Damodar Suar Indian Institute of Technology

Kharagpur 721302 (WB)

Disciplinary Context of Methodological Paradigms

History: Currently, methodological behaviourism has become the dominant paradigm and the QM has turned into the synonym of scientific, objective, and dependable source of knowledge.

Qualitative methods employ case studies, historical methods, in-depth interview, participant observation, participatory rural appraisal, grounded theory, and narratives.

QMs employ mathematics, modelling, meta-analysis, true and quasi experiments, objective assessments, sample surveys, and statistical data analyses.

Both methods have their assets, liabilities and standards of practice. The shortcomings can be upset adopting triangulated or mixed method.

Still there is another called action research, execution of projects, modifying knowledge getting feedback from field.

The methods differ in reasoning, defining reality, emphasis, research objective, focus, types of questions, nature of observation, sample, nature of data, data collection methods, analysis and report preparation. Both Qualitative and QMs are complimentary. A quantitative study is also qualitative because interpretation is common to both.

QMs are favoured because of positivist scientific approach, more precision, large sample and generalisation, availability of computer software.

Steps in Survey Research

Selection of topicObjectives,

Hypotheses, If statements,

Research questions

Constructs,Variables

Questionnaire, Interview schedule

Sampling Data collection

Master-sheet

Pre-test,Pilot survey

Statistical analysis, Mathematical

models Interpretation

Report writing

APPLICATION OF QMs

Review of Literature If the concern of the review is integration and synthesis of

studies examining similar research questions or hypotheses for advancement of knowledge and theory, statistical methods to analyse research literature offer a better choice than the traditional review. The quantitative review or integration of research literature is known as “meta-analysis”.

A meta-analyst can announce in a review topic whether further research is needed by calculating “ fail safe N”.

Few meta-analytic study: Srivastava, 2002, Dutta ,2004, No meta-analytic study up-to 2003 in IPAR.

Suggested Methods that demand minimum assumption about data : 1. Unweighted and weighted Stouffer method for combining independent studies. 2. Effect-size

Variable in used in psychology can be : Categorical variable (CAV),Continuous (quantitative) variable(COV), Independent variable (IV), Dependent variable (DV), Extraneous variable (EV), Moderator variable (MOV), Mediator (intervening) variable (MEV), Active variable (ACV), Attribute variable (ATV).Most of the studies have tested the impact of IV on DV the association among variables. Researchers have frequently incorporated attribute variables depicting psychological characteristics along with socio-demographic variables.

Operationalisation states how a variable is observed, counted or measured. It provides meaning by specifying the operations or activities necessary to measure the variable/construct. It states: do such-and-such in so-and-so manner. Frequently used measurement-oriented operationalisation. Complex Operationalisation: Euclidean distance, identification, index, adding standardized score, adding score in check list.

Conceptual Model

A model is a proposed abstraction of reality. It represents the principles with essential characteristics of behaviour or phenomenon in the real world in a simplified way. Though the complex covert (mental processes) and overt behaviour of the organism are not easy to model quantitatively, higher level of abstraction is necessary for the construction of conceptual models. Models are more precise than verbal descriptions and offer greater manipulability.

Suar (1992), studying the polarisation phenomenon, derived that: R ≥ (Uc-Uf) / (Us-Uf). Using graph theory concepts of nodes or vertices, edges, and ambisidigraph, Acharya and Joshi (2005) have rationalised the various combinations of attraction, repulsion, and indifference among members in small social groups.

Sampling

The power of a sample to produce a close approximation to the population depends on (a) the sample size, (b) the methods by which we draw the sample, and (c) the measurement of non respondent bias.

None of the studies has provided information on the process that leads to sample size decision. Adequate sample size can be rationally determined (a) in advance before conducting the study, (b) applying rules of thumb, and (c) collecting pilot data.

Cohen Table: effect-size, power, alpha levelRule of Thumb: Factorial design: 20 cases per cell, correlation an path

diagram 1:10, reliability:300-400, SEM > 200. Collecting pilot data: n = SD2 X (Z2 / E2)Method: Rarely mentioned, mostly non-probability sampling, Response

Rate, Nonrespondent Bias: The nonrespondent bias is measured either (a)

stating sample representativeness, (b) comparing those who respond and who do not, or (c) early versus late respondents.

Methods of Data Collection

Experimental Research: External validity Presence-absence, amount, type techniques are

followed in pure experimental deign. A close perusal of the reports of these studies

indicates several features. First, attrition of experimental subjects caused due to noncompliance, dropout, and other reasons is not mentioned. Second, researchers have frequently randomized block design, and factorial designs and reported main and interaction effects. Latin square design, and split-plot and repeated measures are hardly used. Lastly, in experiments on human subjects, authors and/or coauthors are the experimenters. They need to disclose what specific methods are adopted to deal with research artifacts.

Experimenter bias and implicit demand on subjects’ performance are research artifacts.

Methods of Data Collection

Double-blind control, computer-based experiments, conduct experiments via Internet. Semi- or quasi-experiments to understand reality.

Non-experimental research: (a) comparative research, and (b) correlational research.

A self-reported questionnaire has become an ubiquitous tool for such research. Checklist, multiple choice, ranking questions are rare. Reliability and validity poor.

PI, C-OAR, Scaling response categories, dolls, ladder, visuals, contextual measuring tools, secondary data use.One main source of measurement error in behavioural investigation is the “common method bias”, variance attributable to measurement method rather than to the construct of interest. It includes the contents of specific items, scale-type, response format, and the general context, or the response biases as hallo effects, social desirability, acquiescence, and leniency effects.

Data Analysis, Assessment and Indigenous PsychologyData Analysis

Data Entry,

Examine Raw Data: Examining the data

statistically or graphically has three basic purposes. First, the researcher gets insight into the basic character of the data, relationships, and differences among variables. Second, missing values, illegal values, and outliers are identified and resolved. Third, the basic assumptions of the statistical methods are identified and compiled with.

The researcher can test the basic assumption in data graphically or statistically that statistical methods demand. The common among them are normality, linearity, homoscedasticity, and multicollinearity.

Data Analysis, Assessment and Indigenous Psychology

Statistical Data Analysis: First, what the investigator is looking for in accordance with objectives, hypotheses, and research questions? Different statistical methods will be used for understanding difference, relationship, prediction, and interaction. Second, are the data from same set of sample or different samples? Depending on sample categories/groups, analyses differ. Third, in which scale are the data of different variables (metric--interval and ratio scale, nonmetric-- ordinal and nominal scale)? Once the questions are replied, appropriate statistical methods may be employed to answer the research questions.

Occasionally used multivariate statistics are multiple regression with dummy variables, canonical correlation, correspondence analysis, cluster analysis, principal component analysis, confirmatory factor analysis, and path analysis. State-of-art method: SEM

AN AGENDA FOR FUTURE Revamp the Course on Research Methodology Contents need inclusion are quantitative reviews; sampling; item-

response theory, and scale construction; content analysis; multivariate statistics of multiple regression analysis; discriminate analysis; conjoint analysis; correspondence analysis; canonical correlation; confirmatory factor analysis; and path analysis. Psychologists treat the real world phenomena as linear and simple which are nonlinear, dynamic, and complex. It is a challenge for us to determine whether the methodologies that have been developed to study dynamic, non-linear, and complex systems can fundamentally advance our understanding of human behaviour.

Analytical and reflective mindset: Pedagogy- lecture and case study

methods. Hands-on-experience and learning by doing in SPSS, SYSTAT, and AMOS with hypothetical data, analysis, and interpretation can boost the confidence of researchers

Training: Allahbad, Utkal, IIT, IIM, Delhi, Calcutta Uni.

Representative Sample, Longitudinal Studies, and New Methods of Data Collection

Longitudinal studies are required on the same sample or cohort groups over an extended period of time repeatedly for understanding, and predicting of individuals’, groups’, and communities’ history, transitions, differences, future expectations, and cumulative effects. It would help testing or generating theories, and formulating public policy.

The tools of interview schedule, semi-projective tests, projective-inventory, visuals, and contextual measuring methods can be used and developed to measure variables. Single case study, which has important bearing in clinical investigation, also deserves our attention.

Use of Available Secondary Data: Census reports, statistical handbooks, national sample survey records, annual reports of companies, Internet, and intranet provide a wealth of data. The substance from the secondary “hard data” can and will definitely supplement to the behavioural soft data.

Data Documentation: A databank, created by a nodal psychology department of the country, will eliminate the collection of underused data, reveal the phenomenon across time, increase the electronic access to data, help doing meta-analysis, and guide policy formulation with evidence on important social issues of poverty, health, education, employment, etc.

Theory-driven Research Integrate Qualitative and QMs, and Multi-

disciplinary Perspective: More complex the psychological issues under investigation, multiple methodologies are required for comprehending and in-depth probing. If the QMs can be applied with participant observation, ethnography, unstructured interview, content analysis, and historical methods, the information base will be rich for advancement of knowledge. An unidisciplinary outlook provides tunnel vision. Integration of multi-disciplinary perspectives can contribute to the fuller understanding of the phenomenon under investigation.

Actual Reality Presumed Reality Hypothesis

Research Design

Data Analysis

Evidence on Observed Reality

Assessment of the

Correspondence between Observed

Reality and Conjectures

about Presumed

Reality

Generation of an Empirical

Fact (Observed Reality)

Model Building

Model building leading to hypothesis specification is done at an early stage of research (conceptual part of research, theory building)

To represent the reality: To what extent observed results depict the reality( empirical part of research, theory testing)

Knowledge: Actual knowledge about reality exists outside. The researcher formulates beliefs about that reality.

The belief statements about the happenings of reality are the basis of conjectures/hypotheses/research questions

Then these are tested collecting data, analysising data, and reporting results.

If the results supports the beliefs, knowledge generated is accepted.

Social science models are in fuzzy state.

H1a +

H1b + + + _ H2a _ + + +

_ H1c + + +

H1d + H3a _ H2 b

_ +

H1e + H3b _ +

+ H1f + + H3c

_ H1g + H1h + H2c

_ + + + Note: ‘+’ indicates positive impact and ‘–’ indicates negative impact

Fig. 1. Conceptual model of antecedents, job burnout, work-related outcomes, and buffers

Job

burnout

Organizational commitment

Role ambiguity

Role conflict

Schedule pressure

Irregular shifts

Pressure from client

interaction

Group non-cooperation

Psychological contract violation

Work-family conflict

Subjective well-being

Social support

Practising yoga and meditation

Work performance

Interpersonal relationships

Professional efficacy

Exhaustion Cynicism

Affective commitment

Normative commitment

Continuance commitment

+ H4a + _ _ _

H5a _ _ +

+ H4b + +

H5b _ _ +

H5c

_ _ _ _ _

H4c + Note: ‘+’ indicates positive impact and ‘–’ indicates negative impact

Fig. 2. Conceptual model of job burnout, health-related outcomes, and buffers

Practising yoga and meditation

Exhaustion

Professional efficacy

Cynicism

Job burnout

Subjective well-being

Social support

Behavioral symptoms

Mental health

Anxiety and depression

Social dysfunction

Loss of confidence

Physical health

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