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Conducting Psychological Research

Conducting Psychological Research. Slides Prepared by Alison L. O’Malley. Passer Chapter 2 . What is “ good science” ? Jot down 3 characteristics…. Origins of Research Questions. Generate an example associated w ith each source. . Personal experience and daily events

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Conducting Psychological Research

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  1. Conducting Psychological Research Slides Prepared by Alison L. O’Malley Passer Chapter 2

  2. What is “good science”? Jot down 3 characteristics…

  3. Origins of Research Questions Generate an example associated with each source. • Personal experience and daily events • Prior research and theory • Real-world problems • Serendipity

  4. Conducting a Literature Search Where to begin?

  5. Conducting a Literature Search • Online databases: PsycInfo, Google Scholar… • Boolean operators: AND, NOT, OR to narrow results • ***Peer-reviewed articles*** • Full text access? • If not, try authors’ personal websites • … or interlibrary loan (allow plenty of time!)

  6. Conducting a Literature Search Research Question: Are pet owners happier than non pet owners? What’s the optimal What’s the optimal Way way to enter this question into a search database? In search database?

  7. Making Sense of What You Find

  8. Making Sense of What You Find

  9. Making Sense of What You Find

  10. Making Sense of What You Find

  11. Making Sense of What You Find

  12. Making Sense of What You Find

  13. Making Sense of What You Find Note. Review papers (e.g., Annual Review of Psychology) will deviate from this format

  14. Forming a Hypothesis Is one logical approach “better” than the other? • Inductive: Specific facts  general conclusion • Data driven; “bottom up” • E.g., medical diagnosis based on symptoms • Deductive: Generalprinciple  specific conclusion • Theory driven; “top down” • E.g., All people have ___. Pat is a person. Therefore, Pat has ___.

  15. Research Approaches: Key Distinctions Qualitative vs. Quantitative • Describe the characteristics of a recent happy episode in your life. • How happy are you? 1 2 3 4 5

  16. Research Approaches: Key Distinctions Experimental vs. Descriptive • Research Scenario 1: Employees randomly assigned to receive cookies or not receive cookies while completing a job satisfaction questionnaire (Brief, Butcher, & Roberson, 1995) • Research Scenario 2: Employees complete a questionnaire containing questions about mood and job satisfaction

  17. Research Approaches: Key Distinctions Experimental vs. Descriptive • Research Scenario 1: Employees randomly assigned to receive or not receive cookies while completing a job satisfaction questionnaire (Brief, Butcher, & Roberson, 1995) • Research Scenario 2: Employees complete a questionnaire containing questions about mood and job satisfaction What can we conclude on the basis of each research scenario? Why?

  18. Research Design: Mind Your Variables Independent variable: Systematically manipulated by the researcher in experimental research Dependent variable: Outcome of interest; what we design research to assess/measure

  19. Research Design: Mind Your Variables Identify the IV(s) and DV(s) in this scenario: Employees randomly assigned to receive cookies or not receive cookies while completing a job satisfaction questionnaire

  20. Mastering IVs and DVs • Generate and describe a good strategy for distinguishing independent variables from dependent variables in research scenarios.

  21. Research Approaches: Key Distinctions Laboratory vs. Field • Did employees complete the job satisfaction questionnaire under the same conditions (i.e., in identical environments), or did they take the questionnaire online at a time and place of their choosing? Lab settings = CONTROL

  22. Research Approaches: Key Distinctions Laboratory vs. Field • Field experiments still entail manipulation of an IV, but occur in a natural setting as opposed to a lab setting. • Researchers often mention the tradeoff between internaland external validity. What exactly does this mean, and why does such a tradeoff occur?

  23. Research Approaches: Key Distinctions Cross-sectional vs. Longitudinal • 20 year olds • 40 year olds • 60 year olds If all three age groups are measured and compared in summer 2013, the design is cross-sectional.

  24. Research Approaches: Key Distinctions Cross-sectional vs. Longitudinal • 20 year olds • 40 year olds • 60 year olds If all three age groups are measured and compared in summer 2013, the design is cross-sectional. Beware of cohort effects–different age groups have different histories. Are observed differences due to age differences or the groups’ different historical experiences?

  25. Research Approaches: Key Distinctions Cross-sectional vs. Longitudinal • 20 years old Summer 2013 • 40 years old Summer 2033 • 60 years old Summer 2053 If a group of participants is measured repeatedly over time, the design is longitudinal.

  26. Research Approaches: Key Distinctions Cross-sectional vs. Longitudinal • 20 years old Summer 2013 • 40 years old Summer 2033 • 60 years old Summer 2053 If a group of participants is measured repeatedly over time, the design is longitudinal. Sequential research designs examine several age cohorts longitudinally.

  27. Research Approaches: Key Distinctions Cross-sectional vs. Longitudinal • 20 years old Summer 2013 • 40 years old Summer 2033 • 60 years old Summer 2053 What are the advantages and disadvantages of longitudinal and sequential research designs?

  28. Research Design: Mind Your Variables • Internal validity is compromised by the presence of confounds, a particularly pesky type of extraneous variable.

  29. Research Design: Mind Your Variables • Example: Do participants prefer stimuli associated with the first letter of the English alphabet? • If random assignment is used such that half the participants see the object on the left and half see the object on the right, what’s the problem? A B

  30. The Role of Sampling Population vs. Sample • What is a population? • The entire group of scores that a researcher desires to learn about (e.g., all U.S. college students) • What is a sample? • A subset of scores from the population (e.g., 1,000 college students from a variety of colleges)

  31. Analyzing Data and Drawing Conclusions Quantitative and qualitative analysis

  32. Descriptive Statistics Organize and summarize a set of data • Measures of central tendency address the typicality of a score: • Mode: most frequent score • Median: middle score (of an ordered set) • Mean: mathematical center of distribution

  33. Descriptive Statistics Central Tendency • Build a dataset comprised of how many siblings each of your classmates has. • Establish the mode, median, and mean for this dataset.

  34. Descriptive Statistics: Central Tendency Is it more appropriate to report the mean or the median for men and women in this dataset? Why?

  35. Descriptive Statistics: Measures of Dispersion Organize and summarize a set of data • Measures of dispersion address the spread (i.e., the variability)ofa set of scores. Sketch the distribution associated with each of the three parties.

  36. Descriptive Statistics: Measures of Dispersion Organize and summarize a set of data • Measures of dispersion address the spread (i.e., the variability)ofa set of scores. Range: distance between highest and lowest score Variance: spread of scores relative to mean Standard deviation: square root of variance

  37. Inferential Statistics We use sample data to infer the nature of the population • An oft heard question is whether research findings are “statistically significant.” Are our findings merely due to random error—to chance? • Inferential statistics reveal the probability that our findings are due to chance.

  38. Inferential Statistics We use sample data to infer the nature of the population • Psychological scientists traditionally maintain that findings are statistically significant if the probability is less than 5% that the results are due to random error. p < .05 = 

  39. Inferential Statistics: Drawing Conclusions We use sample data to infer the nature of the population • Statistically significant findings mean that we’ve proven how the world works, right?

  40. Inferential Statistics: Drawing Conclusions We use sample data to infer the nature of the population • Statistically significant findings mean that we’ve proven how the world works, right? • WRONG.

  41. Inferential Statistics: Drawing Conclusions We use sample data to infer the nature of the population • Our results may not be practically important… • …or perhaps there were confounding variables at play. • Good research design is critical! • And even with solid research design, maybe our conclusion is downright wrong.

  42. Drawing Conclusions Two errors: False alarms and missed opportunities An innocent person is found guilty  False alarm (Type I error) In research terms, we mistakenly conclude that two variables are associated when they really have nothing to do with each other.

  43. Drawing Conclusions Two errors: False alarms and missed opportunities A guilty person is found innocent  Missed opportunity (Type II error) In research terms, we mistakenly conclude that two variables are not associated when they really are related.

  44. Drawing Conclusions Two errors: False alarms and missed opportunities Apply the false alarm and missed opportunity scenarios to the “cookie” experiment (Brief et al., 1995).

  45. How to Tell Your Research Story So we all speak the same language! • Run, don’t walk, to access the 6th edition of the APA publication manual! • http://www.apastyle.org/

  46. Building Knowledge and Theories • Contemplate the distinction between a theory and a hypothesis… • Now, why does theory building matter?

  47. What Makes a Good Theory? • Testability and specificity • Does theory lend itself to testable hypotheses and specific predictions? • Internal consistency and clarity • Does theory avoid contradictory predictions? Can it be falsified? Is it clear to experts how components of the theory relate to each other?

  48. What Makes a Good Theory? • Empirical support • Can theory be reconciled with current knowledge base? If not, can it debunk current “fact”? Does high quality research support new hypotheses derived from theory? • Parsimony • Law of parsimony: Explanations should use the minimum number of principles necessary to account for the maximum number of facts.

  49. What Makes a Good Theory? Last, but not least: Does the theory have an impact on the field?

  50. Proof and Disproof Research is more “probabilistic” than “absolute” (Baumeister, 2008) • Science values lively debate. There is no tolerance for the notion of “absolute proof.” It’s always possible that our results are due to chance. Similarly, a single set of results cannot “disprove” a hypothesis derived from a theory. • Science is forward-moving, and theories are strengthened or weakened as supportive or unsupportive findings continually emerge.

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