Reasoning in Psychology Using Statistics

1. Reasoning in PsychologyUsing Statistics Psychology 138 2017

2. Don’t forget Quiz 1, due Friday, Jan. 27 • Exam 1 not far away, Wed Feb 8th 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

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 • What theory is about (absence, fondness) • Operational level of variables • 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

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 scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale Measurement

14. Categorical variables • Quantitative variables • Set of discrete kinds of things (categories) • Can attach names to these categories • 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 Scales of measurement

15. 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

16. Small, Med, Lrg, 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

17. 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

18. 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

19. Ratio scale 8 cards high Scales of measurement

20. Interval scale 5 cards high Scales of measurement

21. 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

22. In SPSSScale of Measure: Nominal, Ordinal, Scale (interval or ratio) Scales of measurement

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

24. 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

25. 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

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

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

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

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

30. Today’s lab: Measurement • Questions? SPSS Wrap up