Tools for Studying Intimate Relationships

1. Chapter 2 Tools for Studying Intimate Relationships

2. The Importance of Relationships • Relationships matter for people’s physical and psychological well-being. • People want to know what is related to relationship satisfaction and relationship stability.

3. Asking and Answering Questions • The problem in the area of relationships is not a lack of information, but too much information and too much advice. • How can the accurate information be separated from the inaccurate information?

4. Asking and Answering Questions

5. The Key of Methodology • The key to separating accurate from inaccurate information is a focus on methodology. • Methodology asks: How was the information gathered? How were the conclusions made?

6. Scientific Method • One means of gathering information, making conclusions, and testing those conclusions • Key ingredients: theory, hypothesis, operationalization, measurement, study design, data analysis, revision of theory

7. The Scientific Method

8. Theories and Hypotheses • Theory: general explanation • Must be falsifiable • For instance, one theory concerning relationship satisfaction and stability is attachment theory. • This theory states that the nature of people’s romantic relationships is associated with the nature of their parental relationships. • It is a general answer to the issue of relationship satisfaction and stability, and many specific predictions can come from it.

9. Theories and Hypotheses • Hypothesis: Specific, testable prediction that comes from a theory and generally involves the prediction that two or more variables will be related, or that two or more groups will be different • For instance, a hypothesis that comes from attachment theory is that people with a secure attachment to their parents will also tend to have a secure attachment to their relationship partner.

10. Choosing a Measurement Strategy • Operationalization: Specific, concrete way of thinking about a psychological construct • There are many different ways to operationalize the same psychological construct. • For instance, relationship satisfaction can be operationalized in terms of mood when with partner, desire to be with partner, overall feelings concerning partner, and relationship.

11. Choosing a Measurement Strategy • Construct validity: The degree to which the operationalization used reflects the psychological construct of interest • For instance, an operationalization of relationship satisfaction in terms of the degree to which partners’ clothes match would not have construct validity.

12. Choosing a Measurement Strategy • Measurement: A means of collecting data using the operationalization of the psychological construct • Could be in the form of self-report • Must watch out for social desirability concerns • Could be an open-ended or a fixed response • Could be omnibus or global • Could be in the form of observation • Inter-rater reliability is key • Could be in the form of a physiological response

13. Memory Biases in Self-reports

14. Designing the Study • Correlational study design: People are measured as they are. It examines the degree to which variables are related to each other. • May be positively correlated (e.g., coffee drinking and energy) or negatively correlated (e.g., coffee drinking and sleep) • Cannot make causal conclusions • May be cross-sectional • May be longitudinal • May use a daily diary

15. Different Types of Correlations

16. Designing the Study • Experimental research: Rather than measuring people as they are, the researchers first put them into different groups, using random assignment. • Allows for causal conclusions • Involves: independent variable (cause that is tested) and dependent variable (effect that is tested)

17. The Elements of a True Experiment

18. Designing the Study • Archival research: Use of pre-existing data or information to see if variables are related or groups are different • For instance, obituaries may be used to examine whether married people live longer than single people.

19. Summary of Research Designs

20. Choosing Whom to Study • Sample: People from whom data are collected • Population: Group about which the researcher wants to draw conclusions • The sample must match the population: • For instance, it would not make sense to collect data from a sample of dating couples in order to make conclusions about the population of married couples.

21. Drawing Conclusions • Research hypothesis: Prediction that comes from theory, frequently referred to simply as the hypothesis • Null hypothesis: “No difference” hypothesis, opposite of the null hypothesis; a prediction of no association between variables or no difference between groups

22. The Logic of Data Analysis • The researcher assumes the null hypothesis is true, collects data, and examines how likely it would be to get those data if the null hypothesis were true. • If this likelihood is low enough (smaller than 5% or .05), the researcher concludes that the null hypothesis is not true and consequently, the research hypothesis is supported.

23. An Example of Data Analysis • You hypothesize that females are more likely to break up with males than males are to break up with females. • The null hypothesis predicts that females and males are equally likely to initiate breakups.

24. An Example of Data Analysis, Continued • You collect data and find that 90% of the females and 1% of the males in your sample have initiated a breakup. • If the null hypothesis were true: • In the population males and females would be equally likely to initiate breakups. • By chance, you could have happened to find a sample where more females than males break up with their partners. • Is this likely?

25. An Example of Data Analysis, Continued • If the null hypothesis were true: • It would be very unlikely to find a sample with so many females who initiate breakups and so many males who do not initiate breakups. • It would be so unlikely, in fact, that the researcher would reject the null hypothesis. • The researcher would conclude that the null hypothesis is false and that the research hypothesis is true. • The researcher would conclude that females are more likely than males to initiate breakups.

26. In Research Terms • The likelihood of finding the data that were found in a study if the null hypothesis is true = p. • Any time p < .05, the null hypothesis is rejected. • When this happens, the research hypothesis is supported and the result is statistically significant.

27. Ethical Issues • Researchers are obligated to make sure that: • Participants’ time isn’t wasted (i.e., studies have to be well-designed). • Participants’ answers aren’t shared with others, thanks to confidentiality and anonymity. • Participants aren’t harmed by participating in studies. • Participants know what to expect (i.e., they are asked for informed consent): • Participants are not told about the hypothesis – instead, they are told what participating in the study will be like.

28. The Need for Many Studies • Researchers test aspects of theories through many different studies. • The more studies there are that demonstrate results supporting the theory, the more confidence researchers have in the theory. • If study results contradict a theory, the theory is modified and the modified theory is tested.

29. Additional Art for Chapter 2

30. Figure 2.3

31. Table 2.1

32. Table 2.2

33. Table 2.3

34. Figure 2.4

35. Figure 2.6

36. Table 2.4

37. Figure 2.7

38. Figure 2.8a

39. Figure 2.8b

40. Figure 2.9 a

41. Figure 2.9 b

42. This concludes the presentation slides for Chapter 2: Tools for Studying Intimate Relationships For more, visit our online StudySpace at: http://www.wwnorton.com/college/psych/intimate-relationships/ © 2010, W. W. Norton & Company, Inc.