Reasoning in Psychology Using Statistics

1. Reasoning in PsychologyUsing Statistics Psychology 138 Spring 2019

2. “It’s about almost everything in modern society.” Bennett, Briggs, Triola (2003), Statistical Reasoning for Everyday life • Statistics: tools used to make Data based decisions • Data: numbers with a context What are Statistics?

3. Context • Main points from the video • Every statistical test starts with an appropriate selection of subjects • Inferences must be based on more than one observation because of variability • Two types of error must be controlled while testing hypotheses • A decision is based on two things: • The difference between groups • The variability of the scores Week 3 Descriptive Statistics Weeks 5&6 Week 10 Weeks 10-15 Statistics Inferential Video to Course

4. Data: numbers with a context e.g., How were numbers measured? Who did they come from? What do they mean? Understanding the context in which the observations are made is critical for both doing statistical analyses as well as interpreting the results. What are all these numbers?

5. T r u “The world of statistics starts with a question, not with data” Keller 2006, Tao of Statistics • Traditional Knowing: truth or error • Assumes perfect uniformity • Assumes error-free repetitions • Modern Knowing: probabilistic • Assumes variability • Our focus: Scientific Method • Systematic observation (& experimentation) used to explain how and why events occur • Systematic observations constitute data • Statisticsare used to describe data & relationships within data T r u t h Ways of Knowing “Alternative facts” “Truthiness”

6. “The world of statistics starts with a question, not with data” Keller 2006, Tao of Statistics • Scientific Method • Ask research question • Identify variables and formulate hypotheses • Define population • Select research methodology • Collect data from sample • Analyze data • Draw conclusions based on data • Repeat Statistics The research process

7. ?? • Claim: Absence makes the heart grow fonder • But, what about your observation that long distance romances never work out? (Out of sight, out of mind) • How to test the claim scientifically? • What data do we collect? • Who to test? • How do we make our observations? An Example Actual examples: Jiang & Hancock (2013)

8. What data do we collect? • Identify what we are studying • Variables • Characteristics or conditions that change or have different values for different individuals (or situations) • Independent (explanatory) variables (IV) • Variable that has causal impact • In experiment, variable that is manipulated by researcher • Dependent (response) variable (DV) • Variable observed for changes to assess effect of the manipulation (of the IV) in an experiment • Variables measured in observational research Variables Actual example: Jiang & Hancock (2013)

9. To switch between the views click on the tabs Two view windows: Brief tour of SPSS

10. Each row corresponds to a variable Each column corresponds to a feature of the variables This is where you specify the details about the variables The Variable View

11. Each row corresponds to an experimental unit (called “cases” in SPSS lingo) Each column corresponds to a variable So each column in the data view corresponds to a row in the variable view The Data View

12. ?? • Absence makes the heart grow fonder • What are some potential Independent (explanatory) variables? • How long apart? • How far apart? • How much communication? • How “strong” was the relationship to begin with (quasi-independent)? • What are some potential Dependent (response) variables? • Ratings of fondness for partner • Heart rate when seeing a picture of partner • fMRI of brain when hearing partner’s voice Independent and Dependent Variables Actual example: Jiang & Hancock (2013)

13. What is the level at which the research is focused? (what do the rows in the Data view correspond to?) • Individuals • Between individuals • Within individuals • Across groups • Couples • Families • Cities • Ethnic groups • Our example:Absence makes the heart grow fonder • What level(s) could we focus on? Experimental Unit Actual example: Jiang & Hancock (2013)

14. What data do we collect? • Who to test? • Population • Set of all individuals of interest • Typically no access to whole population • Sample • Subset of population data collected from Inferential Statistics: Test sample & generalize results to population as a whole Observing participants (getting data) Actual example: Jiang & Hancock (2013)

15. Absence makes the heart grow fonder • How could we go about testing this? • What data should we collect? • Who to test? • How should we make our observations? • Observational study (Explanatory and Response variables) • Observe & measure variables of interest to find relationships • No attempt to manipulate or influence responses • Experimental methodology (Independent and Dependent variables) • Independent variable manipulated while changes observed in another variable (dependent) • Can establish cause-and-effect relationships • Extensive controls to minimize extraneous sources of variability • Quasi-Experimental methodology • One (or more) of the independent variables is a pre-existing characteristic (e.g., sex, age, etc.) Basic Research Methods Actual example: Jiang & Hancock (2013)

16. Learning the basics of SPSS including entering data Today’s Lab Reminder: Monday is MLK Day, so no class or labs