Definition
Segmentation is the process which identifies and evaluates groups of customers who have
distinctive buying behaviour. Essential principle behind the segmentation process is to make
sense of complex markets where there exists a diversity of needs, wants, drives and choices.
Segmentation strategy includes use of product life-cycle analyses, product-market expansion matrices, portfolio analysis, brand mapping.
Segmentation is a focus through positioning on some particular target markets rather rough
and general direction towards a huge market or towards many segments.
Principles of Segmentation:
1. Segment size: Segment may be small to warrant any special marketing effort.
2. Segmentation information: Buying behaviour, product positioning, psychographs, and
demographics.
3. Segment discrimination: Segmentation aggravates customers so that a diverse market
can be understood in terms of distinctive, homogenous segments.
4. Segmentation exercise: Minimize within group variation and maximize between
group variations.
5. Segmentation overlapping: One segment may overlap with other for some similarity.
Segmentation by Geographic:
Standard planning regions
TV regions
Urban/Rural
Segmentation by demographics: age, sex, ethnic group, family size, family life cycle (age, presence of children, income), income, occupation, social grade, education, religion, nationality.
By life style: It is the learned as a consequence of numerous influences including culture, family, social grade, reference groups, and peer groups. Life style has been referred to as patterns in which people live and spend time and money.
Life style is usually measured by AIO framework-activities, interests and opinions. The appeal of life style is that buying behaviour, particularly for high involvement consumer products. The usual problems with life style are that it lacks clarity because the data are elusive or the method of analysis is obscure and involves a degree of subjectivity that is unacceptable. Another problem is stability of life style. An interesting example of life style analysis is the segment identified as ‘Generation X’ which contains young people often enjoying high disposable income for clothes and entertainment. But ‘Generation X’ is not stable as they are highly self-aware and also highly cynical such that traditional marketing activity is seen as transparent and false.
There are two variables- general (customer size, socio-economic condition) and behavioural (buying habit, buying criteria, frequency of purchase, size of purchase). Behavioural variables act more on marketing.
Problem in methodology
Process of segmentation is concerned with identifying and with evaluating. Market segmentation is multidimensional in nature as consumer is multidimensional. So using one or two single variables can have limited success. It is unlikely that a segment can be identified in terms of usage rate and age.
Market Segmentation Research
Step 1: Define the problem and research objectives- Problem should not be too broad or too
narrow. It should be strategic. Problem may focus on three things- descriptive, associative
and predictive.
Step 2: Develop the research plan- It includes decision on cost of project, data sources, research approaches, research instruments, sampling plan and contact methods.
Data Sources: Primary and secondary data. When secondary data are inaccurate, incomplete, unreliable and out dated, primary data are important.
Research approaches: Observational, focus group, survey research, behavioural data,
experimental research (effect of treatment when extraneous variables are controlled).
Research Instruments:
#Questionnaires: There are different types as dichotomous, multiple choice, summated rating
scale, semantic differential, Word Association, Sentence Completion, Story Completion,
Picture, TAT.
#Psychological tools: Rorschach, depth interviewed.
# Mechanical devices: GSR, Eye-movement test by eye camera, audiometer.
#Qualitative measures: Informal interview ----- at a cafe or bar.
Sampling plan:
Sampling unit, sampling size, sampling procedure-Probability (simple, stratified and cluster)
and non-probability sample (convenient sampling, judgment and quota sampling).
Contact method:
Mail questionnaire, personal interviewing, online method.
Collection of data
Analyze the information
Present the findings
Make the decision
Lecture notes of D. Dutta Roy
Monday, May 18, 2015
Tuesday, April 14, 2015
Scale Construction and Validation for IIM, Kolkata
Things are going to happen much faster in the future and only those who will keep up with the fast pace will stay alive in the 21st century. -Alvin Toffler
- Questionnaire provides in-depth information about social cognition. It gives us knowledge with much consistency about specific dimensions on which individual in society perceives the social change.
- Business goals are based on both closed and open system. Success lies on perception and efficacy of business units . Questionnaire provides knowledge about it.
Wednesday, April 1, 2015
Advance Diploma OF PATC
Broadly Psychotherapy can be classified from two perspectives - size and time. From size, individual and group therapy and from time perspective, it is short and long term therapy. Usually, short term therapy is limited within 10 therapeutic sessions.
There are different types of therapy as Client-centered therapy, transactional, cognitive, cognitive behavour and Rabindrik Psychotherapy.
Client-centered therapy: The goal of CCT is to provide clients with an opportunity to develop a sense of self where they can realize how their attitudes, feelings and behavior are being negatively affected. Other names are Person-centered therapy, person-centered psychotherapy, person-centered counseling, client-centered therapy and Rogerian psychotherapy. It is a form of talk-psychotherapy developed by psychologist Carl Rogers in the 1940s and 1950s.
Transactional analysis is a theory in psychology that examines the interactions, or 'transactions', between a person and other people. The underlying precept is that humans are social creatures and that a person is a multi-faceted being that changes when in contact with another person in their world. Canadian-born US psychiatrist Eric Berne developed the concept and paradigm of TA in the late 1950s.
Cognitive therapy: It is based on the cognitive model, which states that thoughts, feelings and behavior are all connected, and that individuals can move toward overcoming difficulties and meeting their goals by identifying and changing unhelpful or inaccurate thinking, problematic behavior, and distressing emotional responses. This involves the individual working collaboratively with the therapist to develop skills for testing and modifying beliefs, identifying distorted thinking, relating to others in different ways, and changing behaviors.
Therapy may consist of testing the assumptions which one makes and looking for new information that could help shift the assumptions in a way that leads to different emotional or behavioral reactions. Change may begin by targeting thoughts (to change emotion and behavior), behavior (to change feelings and thoughts), or the individual's goals (by identifying thoughts, feelings or behavior that conflict with the goals). Beck initially focused on depression and developed a list of "errors" in thinking that he proposed could maintain depression, including arbitrary inference, selective abstraction, over-generalization, and magnification (of negatives) and minimization (of positives).
Cognitive behavour therapy: Cognitive behavioral therapy (CBT) is a short-term psychotherapy works to solve current problems and change unhelpful thinking and behavior.The name refers to behavior therapy, cognitive therapy, and therapy based upon a combination of basic behavioral and cognitive principles.Most therapists working with patients dealing with anxiety and depression use a blend of cognitive and behavioral therapy. This technique acknowledges that there may be behaviors that cannot be controlled through rational thought, but rather emerge based on prior conditioning from the environment and other external and/or internal stimuli. CBT is "problem focused" (undertaken for specific problems) and "action oriented" (therapist tries to assist the client in selecting specific strategies to help address those problems),or directive in its therapeutic approach.
ASSESSMENT:
Thursday, March 26, 2015
Psychographic analysis for Amrita University
17.2.2012
In Marketing research one of the major problem is to classify the customers as marketing has to determine which segments offer the best opportunities. The process of dividing a market into distinct groups of buyers who have different needs, characteristics or behaviors, who might require separate products or marketing program, is called market segmentation.
For classification, generally, demographic data are used. It is noted that some psychological factors play critical roles in changing consumer's attitude. These are intangible variables such as need profile, personality, interests, values and life styles of potential customer. For example purchasing speedy motor car depends not only on socioeconomic condition but also one's risk taking personality trait.
The idea is that marketers can sell the product to enhance the life style of the consumers. For example, by defining bathing style of customers using bar soap, marketers push liquid soap to enhance life style of the consumers. In life style analysis, marketing researcher should study all the activities of consumers - working, shopping, holiday and social life. In analysis of behaviour, followings will be taken into account as End use, Benefits sought, loyalty, usage rate etc.Alexandre Psychographic profile data can be used for brand development
Psychographic data provide market intelligence to the company. Based on psychographic segmentation, company can modify it's width or depth of business. It is the process of exploring business opportunities or to identify specific opportunities for cross sell or up sell.
LIFE STYLE
Cell phone research:
How often
Collection from study.com
Psychographic data are important for market segmentation so that market potential across different segmented markets can be understood. Most marketing departments use multiple segmentation strategies.
Multiple Segmentation Strategies
Geography
Demographics
Psychographics
Benefits sought
Usage rate
Geographic Segmentation
One of the first variables that the team could use in their segmentation strategy is geographic. This would allow the team to break the market into sections by climate, density, market size, world or states. Many companies use climate if their products or services rely on the weather, such as snow shovels, melting pavement salt, wave runners and boats. Our Town USA is more interested in targeting geographic locations that are located near the park in a 100-mile radius. They believe some customers will fly in from out of state, so in addition, they will target large-density areas nearby.
Demographic Segmentation
Demographic segmentation is extremely important to all marketing departments since the data is easily available and does drastically affect buying patterns. Age, income, gender, ethnic background and family life cycle are all important factors of demographic segmentation. The park is going to use an age range of 2-60 years of age so they can include kids, teens, parents and even grandparents. The income level would have to be middle to upper class - $50,000 annual income or above - since park tickets are very expensive. The amusement park is not a gender-specific product, and ethnicity will also not affect the overall plan.
The marketing team is very interested in the family life cycle sub-segments. Family life cycle segmentation is a series of stages determined by a combination of age, marital status and the number of children in a household. Obviously, the park is very interested in the family life cycle of young single, young married with kids, middle-aged married with kids, young divorced with children and middle-aged divorced with kids. They plan on advertising via social media and local cable ads where parents and kids congregate.
RESEARCH DESIGN
In this analysis, the dependent variables are
how frequently customers purchase a given item, how much they spend on the item per year, and what factors cause them to purchase the item.
Some useful links are:
http://www.ehow.com/info_8244606_psychographic-data-marketing.html
Lifestyle psychographic:http://www.warc.com/fulltext/esomar/80217.htm
Big-5: http://digitalmarketingmagazine.co.uk/digital-marketing-features/psychographic-profiling-identifying-new-levels-of-customer-understanding/787
Substance abuse : http://www.ncbi.nlm.nih.gov/pubmed/24729744
In Marketing research one of the major problem is to classify the customers as marketing has to determine which segments offer the best opportunities. The process of dividing a market into distinct groups of buyers who have different needs, characteristics or behaviors, who might require separate products or marketing program, is called market segmentation.
For classification, generally, demographic data are used. It is noted that some psychological factors play critical roles in changing consumer's attitude. These are intangible variables such as need profile, personality, interests, values and life styles of potential customer. For example purchasing speedy motor car depends not only on socioeconomic condition but also one's risk taking personality trait.
The idea is that marketers can sell the product to enhance the life style of the consumers. For example, by defining bathing style of customers using bar soap, marketers push liquid soap to enhance life style of the consumers. In life style analysis, marketing researcher should study all the activities of consumers - working, shopping, holiday and social life. In analysis of behaviour, followings will be taken into account as End use, Benefits sought, loyalty, usage rate etc.Alexandre Psychographic profile data can be used for brand development
Psychographic data provide market intelligence to the company. Based on psychographic segmentation, company can modify it's width or depth of business. It is the process of exploring business opportunities or to identify specific opportunities for cross sell or up sell.
LIFE STYLE
Cell phone research:
How often
- do you send text message?
- do you talk on cell phone ?
- do you use social networking site ?
- do you meet person outside your school work ?
- do you often talk on landline phone ?
- do you use e-mail?
Collection from study.com
Psychographic data are important for market segmentation so that market potential across different segmented markets can be understood. Most marketing departments use multiple segmentation strategies.
Multiple Segmentation Strategies
Geography
Demographics
Psychographics
Benefits sought
Usage rate
Geographic Segmentation
One of the first variables that the team could use in their segmentation strategy is geographic. This would allow the team to break the market into sections by climate, density, market size, world or states. Many companies use climate if their products or services rely on the weather, such as snow shovels, melting pavement salt, wave runners and boats. Our Town USA is more interested in targeting geographic locations that are located near the park in a 100-mile radius. They believe some customers will fly in from out of state, so in addition, they will target large-density areas nearby.
Demographic Segmentation
Demographic segmentation is extremely important to all marketing departments since the data is easily available and does drastically affect buying patterns. Age, income, gender, ethnic background and family life cycle are all important factors of demographic segmentation. The park is going to use an age range of 2-60 years of age so they can include kids, teens, parents and even grandparents. The income level would have to be middle to upper class - $50,000 annual income or above - since park tickets are very expensive. The amusement park is not a gender-specific product, and ethnicity will also not affect the overall plan.
The marketing team is very interested in the family life cycle sub-segments. Family life cycle segmentation is a series of stages determined by a combination of age, marital status and the number of children in a household. Obviously, the park is very interested in the family life cycle of young single, young married with kids, middle-aged married with kids, young divorced with children and middle-aged divorced with kids. They plan on advertising via social media and local cable ads where parents and kids congregate.
RESEARCH DESIGN
In this analysis, the dependent variables are
how frequently customers purchase a given item, how much they spend on the item per year, and what factors cause them to purchase the item.
Some useful links are:
http://www.ehow.com/info_8244606_psychographic-data-marketing.html
Lifestyle psychographic:http://www.warc.com/fulltext/esomar/80217.htm
Big-5: http://digitalmarketingmagazine.co.uk/digital-marketing-features/psychographic-profiling-identifying-new-levels-of-customer-understanding/787
Substance abuse : http://www.ncbi.nlm.nih.gov/pubmed/24729744
Thursday, March 12, 2015
SPSS training for Doctoral students of Psychology, University of Calcutta
GENERAL OUTLINE
SPSS is the statistical package for scientific researches in social sciences like psychology, sociology, economics and in engineering sciences. In social sciences, there are large number of variables and cases. Specific distribution of variable or case or association among set of variables or cases can be examined by SPSS.
SPSS provides both menu and syntax driven approaches in analysis of data. In menu driven approach, researcher uses the icons of the SPSS tool bars. In syntax approach, researcher writes the programme in the syntax window and runs it for the output. Syntax approach is always better than menu driven,as researcher gets freedom to analysed the variables. Syntax archive helps researchers in locating specific files and analysis of data.
There are several function in SPSS - file management, variable creation, variable transformation,data visualization,and analysis of text and numeric data.
File-Management:
SPSS accepts both MS-Exel and Text file as input and Spss output can be inserted in the MS- office files.
Variable Creation : New variable can be created in SPSS following specific scales of measurements.Saved output variables can be inserted in the original files.
Variable Transformation :Variable properties can be transformed from text to numeric or vise - versa.One numeric property of variable can be transformed another numeric through SPSS, for the same, one can create new transformed variable or replace original variable through recode into same or different variables.New variable can be created by manipulating more numbers of variables.
Case/ variable Selection: Single or multiple cases can be analysed through select cases or if command.Similarly descriptive statistics of single variables or set of variables can be extracted through SPSS.
Data Visualisation: Visual display of data is useful in examining data quality.This is possible in SPSS.Structured or unstructured , large text and numeric data can be summerized an visualised through graphs and tables.
Outlier Detection: Presence of outlier or the extreme data affects the distribution of data adversely.This is specifically dangerous for pervasive statistics like correlation. Through box-whisker plot location of extreme data can be identified and be manipulated to serve the quality of data.
http://www.slideshare.net/ddroy/box-whisker-show
Analysis of data,SPSS is useful for non- parametric and parametric statistics.
ANOVA: In SPSS, ANOVA follows few nomenclatures. Independent variables is called factor. This is categorical in nature. This is determined by experimental membership. Dependent list includes list of dependent variables. These are metric or scaled variables. Results provide three sums of squares - between, within and total sums of squares; degrees of freedom, mean square, F ratio and level of significance. When more than one IV interact with each other to change in DV, it extracts interaction sum of squares.
The effects on DV are of two types - fixed and random effects.
Random effects cause errors. It usually happens in sampling errors, instrumental errors and environmental errors. Confidence intervals are accounted in accepting regions of random effects.
Non-parametric ANOVA : Kruskal-Wallis H test is used when original data set actually consists of one nominal variable and one ranked variable. In SPSS, it is not important to rank the scaled data when scaled data are dependent variables. SPSS provides Mean rank, chi-square, df and significance level for interpreting the result.
see more:http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm
Regression : http://dss.princeton.edu/online_help/analysis/regression_intro.htm
SPSS is the statistical package for scientific researches in social sciences like psychology, sociology, economics and in engineering sciences. In social sciences, there are large number of variables and cases. Specific distribution of variable or case or association among set of variables or cases can be examined by SPSS.
SPSS provides both menu and syntax driven approaches in analysis of data. In menu driven approach, researcher uses the icons of the SPSS tool bars. In syntax approach, researcher writes the programme in the syntax window and runs it for the output. Syntax approach is always better than menu driven,as researcher gets freedom to analysed the variables. Syntax archive helps researchers in locating specific files and analysis of data.
There are several function in SPSS - file management, variable creation, variable transformation,data visualization,and analysis of text and numeric data.
File-Management:
SPSS accepts both MS-Exel and Text file as input and Spss output can be inserted in the MS- office files.
Variable Creation : New variable can be created in SPSS following specific scales of measurements.Saved output variables can be inserted in the original files.
Variable Transformation :Variable properties can be transformed from text to numeric or vise - versa.One numeric property of variable can be transformed another numeric through SPSS, for the same, one can create new transformed variable or replace original variable through recode into same or different variables.New variable can be created by manipulating more numbers of variables.
Case/ variable Selection: Single or multiple cases can be analysed through select cases or if command.Similarly descriptive statistics of single variables or set of variables can be extracted through SPSS.
Data Visualisation: Visual display of data is useful in examining data quality.This is possible in SPSS.Structured or unstructured , large text and numeric data can be summerized an visualised through graphs and tables.
Outlier Detection: Presence of outlier or the extreme data affects the distribution of data adversely.This is specifically dangerous for pervasive statistics like correlation. Through box-whisker plot location of extreme data can be identified and be manipulated to serve the quality of data.
http://www.slideshare.net/ddroy/box-whisker-show
Analysis of data,SPSS is useful for non- parametric and parametric statistics.
ANOVA: In SPSS, ANOVA follows few nomenclatures. Independent variables is called factor. This is categorical in nature. This is determined by experimental membership. Dependent list includes list of dependent variables. These are metric or scaled variables. Results provide three sums of squares - between, within and total sums of squares; degrees of freedom, mean square, F ratio and level of significance. When more than one IV interact with each other to change in DV, it extracts interaction sum of squares.
The effects on DV are of two types - fixed and random effects.
Random effects cause errors. It usually happens in sampling errors, instrumental errors and environmental errors. Confidence intervals are accounted in accepting regions of random effects.
Non-parametric ANOVA : Kruskal-Wallis H test is used when original data set actually consists of one nominal variable and one ranked variable. In SPSS, it is not important to rank the scaled data when scaled data are dependent variables. SPSS provides Mean rank, chi-square, df and significance level for interpreting the result.
see more:http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm
Regression : http://dss.princeton.edu/online_help/analysis/regression_intro.htm
Monday, February 16, 2015
Rabindrik Psychotherapy : Abstract of my presentation at the Rotary Club, Kolkata
Rabindrik Psychotherapy
D. Dutta Roy
Invited lecture at Rotary Club, Kolkata
16.2.2015
Abstract
Rabindrik psychotherapy refers to the way of self-awakening with therapeutic postulates extracted from the literary works of Rabindranath Tagore. It is the paradigm shift in treatment of psychological disorders. When classical psychotherapy emphasizes on labelling the disorders, Rabindrik psychotherapy is free from labeling. When classical psychotherapy emphasizes on talk therapy, Rabindrik psychotherapy gives stress on performing arts therapy. Both tend to reconstruct the disequilibrium states of consciousness through therapeutic customization. In classical psychotherapy, psychotherapist plays active role. But in Rabindrik psychotherapy, psychotherapist facilitates the environment and client him or her self can customize the situation through Rabindrik performing arts. Therefore, in classical psychotherapy, client assumes self as patient or client but in Rabindrik psychotherapy client never labels him or herself. In Rabindrik psychotherapy, client can not think how he or she is recovered.
D. Dutta Roy
Invited lecture at Rotary Club, Kolkata
16.2.2015
Abstract
Rabindrik psychotherapy refers to the way of self-awakening with therapeutic postulates extracted from the literary works of Rabindranath Tagore. It is the paradigm shift in treatment of psychological disorders. When classical psychotherapy emphasizes on labelling the disorders, Rabindrik psychotherapy is free from labeling. When classical psychotherapy emphasizes on talk therapy, Rabindrik psychotherapy gives stress on performing arts therapy. Both tend to reconstruct the disequilibrium states of consciousness through therapeutic customization. In classical psychotherapy, psychotherapist plays active role. But in Rabindrik psychotherapy, psychotherapist facilitates the environment and client him or her self can customize the situation through Rabindrik performing arts. Therefore, in classical psychotherapy, client assumes self as patient or client but in Rabindrik psychotherapy client never labels him or herself. In Rabindrik psychotherapy, client can not think how he or she is recovered.
¬ Rabindrik psychotherapy assumes that
consciousness is composed of three layers - Murta, Raag and Saraswat. Attention, sensation, perception are
adversely affected when murta layer is deformed or
disoriented. Feelings and emotions affect our cognitions in Raag layer. Saraswat layer minimizes errors in
cognition, emotion and strengthens harmony with environment. Saraswat layer provides feeling of peace,
and negative entropy. The layers are dynamic. Dynamicity is determined by
the changes in the physical environment or in the psychological field. Any
change causes vibration and vibration starts to move across layers. Impact of
vibration on consciousness depends on the strength, duration, properties of
vibrating agents, resiliency, and acceptability of consciousness. Rabindrik therapist customizes dynamicity
in the consciousness through positive vibration followed by consciousness
diagnostics. Rabindrik psychotherapist must be
proficient with (a) consciousness layer dynamics, (b) therapeutic postulates,
(c) customization and precautions in therapy. This lecture will focus on
these three things with case studies.
Saturday, December 27, 2014
Statistics and Research Methodology in Clinical Psychology
PAPER - III: Statistics and Research Methodology
Aim:
The aim of this paper is to elucidate various issues involved in conduct of a sound
experiment/survey. With suitable examples from behavioral field, introduce the trainees to the
menu of statistical tools available for their research, and to develop their understanding of the
conceptual bases of these tools. Tutorial work will involve exposure to the features available in a
large statistical package (SPSS) while at the same time reinforcing the concepts discussed in
lectures.
Objectives:
By the end of Part – II, trainees are required to demonstrate ability to:
1. Understand the empirical meaning of parameters in statistical models
2. Understand the scientific meaning of explaining variability
3. Understand experimental design issues - control of unwanted variability, confounding and bias.
4. Take account of relevant factors in deciding on appropriate methods and instruments to use in
specific research projects.
5. Understand the limitations and shortcomings of statistical models
6. Apply relevant design/statistical concepts in their own particular research projects.
7. Analyze data and interpret output in a scientifically meaningful way
8. Generate hypothesis/hypotheses about behavior and prepare a research protocol outlining the
methodology for an experiment/survey.
9. Critically review the literature to appreciate the theoretical and methodological issues involved.
RCI M.Phil Clinical Psychology Revised Syllabus 2009 50Academic Format of Units:
The course will be taught mainly in a mixed lecture/tutorial format, allowing trainees to participate
in collaborative discussion. Demonstration and hands-on experience with SPSS program are
desired activities.
Evaluation:
Theory - involving long and short essays, and problem-solving exercises
Syllabus:
Unit - I: Introduction: Various methods to ascertain knowledge, scientific method and its features;
problems in measurement in behavioral sciences; levels of measurement of psychological variables
- nominal, ordinal, interval and ratio scales; test construction - item analysis, concept and methods
of establishing reliability, validity and norms.
Unit - II: Sampling: Probability and non-probability; various methods of sampling - simple random,
stratified, systematic, cluster and multistage sampling; sampling and non-sampling errors and
methods of minimizing these errors.
Unit - III: Concept of probability: Probability distribution - normal, poisson, binomial; descriptive
statistics - central tendency, dispersion, skewness and kurtosis.
Unit - IV: Hypothesis testing: Formulation and types; null hypothesis, alternate hypothesis, type I
and type II errors, level of significance, power of the test, p-value. Concept of standard error and
confidence interval.
Unit - V: Tests of significance - Parametric tests: Requirements, "t" test, normal z-test, and "F" test
including post-hoc tests, one-way and two-way analysis of variance, analysis of covariance,
repeated measures analysis of variance, simple linear correlation and regression.
Unit – VI: Tests of significance - Non-parametric tests: Requirements, one sample tests – sign test,
sign rank test, median test, Mc Nemer test; two-sample test – Mann Whitney U test, Wilcoxon rank
sum test, Kolmogorov-Smirnov test, normal scores test, chi-square test; k sample tests - Kruskal
Wallies test, and Friedman test, Anderson darling test, Cramer-von Mises test.
Unit - VII: Experimental design: Randomization, replication, completely randomized design,
randomized block design, factorial design, crossover design, single subject design, non-
experimental design.
Unit - VIII: Epidemiological studies: Prospective and retrospective studies, case control and
cohort studies, rates, sensitivity, specificity, predictive values, Kappa statistics, odds ratio, relative
risk, population attributable risk, Mantel Haenzel test, prevalence, and incidence. Age specific,
disease specific and adjusted rates, standardization of rates. Tests of association, 2 x 2 and row x
column contingency tables.
Unit - IX: Multivariate analysis: Introduction, Multiple regression, logistic regression, factor
analysis, cluster analysis, discriminant function analysis, path analysis, MANOVA, Canonical
correlation, and Multidimensional scaling.
Unit - X: Sample size estimation: Sample size determination for estimation of mean, estimation of
proportion, comparing two means and comparing two proportions.
Unit - XI: Qualitative analysis of data: Content analysis, qualitative methods of psychosocial
research.
Unit - XII: Use of computers: Use of relevant statistical package in the field of behavioral science
and their limitations.
Essential References:
Research Methodology, Kothari, C. R. (2003). Wishwa Prakshan: New Delhi
Foundations of Behavioral Research, Kerlinger, F.N. (1995). Holt, Rinehart & Winston: USA
RCI M.Phil Clinical Psychology Revised Syllabus 2009 52Understanding Biostatistics, Hassart, T.H.
(1991). Mosby Year Book
Biostatistics: a foundation for analysis in health sciences, 8th ed, Daniel, W.W. (2005). John
Wiley and sons: USA
Multivariate analysis: Methods & Applications, Dillon, W.R. & Goldstein, M. (1984), John
Wiley & Sons: USA
Non-parametric statistics for the behavioral sciences, Siegal, S & Castellan, N.J. (1988).
McGraw Hill: New Delhi
Qualitative Research: Methods for the social sciences, 6th ed, Berg, B.L. (2007). Pearson
Education, USA
Aim:
The aim of this paper is to elucidate various issues involved in conduct of a sound
experiment/survey. With suitable examples from behavioral field, introduce the trainees to the
menu of statistical tools available for their research, and to develop their understanding of the
conceptual bases of these tools. Tutorial work will involve exposure to the features available in a
large statistical package (SPSS) while at the same time reinforcing the concepts discussed in
lectures.
Objectives:
By the end of Part – II, trainees are required to demonstrate ability to:
1. Understand the empirical meaning of parameters in statistical models
2. Understand the scientific meaning of explaining variability
3. Understand experimental design issues - control of unwanted variability, confounding and bias.
4. Take account of relevant factors in deciding on appropriate methods and instruments to use in
specific research projects.
5. Understand the limitations and shortcomings of statistical models
6. Apply relevant design/statistical concepts in their own particular research projects.
7. Analyze data and interpret output in a scientifically meaningful way
8. Generate hypothesis/hypotheses about behavior and prepare a research protocol outlining the
methodology for an experiment/survey.
9. Critically review the literature to appreciate the theoretical and methodological issues involved.
RCI M.Phil Clinical Psychology Revised Syllabus 2009 50Academic Format of Units:
The course will be taught mainly in a mixed lecture/tutorial format, allowing trainees to participate
in collaborative discussion. Demonstration and hands-on experience with SPSS program are
desired activities.
Evaluation:
Theory - involving long and short essays, and problem-solving exercises
Syllabus:
Unit - I: Introduction: Various methods to ascertain knowledge, scientific method and its features;
problems in measurement in behavioral sciences; levels of measurement of psychological variables
- nominal, ordinal, interval and ratio scales; test construction - item analysis, concept and methods
of establishing reliability, validity and norms.
Unit - II: Sampling: Probability and non-probability; various methods of sampling - simple random,
stratified, systematic, cluster and multistage sampling; sampling and non-sampling errors and
methods of minimizing these errors.
Unit - III: Concept of probability: Probability distribution - normal, poisson, binomial; descriptive
statistics - central tendency, dispersion, skewness and kurtosis.
Unit - IV: Hypothesis testing: Formulation and types; null hypothesis, alternate hypothesis, type I
and type II errors, level of significance, power of the test, p-value. Concept of standard error and
confidence interval.
Unit - V: Tests of significance - Parametric tests: Requirements, "t" test, normal z-test, and "F" test
including post-hoc tests, one-way and two-way analysis of variance, analysis of covariance,
repeated measures analysis of variance, simple linear correlation and regression.
Unit – VI: Tests of significance - Non-parametric tests: Requirements, one sample tests – sign test,
sign rank test, median test, Mc Nemer test; two-sample test – Mann Whitney U test, Wilcoxon rank
sum test, Kolmogorov-Smirnov test, normal scores test, chi-square test; k sample tests - Kruskal
Wallies test, and Friedman test, Anderson darling test, Cramer-von Mises test.
Unit - VII: Experimental design: Randomization, replication, completely randomized design,
randomized block design, factorial design, crossover design, single subject design, non-
experimental design.
Unit - VIII: Epidemiological studies: Prospective and retrospective studies, case control and
cohort studies, rates, sensitivity, specificity, predictive values, Kappa statistics, odds ratio, relative
risk, population attributable risk, Mantel Haenzel test, prevalence, and incidence. Age specific,
disease specific and adjusted rates, standardization of rates. Tests of association, 2 x 2 and row x
column contingency tables.
Unit - IX: Multivariate analysis: Introduction, Multiple regression, logistic regression, factor
analysis, cluster analysis, discriminant function analysis, path analysis, MANOVA, Canonical
correlation, and Multidimensional scaling.
Unit - X: Sample size estimation: Sample size determination for estimation of mean, estimation of
proportion, comparing two means and comparing two proportions.
Unit - XI: Qualitative analysis of data: Content analysis, qualitative methods of psychosocial
research.
Unit - XII: Use of computers: Use of relevant statistical package in the field of behavioral science
and their limitations.
Essential References:
Research Methodology, Kothari, C. R. (2003). Wishwa Prakshan: New Delhi
Foundations of Behavioral Research, Kerlinger, F.N. (1995). Holt, Rinehart & Winston: USA
RCI M.Phil Clinical Psychology Revised Syllabus 2009 52Understanding Biostatistics, Hassart, T.H.
(1991). Mosby Year Book
Biostatistics: a foundation for analysis in health sciences, 8th ed, Daniel, W.W. (2005). John
Wiley and sons: USA
Multivariate analysis: Methods & Applications, Dillon, W.R. & Goldstein, M. (1984), John
Wiley & Sons: USA
Non-parametric statistics for the behavioral sciences, Siegal, S & Castellan, N.J. (1988).
McGraw Hill: New Delhi
Qualitative Research: Methods for the social sciences, 6th ed, Berg, B.L. (2007). Pearson
Education, USA
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