Reasoning in Psychology Using Statistics

1. Reasoning in PsychologyUsing Statistics Psychology 138 2018

2. Don’t forget Quiz 1, due this Friday, Jan. 26 • Exam 1 not far away, Wed Feb 7th Announcements

3. Scientific Method • Ask research question • Identify variables and formulate hypothesis • Define population • Select research methodology • Collect data from sample • Analyze data • Draw conclusions based on data • Repeat Where do the data come from? • Experiments method • Independent variables • Dependent variables • Observational method • Explanatory variables • Response variables From last time Measuring and Manipulating Variables

4. Response (dependent) variable Explanatory (independent) variable Claim: Absence makes the heart grow fonder • What are the variables in this claim? Measuring and Manipulating Variables

5. Two levels of variables • Conceptual level of variables (abstract) • What theory is about (absence, fondness) • Operational level of variables (concrete) • What actually manipulated/measured in research • Duration of time apart • Rated fondness Claim: Absence makes the heart grow fonder Operational definition • Specifies relationship between conceptual & operational levels Measuring and Manipulating Variables Read the methods of actual example: Jiang & Hancock (2013)

6. Operational definition • Specifies relationship between conceptual & operational levels • Describes set of operations or procedures (the instrument) for measuring conceptual variable • Defines the variable in terms of measurement Measuring and Manipulating Variables

7. Claim: Absence makes the heart grow fonder What do we mean by absence? Two people involved in relationship having to be apart for a long time. How do we measure (or manipulate)absence? Amount of time apart, number of visits, distance one of these or perhaps a combination Measuring Variables

8. Brainwave machine Survey Claim: Absence makes the heart grow fonder So what do we mean by heart grow fonder? • Strength of relationship • Level of desire How do we measurefondness of the heart? • Have couple rate fondness for one another • Hook each to brain monitor & record while seeing pictures of sweetheart & pictures of other people Measuring Variables

9. Brainwave machine Survey Claim: Absence makes the heart grow fonder • Choosing your instrument • How might these measures be different? • What impact might these differences have? Very much Somewhat Not at all How fond are you of your partner? 1 - 2 - 3 - 4 - 5 Measuring Variables

10. Properties of measurement • Unit of measurement • Scale of measurement • Error in measurement • Validity • Reliability Measurement: Quantitative Research

11. Properties of measurement • Unit of measurement- minimum sized unit • Scale of measurement • Error in measurement • Validity • Reliability Measurement: Quantitative Research

12. 3, or 2.5 cookies 2, 1 kid or 2 kids , but not 2.5 • Continuous variables • Variables can take any number & be infinitely broken down into smaller & smaller units • E.g., For lunch I can have • Discrete variables • Broken into a finite number of discrete categories that can’t be broken down • E.g., In my family I can have Units of Measurement

13. Properties of measurement • Unit of measurement • Scale of measurement- correspondence between properties of numbers & variables measured • Stevens (1946) Typology • Categorical variables: Nominal & Ordinal Scales • Set of discrete kinds of things (categories) • Can attach names to these categories • Quantitative variables: Interval and Ratio Scales • Distinct levels with differing amounts of characteristic of interest • Can attach numbers to these amounts Which scale you use will impact what statistics you can perform and how you should interpret your analyses Measurement: Quantitative Research

14. brown, hazel blue, green, • Nominal Scale: Consists of a set of categories that have different names. • Measurements on a nominal scale label and categorize observations, but do not make any quantitative distinctions between observations. • Example: • Eye color: Scales of measurement

15. Small, Lrg, Med, XL, XXL • Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. • Measurements on an ordinal scale rank observations in terms of size or magnitude. • Example: • T-shirt size: Scales of measurement Think about Restaurant ratings

16. Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. • With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. • Ratios of magnitudes are not meaningful. • Example: • Fahrenheit temperature scale 40º 20º “Not Twice as hot” Scales of measurement

17. Ratio scale: An interval scale with the additional feature of an absolute zero point. • With a ratio scale, ratios of numbers DO reflect ratios of magnitude. • It is easy to get ratio and interval scales confused • Consider the following example: Measuring your height with playing cards Scales of measurement

18. Ratio scale 8 cards high Scales of measurement

19. Interval scale 5 cards high Scales of measurement

20. Ratio scale Interval scale 8 cards high 5 cards high 0 cards high means ‘as tall as the table’ 0 cards high means ‘no height’ Scales of measurement

21. In SPSSScale of Measure: Nominal, Ordinal, Scale (interval or ratio) – in the variable view Scales of measurement

22. Properties of measurement • Unit of measurement • Scale of measurement • Error in measurement • Validity • Reliability Measurement: Quantitative Research

23. Validity • Does our measure really measure the construct? (accuracy/precision) • Think about the operational definition • Is there bias in our measurement? • Systematic error • Reliability • Do we get the same score with repeated measurements? Errors in measurement

24. Center represents the true score Collection of ‘darts’ is a sample of measurements The center of the sample is the estimate of the true score Dart board represents Population of all possible scores Dart board example

25. Low variability/low bias Points are all close together (similar) & Centered on the target Reliable & Valid measure Dart board example

26. Low variability/high bias Points are all close together (similar) & NOT centered on the target Reliable but Invalid measure Dart board example

27. High variability/low bias Points are NOT all close together (dissimilar) & Centered on the target Valid but Unreliable measure Dart board example

28. High variability/high bias Points are NOT all close together (dissimilar) & NOT centered on the target Unreliable & invalid measure Dart board example

29. Today’s lab: Measurement • Questions? SPSS Operational Definitions: Measuring Happiness (~2 mins) Operational Definitions (~20 mins) Scales of measurement (~6 mins) Scales of measurement (~7 mins) Scales of measurement (~15 mins) Reliability and validity (~ 2 mins) Reliability and validity (~ 2 mins) Wrap up