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CHAPTER 4 Research in Psychology: Methods & Design

CHAPTER 4 Research in Psychology: Methods & Design. If you have not already done so…. EMAIL ME at alla.chavarga@gmail.com Subject: YOUR NAME Psych 3450- TA. Chapter 4. Measurement and Data Analysis Chapter Objectives.

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CHAPTER 4 Research in Psychology: Methods & Design

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  1. CHAPTER4Research in Psychology:Methods & Design

  2. If you have not already done so… • EMAIL ME at alla.chavarga@gmail.com Subject: YOUR NAME Psych 3450- TA

  3. Chapter 4. Measurement and Data AnalysisChapter Objectives • Recognize the variety of behavioral measures used when conducting research in psychology • Describe what psychologists mean by a construct and how measurable behaviors are developed and used to study constructs • Explain how a behavioral measure is reliable and relatively free from measurement error • Explain how a behavioral measure is valid, and distinguish several forms of validity

  4. Chapter Objectives • Identify the defining features of nominal, ordinal, interval, and ratio scales of measure- ment, and describe when each should be used • Summarize data effectively using measures of central tendency (e.g., mean), variability (e.g., standard deviation), and visual displays (e.g., bar graphs) • Understand the logic of hypothesis testing and what is involved in making an inferential analysis of data

  5. Chapter Objectives • Distinguish between Type I and Type II errors and explain how they relate to statistical hypothesis testing • Understand what is meant by (a) effect size and (b) the power of a statistical test, and know the factors that enhance power

  6. What to Measure—Varieties of Behavior • Developingmeasures from constructs • Relates again to operational definitions • Research Example – Habituation • Construct  understanding of gravity • Measures  preferential looking and time spent looking

  7. What to Measure—Varieties of Behavior • Research Example – Reaction Time • Construct visualization/ visuo-spatial sketchpad • Measure  reaction time

  8. Evaluating Measures • Reliability: extent to which it produces the same result when applied to the same person under the same conditions • Results from a minimum amount of measurement error • Reliability = repeatability, consistency • Validity: • Content validity ( face validity): extent to which the measurement method covers the entire range of relevant behaviors, thoughts, and feelings that define the construct being measured • Criterion (predicting): extent to which people’s scores are correlated with other variables or criteria that reflect the same construct. For example, an IQ test should correlate positively with school performance. • Construct (convergent and discriminant) extent to which it measures the construct that it is supposed to measure

  9. Scales of Measurement • assigning numbers to events, characteristics, or behaviors • 4 Scales of Measurement: • nominal scales • ordinal scales • interval scales • ratio scales

  10. Nominal Scales • assign numbers to events to classify them into one group or another • numbers are used as names [categorical] How used: • assign individuals to categories • count the number of individuals falling into each category (reported as frequencies) Example: Verdict: 0 = not guilty, 1 = guilty

  11. Ordinal Scales • numbers are used to indicate rank order How used: • rank order (1st, 2nd, 3rd, etc.) individuals based on one or several other pieces of data Example: 4 students’ class rank (based on GPA): 1, 2, 35, 100

  12. Interval Scales • scores indicate quantities • equal intervals between scores • score of zero just a point on the continuum • a score of zero does not indicate ‘absence’ of something How used: • calculate score from participants’ responses on a test Examples: temperature, IQ scores, scores from personality tests (see Box 4.2)

  13. Ratio Scales • scores indicate quantities • equal intervals between scores • score of zero  does denote ‘absence’ of something How used: • calculate score from participants’ responses on a test EXAMPLES: # words recalled, # errors made in maze learning task, time to make a response (reaction time)

  14. Scales of Measurement • Which measurement scales are being used?

  15. Scales of Measurement • Why do we need to know which measurement scales is being used? • Because it guides our decisions on which statistical tests are appropriate to use!

  16. Statistical Analysis • Descriptive and inferential statistics • What is the difference between a population and a sample? • How are the population and sample related to statistical analysis?

  17. Statistical Analysis • Descriptive and inferential statistics • Descriptive statistics • Describe the sample data • Measures of central tendency • What scores are at the center of a distribution • Mean, median, mode • With outliers  median better than mean • Measures of variability • How spread out or dispersed scores are in a distribution • Range, standard deviation, variance • Visual displays of data • Histograms from frequency distributions • With graphs, carefully examine Y-axis (Box 4.3) to avoid being misled

  18. Statistical Analysis • Visual displays of data • CNN example – beware the Y-axis • Figures 4.8 and 4.9 from Box 4.3

  19. Statistical Analysis • Descriptive and inferential statistics • Inferential statistics • Inferring general conclusions about the population from sample data • Examples t-tests, ANOVAs

  20. Statistical Analysis • Hypothesis testing • Null hypothesis • No relationship (“no difference”) between variables in the population expected, given our sample • Alternative hypothesis • A relationship (“a difference) between variables in population is expected, given our sample • A researcher’s predictions often specifies the direction of the relationship

  21. Statistical Analysis • Hypothesis testing • 2 possible outcomes • Reject null hypothesis (with some probability) • Conclude you found a significant relationship between variables • Fail to reject the null hypothesis • Conclude you found no significant relationship between variables • Because you are testing a sample and making inferences about the population, your statistical decisions have a probability of being wrong! • Possible errors • Type I  reject null hypothesis, but be wrong • Type II  fail to reject null hypothesis but be wrong

  22. Statistical Analysis

  23. Statistical Analysis • Hypothesis testing • Interpreting failures to reject null hypothesis • Extreme caution • May be useful if the outcome is replicated • Example questioning a claim for the effectiveness of some new therapy; useful if studies consistently show lack of effect of therapy • File drawer effect

  24. Statistical Analysis • Going beyond hypothesis testing • Effect size • Emphasizes the size of difference between variables, not merely whether there is a difference or not • Useful for meta-analysis • Confidence intervals • Range within which population mean likely to be found • Power • Chance of rejecting a false null hypothesis • Sample size an important factor

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