable and most efficient. That hypothesis is introduced latter on due to changes in the old formulated hypothesis. It is denote by “HI”.

Logical Hypothesis

It is that type in which hypothesis is verified logically. J.S. Mill has given four cannons of these hypothesis e.g. agreement, disagreement, difference and residue.

Statistical Hypothesis

A hypothesis which can be verified statistically called statistical hypothesis. The statement would be logical or illogical but if statistic verifies it, it will be statistical hypothesis.

A good hypothesis possesses the following certain attributes.

Power of Prediction

One of the valuable attribute of a good hypothesis is to predict for future. It not only clears the present problematic situation but also predict for the future that what would be happened in the coming time. So, hypothesis is a best guide of research activity due to power of prediction.

Closest to observable things

A hypothesis must have close contact with observable things. It does not believe on air castles but it is based on observation. Those things and objects which we cannot observe, for that hypothesis cannot be formulated. The verification of a hypothesis is based on observable things.


A hypothesis should be so dabble to every layman, P.V young says, “A hypothesis wo0uld be simple, if a researcher has more in sight towards the problem”. W-ocean stated that, “A hypothesis should be as sharp as razor’s blade”. So, a good hypothesis must be simple and have no complexity.


A hypothesis must be conceptually clear. It should be clear from ambiguous information’s. The terminology used in it must be clear and acceptable to everyone.


A good hypothesis should be tested empirically. It should be stated and formulated after verification and deep observation. Thus testability is the primary feature of a good hypothesis.

Relevant to Problem

If a hypothesis is relevant to a particular problem, it would be considered as good one. A hypothesis is guidance for the identification and solution of the problem, so it must be accordance to the problem.


It should be formulated for a particular and specific problem. It should not include generalization. If generalization exists, then a hypothesis cannot reach to the correct conclusions.

Relevant to available Techniques

Hypothesis must be relevant to the techniques which is available for testing. A researcher must know about the workable techniques before formulating a hypothesis.

Fruitful for new Discoveries

It should be able to provide new suggestions and ways of knowledge. It must create new discoveries of knowledge J.S. Mill, one of the eminent researcher says that “Hypothesis is the best source of new knowledge it creates new ways of discoveries”.

Consistency & Harmony

Internal harmony and consistency is a major characteristic of good hypothesis. It should be out of contradictions and conflicts. There must be a close relationship between variables which one is dependent on other.

Types of errors in hypotheses

A type I error (or error of the first kind) is the incorrect rejection of a true null hypothesis. 

type II error (or error of the second kind) is the failure to reject a false null hypothesis. Examples of type II errors would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease; a fire breaking out and the fire alarm does not ring; or a clinical trial of a medical treatment failing to show that the treatment works when really it does.

  1. Research designs

In every other way quasi-experimental research is very much like experimental research. It has:

Quasi-experiments are commonly used in social sciences, public health, education, and policy analysis, especially when it is not practical or reasonable to randomize study participants to the treatment condition.

E.g. : Aim : To study whether aggressiveness in children has a positive correlation with the amount of violence they suffer at home.

Procedure : We divide households into two categories: Households in which the parents beat their children,  and households in which the parents do not beat their children. We can run a linear regression to determine if there is a positive correlation between parents' beating and their children's aggressive behavior.
However, to simply randomize parents to beat or to not beat their children may not be practical or ethical, because some parents may believe it is morally wrong to beat their children and refuse to participate.

Limitations of QEDs

Examples of how QEDs can be useful in many instances in Psychological research

e.g. 1 : Suppose one is interested in examining the IQ scores of people who score highly in each of the five 'Big Five' personality factors. Each of the five personality factors are a quasi-independent variable. Personality traits are inherent to each person, so random assignment cannot be used. Participants would initially be assigned to groups based on their personality assessment score across each of the five personality factors.

Now that one has the participant group assignments, s/he can examine the impact that personality factors may have on intelligence. If a true experimental design were used, each participant would be randomly assigned to each personality group regardless of whether or not they possessed those personality traits, which would not really address the question that was intended to answer.

e.g. 2 :  Dr. Loyd is a multicultural expert and is interested in the effect that race has on academic dishonesty. People cannot be randomly assigned to different race categories, so a quasi-experimental design is used.

If she is interested in examining academic dishonesty among Caucasian, African-American, and Native American college students, then as participants volunteered for her study, they would be assigned to the appropriate group based on their self-identified race. Once the groups have been assigned, Dr. Loyd can expose each group to the measure of academic dishonesty that she created and evaluate the results to see if differences emerge between the groups.


How does sampling help in Psychology

Importance of Sampling with an example

In an actual study that was done in the mid-1970s in the US, a researcher mailed out surveys to a bunch of married women and asked them questions about their marriage. Only 4% of people responded, and of those who did, 98% said they were dissatisfied in their marriage, and 75% said they had or were having an extramarital affair.

As you can imagine, this study sent shockwaves through America as husbands looked at their wives and calculated the probability of dissatisfaction or affairs. But the sample (the 4% who responded) didn't reflect the population of married women. Those who got the survey, filled it out, and returned it were much more likely to be dissatisfied than those who didn't return it. Maybe those who were happy in their marriage were too busy having fun with their spouse to cheat.

Whatever the case, further research on samples reflecting that population showed that, in reality, about 93% of women, at that time, were satisfied in their marriage and only about 7% had extramarital affairs.

That's why sampling is so important to research. If a sample isn't chosen carefully and systematically, it might not represent the population. And if it doesn't represent the population, then the study can't be generalized to the world beyond the study.

Convenience Sampling
Using any person conveniently as a part of the sample. Such samples are essentially biased and research findings would not be reliable

Procedures for making a sample representative:

Sampling Error and Error in Sampling

Guess based on different readings:

  1. Tools of data collection

  1. Analysis and interpretation

Both these methods are not contradictory rather they complement each other. For an accurate analysis, a mix of both should be used.

Application of statistical techniques

x1¯x1¯ = Mean of first set of values
x2¯x2¯ = Mean of second set of values
S1 = Standard deviation of first set of values
S2 = Standard deviation of second set of values
n1 = Total number of values in first set
n2 = Total number of values in second set.

also a sort of measure of the variability in the data.

Factor Analysis

A factor analysis is a statistical procedure that is used in order to find underlying groups of related factors in a set of observable variables. 

Suppose we have to research the grades of college students in an honor's Liberal Arts program. Our study sample consists of 150 college students, all who have taken five end-of-the-year exams- Mathematics, English literature, Science, Latin and Writing.

The students' grades on each of the five exams are positively correlated with each other: this means that students who have high grades on one exam usually have high grades on the others. However,  suppose we find that there are some students who are only good at two or three subjects. We start to wonder if the students' performances on the five exams could be determined by different types of intellectual abilities. One way to answer this question is by conducting a factor analysis.

Factor analysis is a statistical method that is used to investigate whether there are underlying latent variables, or factors, that can explain the patterned correlations within a set of observed variables. In this case, the observed variables would be the five exam scores. Latent variables are underlying constructs that are not directly observable and cannot be measured by one single thing. For example, we cannot directly measure the quality of someone's marriage. Instead, we can use a combination of observable variables to measure marriage quality, including the amount of time the couple spends together, the environment, marital conflict, marital attitudes, etc.

The primary goals of factor analysis are as follows:

- Determine how many factors underlie a set of observable variables
- Provide a method of explaining variance among observable variables by using fewer, newly created factors
- Reduce data by allowing the user to extract a small set of factors (which usually are not related to each other) from a larger set of observable variables (which are usually correlated with each other). This allows for summarization of a large number of variables into a smaller number of factors
- Define the meaning or content of the factors

There are two types of factor analyses:
  1.  Exploratory factor analysis (or EFA) and
  2.  Confirmatory factor analysis (or CFA).

Exploratory Factor Analysis
EFA is used in situations when we do not have a predetermined idea of how many factors there are or the relationship between the factors and the observed variables. The purpose of the EFA is to explore the structure of the factors. The goal is to find the underlying relationships that exist between the variables.

Suppose that we decided to take the data that we collected from the 150 college students and conduct an EFA. We are not sure if there are any underlying relationships between the variables, and we have no hypothesis as to what the relationships might be. We are just curious to see if we can find any underlying factors.

We run the exploratory factor analysis and find that there are two factors. Students who have high scores in math and science are high on the first factor, while students who have high scores on English, Latin, and writing are high on the second factor. We have just figured out the underlying factor structure using EFA.

Confirmatory Factor Analysis
CFA is used in situations where we have a specific hypothesis regarding how many factors there are and which observed variables are related to each factor. The hypothesis is usually based on previous research or theory. The purpose of CFA is to confirm that there is a relationship between the factors and the observed variables.

Suppose in the example above, we notice in the bulk data that some students are good at math and science and have lower scores on English literature, Latin, and writing. There are also students who scored high on English literature, Latin, and writing but did not do so well on math and science.

We may hypothesize that the students' performances on the five exams could be determined by the two types of intellectual abilities. Specifically, math and science are determined by one type of intellectual ability, while English literature, Latin, and writing performance are determined by another type of intellectual ability. In this example, we would perform a CFA.




Uses and Applications

Item Response Theory aka latent trait theory, strong true score theory, or modern mental test theory

  1. Report writing

Methods of Research /Data collection
1. Survey
2. Observation
3. Case Studies
4. Experiments
5. Psychological Testing
6. Correlational (already explained)

1. Survey: can be seen as a group of following methods of inquiry:

PI can be carried out as

Disadvantages of Questionnaires and Telephonic Surveys

General advantages of Survey Research Method

a) Is a Quick method of data collection. Has become even more faster with the advent of ICT.

b) Data related to large no. of persons can be collected

c) Public opinions on new issues can be obtained almost as soon as the issue arises (e.g. Brexit), which is not the case with other methods

Disadvantages of Survey Method

a) Limited or no liberty with the interviewer to change words or language of a questions

b) Fixed set of questions i.e. questions cannot be added or removed.

c) Reluctance, superficial responses and uncooperativeness of respondents.

d) Can sometimes be misleading if the group respondents are not true representative of the target population in terms of gender, religion, income levels, educational levels, cultural background etc.

2. Observation


i) Selection: Select a particular behaviour for study

ii) Recording: Marking tallies when the event occurs; taking notes describing the behaviour; photographs, videos etc.

iii) Analysis

Types of observations

a) Naturalistic (in homes, hospitals, schools etc.) vs Controlled(in a lab)

b) Non-Participant Observation: From a distance; Disadv: may make subjects conscious and thus may not give accurate results vs Participant observation: Observer becomes part of the group and then records behaviour; degree of involvement of the participant may vary depending on the focus of the study; Disadv: can be labourintensive, time consuming + susceptible to observer bias (Hence the observer should record the behaviour as it happens and should not interpret the behaviour at the time of observation itself.)

3. Case Studies

Can be done on

Examples of Case Studies

Cautions with case studies

4. Experiments

Imp. concepts

Cautions in experimental method

  1. The distribution of participants in all experimental and control groups should be RANDOM to eliminate the effect of personal/demographic attributes.

  1. All relevant variables that might influence the dependent variables need to be controlled.

Techniques to control relevant variables

Criticism of Experimental Methods

  1. External Validity: Since experiments are conducted in a controlled lab setup, some argue that they do not generalize well to the outer world or they lack external validity.

  1. Can’t be carried out in every situation: e.g. to check effects of nutritional deficiency on IQ, one cannot give bad nutrition to a particular experimental group (ethically wrong)

  1. Difficult to know and control all relevant variables

Types of Experiments

An experiment that involves more than just one independent variable is called a factorial experiment

5. Psychological Testing (PT)

Standardized and objective instrument which is used to assess an individual’s standing in relation to others on some mental or behavioural characteristics.

Cautions in PT

Characteristics of a good Pscyh Test/ Steps in construction of a good Psych Test

 How can reliability be ensured?

Types of Psychological Tests

Based on language

- Verbal: requires literacy

- Non-verbal: symbols and pictures

- Performance tests: requires movements of objects from their respective places in a particular order.

Based on administration of the test

- Individual: One to one e.g. for children, others who do not know the language

- Group: many people at once (written).

     Adv: Easy to administer + Less time consuming
     Diasdv: respondents may give fake responses or may nt be too motivated to answer questions.

Speed and Power Tests

- Speed Tests: Constrained time limit  + All items are of same difficulty level

- Power Tests: Sufficient time + Items arranged in increasing level of difficulty

It is difficult to construct a pure speed test or a pure power test. Most are a combination of the two.

Experimental and non-experimental research designs

Experimental research design:

- based on a clear hypothesis

- purpose = confirm or refute the validity of the hypothesis.

- have an independent variable, a control (dependent) variable, and a control group

- most exp. conducted in a lab. in a controlled environment

- studies the what, why and even how questions

- experimenter can manipulate variables has a control group and a placebo

- The control group receives the treatment that the experimenter wants to test and the placebo group is tested without any treatment.

- Differences in results of both groups are compared.

- Test is repeated in the same environment

Non-experimental research design

- carried out in natural settings

- no control group here and the research design is highly flexible. However, due to absence of control group the researcher cannot ascertain that the final results are the direct effect of the variable that has been studied

- purpose = study a situation, people or phenomenon over a time period to observe changes.

- no manipulation of the situation, event, circumstances or people.

- e.g. Survey, case studies, correlational studies, comparative studies, longitudinal studies and descriptive studies

- The non-experimental research design study the phenomenon, people or situation in a natural setting without manipulating it, hence the findings can be applied to a wide audience.

Within and Between Group Designs

Lorinda is doing a study. She thinks that girls will do better on a math test than boys will. So she gives the test to boys and girls and then grades the results to see which group does better.

Every social science research study has one or more groups of subjects, or sets of participants who are being studied. In Lorinda's case, she has two groups: girls and boys. But what happens if she discovers that there are more differences between two girls than there are between a boy and a girl?

To help Lorinda out, let's talk about within-group and between-group research.

Between-Group Differences
As we said, Lorinda is giving a math test to two groups, boys and girls, and she wants to see if there's a difference between the two groups. What she's looking for are between-group differences, or data that shows that two or more groups are different.

Between-group research is the most common type of research, and it can take many forms. In Lorinda's case, her groups are established already. That is, her subjects are already boys and girls, even before her study.

Sometimes, though, a researcher might create groups for their research. For example, if Lorinda wanted to test how well a math game helps students, she might create two groups: an experimental group, which plays the math game before taking the test, and a control group that does not play the math game before the test.

The number of groups can vary as well. For example, Lorinda is looking at two groups: boys and girls. But what if she wants to divide her subjects by age? She might have three or four groups if she's looking at different ages.

Whether the researcher creates the groups or they exist already, and regardless of whether there are two groups or more, between-group research focuses on the differences between the groups (hence its name).

Within-Group Differences
Like most researchers, Lorinda is looking for between-group differences, based on the average score on a math test. In other words, she wants to know if the mean score for girls is different from the mean score for boys.

But not every girl will perform equally on the test. There might be a lot of different scores when Lorinda looks at all the girls. When the data shows differences among subjects that are in the same group, this is known as within-group differences.

Within-group research can take a number of different forms. Often, a researcher only wants to look at one group; therefore, their research will only look at within-group differences. For example, if Lorinda only wanted to look at the scores of seven-year-old girls, and didn't want to compare them to any other group, she would look for trends and differences within that group of people (i.e., the seven-year-olds).

Within-group differences often come to light when a researcher is conducting a between-group research study. For example, there are many studies that talk about the differences between boys and girls. These might point out, for example, that one group does better on math tests, or that the two genders communicate differently, or even that there are differences in the brains of boys and girls. These are all between-group studies.

However, when you look closely at these studies, you might see something interesting. The within-group differences are often greater than the between-group differences. To understand what this means, let's go back to Lorinda's study on gender and the math test.

Let's say that the mean score for boys on the math test is 87, and the mean score for girls is 93. Sounds like there's a difference in the genders, right? But what if the scores for the girls ranged from 76 to 100, while the scores for the boys ranged from 80 to 97? In that case, the within-group differences (24 points for girls and 17 points for boys) are much larger than the difference in the mean scores between the two genders (6 points).

Does this mean that girls are better at math than boys are? Looking at the between-group differences, you might think so. And you might be right. But when you look at the variation within groups, you might see that there doesn't appear to be much of a difference between boys and girls after all. In fact, if you compared two girls' scores, you might find that their scores are more different than if you compared a boy and a girl. Within-group differences do not always contradict or cancel out between-group differences, but they can help paint a fuller picture of what's going on.

Every social science research study has one or more groups of subjects, or sets of participants who are being studied. There are two ways to look at the data about these groups. Between-group differences show how two or more groups are different, whereas within-group differences show differences among subjects who are in the same group. Within-group differences can come to light when looking at a between-group research study. While within-group differences do not always contradict or cancel out between-group differences, they can help paint a fuller picture of what's going on.

Focused Group Discussions (FGD)

- Effective method of healthy communication/to discuss a specific topic of interest.

- Exchange of Ideas, knowledge and opinions

- Not more than 12 and not less than 6 members

- group of participants is guided by a moderator (or group facilitator) who introduces topics for discussion and helps the group to participate in a lively and natural discussion

- strength of FGD relies on allowing the participants to agree or disagree with each other so that it provides an insight into how a group thinks about an issue, about the range of opinion and ideas, and the inconsistencies and variation that exists in a particular community in terms of beliefs and their experiences and practices.

FGDs can be used to

Process outline of FGDs

Facilitation/Moderation- the key of FGDs : imp. points to bear in mind in facilitating FGDs :

FGDs can be also done online-particularly useful for overcoming the barrier of distance.


a.k.a. storm ideas, creativity and brainstorming, idea generation or creative thinking.

- A group problem-solving/ creativity technique

- Group of people uses their collective intelligence to approach a creative problem.
- Purpose: Inspires people to come up with creative ideas.
- Should be used at the very beginning of a project/research, should address a specific question.


Brainstorming was originally discovered in the late 1940s by Alex Osborn. It was developed to inspire employees to produce creative ideas for ad campaigns.


- Brainstorming sessions, through poor facilitation or lack focus, can be less productive than expected.
- The six thinking hats technique, created by Edward de Bono, can overcome this problem.

- When wearing one hat at a given point of time, a group can focus on one aspect of the issue at hand, increasing the productivity of the brainstorming session.

Using the Technique

- Define a problem or opportunity and craft a specific question.

- Identify participants: can include 8 to 16 people. Not every group member needs to be an expert on the specific question.

- Participants should be trained in advance, so that they understand the brainstorming process prior to tackling the major issue.

- Set a time limit.

- Ask participants to shout out ideas, encouraging all participants to be active in the process.

- Record solutions. Typically, 2 facilitators capture ideas from the group on a whiteboard or flipchart.

- Participants are encouraged to provide unusual/creative answers. Criticism is reserved.

- Ideas can be combined and improved to form better solutions.


A specific question must be asked, such as, “How might we increase the computer literacy among elderly people?”


The process will yield a large quantity of answers.

Next Steps after Exercise

- Once creative/good ideas are formed and listed, it is necessary to prioritize them to reach the best solution.
- In order to prioritize them, select the 5 best ideas through team consensus.
- Choose five criteria for judging ideas that best solve the problem.

- Give each idea a score.

- The idea with highest score will be a strong solution.

Example of BS : The design firm IDEO used the brainstorming technique to develop the first Apple mouse.

Grounded Theory Approach

- a systematic methodology in the social sciences involving the construction of theory through the analysis of data.

- GT method does not aim for the "truth" but to conceptualize what is going on by using empirical research.

- operates almost in a reverse fashion from social science research in the positivist tradition.(Positivism states that positive knowledge is based on natural phenomena and their properties and relations. Thus, information derived from sensory experience, interpreted through reason and logic, forms the exclusive source of all authoritative knowledge. Positivism holds that valid knowledge is found only in this derived knowledge.)

- Unlike positivist research, a study using GT is likely to begin with a question, or even just with the collection of qualitative data.

- As researchers review the data collected, repeated ideas, concepts or elements become apparent, and are tagged with codes, which have been extracted from the data.

- As more data are collected, and as data are re-reviewed, codes can be grouped into concepts, and then into categories. These categories may become the basis for new theory.

- Thus, GT is quite different from the traditional model of research, where the researcher chooses an existing theoretical framework, and only then collects data to show how the theory does or does not apply to the phenomenon under study.

Rationale of GT to be grounded is that this theory helps close the gap between theory and empirical research

GT mainly came into existence when there was a wave of criticism towards the fundamentalist and structuralist theories that were deductive and speculative in nature.

Highly significant in the fields of medical sociology, psychology and psychiatry.

Stages of analysis in GT

Identifying anchors that allow the key points of the data to be gathered
Collections of codes of similar content that allows the data to be grouped
Broad groups of similar concepts that are used to generate a theory
A collection of categories that detail the subject of the research

Some Keywords in GT

Use in various disciplines : Used in Psychology (e.g. to understand the role of therapeutic distance for adult clients with attachment anxiety), sociology, Public Health, Business, Software Engineering, Nursing etc.