Research Methods in Organizational Psychology pptx

Chap#2 Research Methods in Organizational Psychology

1. Research Methods in I/O Psychology Chapter 2

2. What is Science? A process or method for generating a body of knowledge Represents a logic of inquiry Primary objective is theory building Is psychology a science? Yes! It relies on formal, systematic observation to answer questions about behavior

3. Goals of Science Description Accurate portrayal/depiction of phenomenon Explanation Gathering info about “why” phenomenon exists Prediction Anticipate an event prior to its occurrence Control Manipulation of conditions to affect behavior

4. Assumptions of Science Em p iricism Determinism Discoverability

5. What Is Theory? Interrelated constructs (concepts), definitions, and propositions that present a systematic view of a phenomenon by specifying relations among variables, with the purpose of explaining and predicting the phenomenon What makes a theory good?

6. What Makes a Good Theory? Parsimonious Explains a lot, yet simple Precision Specific and accurate in its wording Testability Verifiable by experimentation/study

7. What Makes a Good Theory? (continued) Useful Practical, helpful in describing, explaining, and predicting important phenomenon Generativity Stimulates additional research Efficient Produces value and saves resources

8. Is the best theory the one that is most right? All theories (or models) are wrong!!! But some are useful Theories are “simplifications” or “abstractions” They are important because they allow us to do work… Not trying to build an exact “replication” but rather an efficient “model”…

9. Cyclical Inductive–Deductive Model of Research Induction : data  theory Deduction : theory  data Most research is driven by the deductive process, but it is also possible to start with data There is no perfect way to “do science”

10. Cyclical Inductive–Deductive Model of Research

11. Research Ethics Ethical Principles of Psychologists and Code of Conduct of the APA Research must be approved by the supporting institution (e.g., university); Institutional Review Board (IRB) Informed consent : providing study info, right to decline, risks/benefits, questions Confidentiality of participant data Avoiding the use of deception Requirements for care/use of animals

12. Nazi Experiments Nazi scientists and physicians performed many horrific experiments on humans without consent. For instance, they inflicted wounds and infections to observe the effects. They also administered experimental drugs without permission. Many of these experiments were made public in the Nuremberg trials (1945- 1949).

13. Research Terminology and Basic Concepts: Overview Causal inference can be made when data indicate that a causal relationship between two variables is likely Can never prove causality due to other variables Key Terms: Independent variable (IV) Dependent variable (DV) Extraneous variable

14. Variables Independent variable : anything that is systematically manipulated Predictor, precursor, or antecedent Dependent variable : what an experiment is designed to assess Criteria, outcome, or consequence Extraneous variable : any other variable that can contaminate results

15. Internal and External Validity Internal validity : extent to which causal inferences can be drawn about variables Ruling out alternative explanations External validity : extent to which results generalize to other people, settings, time Student participants and “real world” applicability *Important trade-off between internal and external validity: control vs. generalizability

16. C o ntrol Important to ensure that a causal inference can be made about the effect of the IV on the DV Ways to control: Hold extraneous variables constant Systematically manipulate different levels of extraneous variables (make part of experimental design) Statistical control

17. Stage Model of the Research Process Formulate the hypothesis Design the study Collect data Analyze the data Report the findings

18. Types of Research Designs: Overview Experimental methods Lab experiments Field and quasi-experiments Observational methods

19. Types of Research Designs: Experimental Methods Two factors characterize experimental methods: Random Assignment Each participant has an equally likely chance of being assigned to each condition Manipulation Systematic control of an independent variable *These two techniques increase internal validity

20. Types of Research Designs: Lab Experiments Random assignment and manipulation of IVs are used to increase control Take place in a contrived setting for control Very high internal validity External validity is questioned

21. Types of Research Designs: Field and Quasi-Experiments Field experiments Random assignment and manipulation in a realistic field setting Quasi-experiments (very common in I/O) Field experiment w/o random assignment Not always practical to randomly assign participants; use of intact groups

22. Types of Research Designs: Observational Methods Observational methods : also called correlational designs, descriptive research Do not involve random assignment or manipulation Make use of available resources Can draw conclusions about relationships but NOT causality Common in field settings

23. Data Collection Techniques: Overview Naturalistic observation Case studies Archival research Surveys

24. Data Collection Techniques: Naturalistic Observation Observation of someone or something in its natural environment Participant observation – observer tries to “blend in” completely with those who are observed Unobtrusive observation – observer objectively observes individuals without drawing attention to him/herself; does not blend in completely

25. Data Collection Techniques: Case Studies Examination of single individuals, groups, companies or societies Main purpose is description; explanation is also a reasonable goal Not typically used to test hypotheses Provide details about a typical or exceptional firm or individual

26. Data Collection Techniques: Archival Research Answering a research question using existing ( secondary ) data sets Lack of control over quality of data is a concern Minimizes time developing measures and collecting data Include both: Cross-sectional data : one point in time Longitudinal data : multiple time periods

27. Data Collection Techniques: Surveys Selecting a sample of respondents and administering a questionnaire Most frequently used method of data collection in I/O Two approaches: Self-adminis t e re d Interviews

28. Data Collection Techniques: Surveys Self-administered questionnaires Completed by respondents in absence of researcher Used in both lab and field settings Useful for 3 reasons: Ease of administration Can be administered to large groups at one time Provides respondents with anonymity

29. Data Collection Techniques: Surveys Self-administered questionnaires (continued) Drawbacks of using surveys Low response rates (mail surveys) Difficult for respondents to ask questions

30. Data Collection Techniques: Surveys Interviews (Investigator-administered) Usually conducted face-to-face, can be done over the phone More time consuming than self-administered questionnaires Clear benefits: Higher response rates Ambiguity about questions can be resolved

31. Data Collection Techniques: Surveys Technological Advances Web-based surveys – valuable alternative Experience sampling methodology (ESM) Captures momentary attitudes and psychological states PDA “signals” participants to answer questions at a predetermined time Popular for the study of emotions at work

32. Measurement: Overview Measurement – assignment of numbers to objects or events using rules in a way that represents specified attributes of the objects Attribute – dimension along which individuals can be measured and along which they vary

33. Measurement: Overview Because accuracy of measurement is important there are two major concerns: Reliability Validity

34. Reliability and Validity Accuracy is similar to Validity Precision is similar to Reliability

35. Reliability The consistency or stability of a measure Predictors must be measured reliably Measurement error renders measurement inaccurate or unreliable We cannot predict outcomes with variables that are not measured well

36. Validit y

37. Summary of Reliability Types and Approaches to Construct Validity

38. Statistics: Overview Statistic : summarizes in a single number the values, characteristics, or scores describing a series of cases Measures of central tendency Measures of dispersion Shapes of distributions Correlation and regression Meta-analysis

39. Statistics: Measures of Central Tendency Characterize a typical member of the group Mode : most frequent single score in a distribution Useful with categorical data Median : score in the middle of a distribution Extreme scores do not affect the median Mean : arithmetic average of a group of scores Sensitive to extreme scores

40. Statistics: Measures of Dispersion Inform us how closely scores are grouped around the measure of central tendency; “spread-outedness” of the data Range : spread of scores from the lowest to the highest Variance : most useful measure of dispersion Standard deviation : square root of the variance; retains original metric of score

41. Statistics: Shapes of Distributions Normal Distribution Bell-shaped curve Most observations are clustered around the mean with fewer outside in either direction Many psychological variables are distributed this way (e.g., job attitudes, performance, intelligence) Normal distribution can be used to calculate percentile scores : individual score relative to the population

42. Statistics: Correlation and Regression Correlation coefficient ( r) : strength of the relationship between two variables Provides information about the direction and the magnitude of the relationship Direction can be positive (elevator) or negative (teeter totter) Magnitude: to 1.00

43. Correlation Examples

44. The Correlation Coefficient ( r ) …indicates how much two variables covary or “go together” (regardless of units of measurement!). Examples… “Need to achieve” & GPA Anger & heart disease Psychopathy & hockey penalties Self-esteem & body weight

45. Effect Size Correlation between being married and satisfaction with life? .10 Correlation between being in a good mood and being willing to help others (i.e., give time or money) .30 Correlation between being in a bad mood and being perceived as aggressive (in lab interactions and team exercises) .50

46. r = 1  perfect covariation (higher x  higher y) r =  no covariation (higher x tells nothing about y) r = -1  perfect negative covariation (higher x  lower y)

47. Statistics: Regression Allows us to predict one variable from another How much variance in a criterion variable is accounted for by a predictor variable Coefficient of determination ( r 2 ): percentage of variance accounted for by the predictor

48. Coefficient of Determination

49. Met a -An a lysis Methodology used to conduct quantitative literature reviews Previously only narrative reviews were conducted Used to combine the results of multiple studies to arrive at the best estimate of the true relationship