1. Selecting the Best Measure for Your Study
Chapter 6
Selecting the Best Measure for Your Study

2. Overview
Sensitivity
Scales of Measurement

3. Sensitivity:
Will measure be able to detect the differences you need to detect?

4. Achieving the Necessary Level of Sensitivity
Look for High Validity
Look for High Reliability
Find Measures That Provide a Variety of Scores

5. Find measures that provide a variety of scores
Avoid Behaviors That are Resistant to Change
Avoid Measures That Produce a Limited Range of Scores
Ask How Much Instead of Whether
Add Scale Points to a Rating Scale
Pilot Test Your Measure

6. Sensitivity: Conclusions
Scores must vary
But for the right reasons

7. Scales of Measurement What are the different scales of measurement?
What scale of measurement do you need?
What measure will provide that scale of measurement?

8. The Different Scales of Measurement
Nominal Numbers: Different Numbers Representing Different States
Ordinal Numbers: When Bigger Means More
Interval Scale Numbers: Knowing How Much More
Ratio Scales: Zeroing In On Perfection

9. Why Measures Often Don’t Provide Ratio Scale Measurement
Often no absolute zero point
Even when there is an absolute zero point, changes in the measure do not perfectly correspond to changes in psychological reality.

10. Which Level of Measurement Do You Need?
When You Need Ratio Scale Data
When You Need At Least Interval Scale Data
When Ordinal Data Are Sufficient
When You Only Need Nominal Data

11. Ethical and Practical Considerations
Attempts to reduce subject biases may lead to ethical problems (spying, violating informed consent)
Desired measure may be too expensive or unavailable to you.
Practical concerns may lead you to choose a measure with high face validity

12. Concluding Remarks
The best measure for your study depends on validity and
Level of sensitivity you need
Scale of measurement you need
Ethical and practical constraints

13. Introduction to Descriptive Methods
Chapter 7
Introduction to Descriptive Methods

14. Overview Uses and Limitations of Descriptive Methods
Why We Need Science to Describe Behavior
Sources of Data
Describing Data From Correlational Studies
Making Inferences From Correlational Data

15. Uses and Limitations of Descriptive Methods
Descriptive Research and Causality
Can’t test causal hypotheses
Can show that A and B are related, but can’t show whether A causes B, B causes A, or both are effects of some other factor
May suggest causal hypotheses
Description for Description’s Sake
Description for Prediction’s Sake

16. Why We Need Science to Describe Behavior
We need objective scientific measurement to overcome the human tendency toward bias
We need systematic, scientific record-keeping because memory is selective
We need objective ways to determine if variables are related because humans don’t innately compute correlation coefficients
We need scientific methods to meet both criteria necessary for making accurate generalizations (1) obtaining a representative sample and (2) making statistical inferences from that sample data

17. Sources of Correlational Data
Ex post facto data
Archival Data
Observation
Tests

18. Ex Post Facto Data You may have collected it while doing an experiment
External validity: Depends on sample
Construct validity: Depends on validity of measures
Internal validity: None

19. Archival Data Some has been collected and coded by others
Some is part of a public record (transcripts, web sites, videotapes, personal ads, etc.)
To code uncoded data, you will probably use content analysis

20. Archival Data (cont)
External validity: May be good: Large sample possible.
Construct validity:
May be good because measures can be nonreactive.
However, if data have been coded by others, their poor coding and/or instrumentation bias may hurt validity. If data were uncoded, validity can’t be better than the validity of your content analysis.
Internal validity: None

21. Observation Lab observation Naturalistic observation
Participant observation

22. Conclusions: Validity of Observation
External validity: Depends on sample
Construct validity: May be damaged by
Observer’s presence changing participants’ behavior
Observer not accurately recording behavior
Internal validity: None

23. Tests External validity: Depends on sample
Construct validity: Usually good
Internal validity: None

24. Describing Data From Correlational Studies
Graphing Data
Graph of a strong positive correlation
Graph of a strong negative correlation
Correlation Coefficients
Graph of a correlation coefficient
Graph of a correlation coefficient
Graph of a correlation coefficient

25. Mathematical Notes About Correlation Coefficients
1. Sign indicates direction of relationship, but not strength 2. Absolute value indicates strength of relationship (farther from zero, the stronger the relationship) 3. Squaring the Pearson r gives you a measure of the strength of the relationship-- the coefficient of determination, which ranges from 0-1

26. Mathematical Notes (cont.)
4. The type of correlation coefficient you should compute depends on the type of data you have
If both variables are interval or ratio, Pearson r
If both variables nominal, phi coefficient

27. Making Inferences From Correlational Data
Analyses Based on Correlational Coefficients
Analyses Not Involving Correlation Coefficients
Interpreting Significant Results
Interpreting Null Results

28. Are the two variables related in the population?
Need random sample of population
Statistical test to determine if the variables are related
Several tests to choose from
All will be more likely to say that the variables are related if
The correlation coefficient is large
Sample size is large

29. Tests Used to Determine If Variables Are Related
t test
ANOVA
Test to see if the correlation coefficient is significantly different from zero

30. Cautions about Significant Results
Don’t allow cause-effect statements
May represent Type 1 errors, especially if
Numerous tests were done and
No corrections were made for the number of tests done

31. Cautions about Null Results
Null results may be due to
Not enough participants scores
Insensitive measure(s)
Nonlinear relationship
Restriction of range
Using a t test rather than the more powerful test

32. Concluding Remarks You now know the basics of descriptive research
However, to learn about the most commonly used descriptive method (the survey), you need to read Chapter 8