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

Overview Sensitivity Scales of Measurement

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

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

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

Sensitivity: Conclusions Scores must vary But for the right reasons

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?

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

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.

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

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

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

Introduction to Descriptive Methods Chapter 7 Introduction to Descriptive Methods

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

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

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

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

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

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

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

Observation Lab observation Naturalistic observation Participant observation

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

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

Describing Data From Correlational Studies Graphing Data Graph of a strong positive correlation Graph of a strong negative correlation Correlation Coefficients Graph of a +1.00 correlation coefficient Graph of a -1.00 correlation coefficient Graph of a 0.00 correlation coefficient

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

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

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

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

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

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

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

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