1. CHAPTER 4 Research in Psychology: Methods & Design

2. If you have not already done so…
ME at
Subject: YOUR NAME Psych TA

3. 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

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
Developing measures 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
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