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Research Methods for the Social Sciences Lorne Campbell Christopher J. Wilbur University of Western Ontario Philosophy of Human Behavior • (1) Behavior is influenced by outside circumstances – Experimental approach • (2) Behavior is influenced by the qualities possessed by the individual – Correlational approach Interactionist Perspective • Behavior is a function of both context and individual differences – E.g., extraversion and social dominance Types of Research Methods • Runkel and McGrath – Developed a circumplex model to describe the goals of the research process, and the basic types of research methods available – Helps structure our thinking of the types of methods available, and the pros/cons of each type of method B Obtrusive Research Operations Laboratory Experiments II Judgment Tasks Experimental Simulations II Field Experiments III Sample Surveys I III I IV Unobtrusive Research Operations Field Studies IV C Formal Theory Computer Simulations A Universal Behavioral Systems Particular Behavioral Systems Benefits of Multi-Method Research • Mono-operation bias – When using the same method time after time, your research suffers from the same set of limitations – Using different methods to address the same question(s) helps overcome the limitation of each method • E.g., research on self-concept Inferential Statistics • Inferential statistics are usually preferred to simply looking at differences because we can conclude with more certainty that the difference accurately characterizes the population Inferential Statistics Inferential Statistics • Inferential statistics are usually preferred to simply looking at differences because we can conclude with more certainty that the difference accurately characterizes the population • Is this difference a true difference in the general population or just a random effect based on the particular sample? Inferential Statistics • Based on analysis of samples, we can make generalizations about the population of interest • • H0: Sleep deprivation does not impair performance H1: Sleep deprivation does impair performance • Compare two groups on performance measure • If a mean difference emerges that is unlikely by chance alone, we assume this difference is accurate of the population Three Basic Statistical Methods 1) T-Test 2) Analysis of Variance (ANOVA) 3) Multiple Regression Three Basic Statistical Methods 2) Analysis of Variance (ANOVA) • Categorical data (i.e., experimental conditions, demographic data) • Between-subjects or within-subjects • Can compare 3 or more conditions or groups • Can examine interactive effects of multiple variables Three Basic Statistical Methods 2) Analysis of Variance (ANOVA) 7 6 Outcome Score 5 4 Control 3 Experimental 2 1 0 Men Women Three Basic Statistical Methods 2) Analysis of Variance (ANOVA) • Examples • Psychology experiments • Voter intentions • Geographical differences Three Basic Statistical Methods 3) Multiple Regression • Continuous data • But can also handle categorical data (subsumes ANOVA) • Y = b0 + b1X1 + b2X2 + ... + bkXk Three Basic Statistical Methods 3) Multiple Regression • Y = b0 + b1X1 + b2X2 + b3X1X2 Three Basic Statistical Methods 3) Multiple Regression Advanced Statistical Techniques • Structural Equation Modeling Advanced Statistical Techniques • Structural Equation Modeling Risky Sexual Behavior Extraversion Talkative Daring Friendly Condom Use STI Testing Casual Sex Advanced Statistical Techniques • Structural Equation Modeling • Hierarchical Linear Modeling Advanced Statistical Techniques • Hierarchical Linear Modeling Tbilisi State University Ilya Chavchavdze University University of Western Ontario Teaching Method A Teaching Method A Teaching Method A Teaching Method B Teaching Method B Teaching Method B Teaching Method A Teaching Method A Teaching Method A Teaching Method B Teaching Method B Teaching Method B Teaching Statistics • Bachelor’s Level • Year 2 • • Introductory statistics course (mathematical; probabilities, logic of inferential statistics, t-tests, ANOVA, correlation/regression, some other assorted tests) Year 3 • Advanced statistics course (logical; logic of the tests; application of the tests with SPSS) Teaching Statistics • Masters Level • Year 1 • • Advanced statistics course (refreshing and extending; large focus on ttests, ANOVA, and correlation/multiple regression) Beyond • Specialized courses in advanced topics (e.g., factor analysis, SEM, HLM, etc.) How I have taught undergraduate courses on research methods • Brief version of syllabus • Week 1 – introduction to course • Week 2 – Validity and Reliability – Validity • Construct • Internal • External – Reliability • Psychometric properties of scales • Week 3 – Experimental design and the significance testing debate • Week 4 – Quasi-experimental designs – E.g., regression discontinuity design, field experiment • Week 5 – Field Studies, simulation methods – E.g., research by Doug Kenrick • Week 6 – Diary research • Week 7 – Multilevel modelling • Week 8 – Dyadic data (collection and analysis) • Week 9 – Social Relations Model (SRM) – E.g, loneliness study • Week 10 – Mediation and Moderation • Week 11 – Methods in Social Cognition – E.g., AMP model • Week 12 – Meta-analysis • Week 13 – Research Ethics