1. Lesson 3 Measurement and Scaling

2. Case: “What is performance?” brandesign.co.za

3. 13–3 What Do I Measure? Measurement The process of describing some property of a phenomenon, usually by assigning numbers in a reliable and valid way.

4. Peer Pressure and Investing Behavior Some individuals are more susceptible to peer pressure than others—even for less visible products and services, such as investments. Researchers examined the susceptibility to interpersonal influence (SCII) and found it does influence investment behavior. Construct??? Concepts measured with multiple variables. driverlayer.com

5. 13–5 Operational Definitions Operationalization The process of identifying scales that correspond to variance in a concept involved in a research process. Scales A device providing a range of values that correspond to different characteristics or amounts of a characteristic exhibited in observing a concept.

6. Research Activity: Define each of the following concepts, and then operationally define each one by providing correspondence rules between the definition and the scale

7. Scales of Measurement 1.Height of girls and boys in primary school 2.Your GPA 3.Your IQ scores 4.# errors made in learning task N O I R

8. Scales of Measurement Assigning numbers to events, characteristics, or behaviors 4 Scales of Measurement: 1.nominal scales 2.ordinal scales 3.interval scales 4.ratio scales

9. 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

10. Ordinal Scales Numbers are used to indicate rank order How used: 1.rank order (1 st, 2 nd, 3 rd, etc.) individuals based on one or several other pieces of data Example: 4 students’ class rank (based on GPA): 1, 2, 35, 100

11. 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)

12. 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)

13. Nominal, Ordinal, Interval, and Ratio Scales Provide Different Information

14. Scales of Measurement Why do we need to know which measurement scales is being used?

15. Activity Measure of intelligence -- Skull circumference http://imgarcade.com/1/macrocephalic-head/

16. Evaluating Measures Reliability vs Validity Repeatability & consistency Measures what it is designed to measure Content validity Criterion Construct

17. 13–17 Validity 1.Is there a consensus that the scale measures what it is supposed to measure? 2.Does the measure correlate with other measures of the same concept? 3.Does the behavior expected from the measure predict actual observed behavior?

18. 13–18 Validity (3Cs) Content Validity The degree that a measure covers the breadth of the domain of interest. Criterion Validity The ability of a measure to correlate with other standard measures of similar constructs or established criteria. Construct Validity Exists when a measure reliably measures and truthfully represents a unique concept.

19. Sensitivity A measurement instrument’s ability to accurately measure variability in stimuli or responses. Generally increased by adding more response points or adding scale items.

20. 13–20 SensitivitySensitivity ReliabilityReliabilityValidityValidity

21. Statistical Analysis What is the difference between a population and a sample? How are the population and sample related to statistical analysis?

22. Descriptive and inferential statistics Statistical Analysis Descriptive statistics Describe the sample data Measures of central tendency Measures of variability Visual displays of data

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

24. 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 (e.g., a positive correlation between variables) Statistical Analysis

25. Hypothesis Testing 2 possible outcomes Reject null hypothesis (with some probability) Fail to reject the null hypothesis Possible errors Type I  reject null hypothesis, but be wrong Type II  fail to reject null hypothesis but be wrong Statistical Analysis

27. Confidence intervals Range within which population mean likely to be found

28. Q & A Some athletes will be given training in a new imaging procedure that they are to use just prior to shooting foul shots; they will be compared with other athletes not given any special training.

29. Q & A Some athletes will be given training in a new imaging procedure that they are to use just prior to shooting foul shots; they will be compared with other athletes not given any special training. H 0 : imaging procedure will have no effect on foul-shooting H 1 : those using the imaging procedure will shoot better than those without the procedure Type I: those using imaging perform significantly better (p <.05), but no true difference exists Type II: differences not significant (p >.05), but imaging really does improve foul-shooting

30. Learning Outcomes 1.Determine what needs to be measured to address a research question or hypothesis 2.Distinguish levels of scale measurement 3.Know how to form an index or composite measure 4.List the three criteria for good measurement 5.Perform a basic assessment of scale reliability and validity