CHAPTER 4 Research in Psychology: Methods & Design

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Chapter 4. Measurement and Data Analysis Chapter 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

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

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

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

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

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

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

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

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

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)

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)

Scales of Measurement Which measurement scales are being used?

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!

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?

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

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

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

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

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

Statistical Analysis

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

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