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:
1.
2.
3.
4.
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:
1. assign individuals to categories
2. 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:
1. 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:
1. 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:
1. 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
• 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