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Reasoning in Psychology Using Statistics

Dive into the world of statistics in psychology with an in-depth look at descriptive and inferential statistics, data collection methods, and the scientific method. Explore how statistics are used to make data-based decisions and draw conclusions. Learn basic research methods and the fundamentals of SPSS data analysis.

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Reasoning in Psychology Using Statistics

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  1. Reasoning in PsychologyUsing Statistics Psychology 138 Spring 2015

  2. Added some tutorial videos authored by Dr. Joel Schneider in the Resources and Materials section of ReggieNet. Announcements

  3. “It’s about almost everything in modern society.” Bennett, Briggs, Triola (2003), Statistical Reasoning for Everyday life What are Statistics?

  4. “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 • Descriptive statistics • Inferential statistics • Data: numbers with a context • How were numbers measured, what do they mean? What are Statistics?

  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

  6. 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 test claim scientifically? • What data to collect? • Who to test? • How do we make our observations? An Example

  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 in an experiment • Variables measured in observational research Variables

  9. ?? • 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

  10. What is the level at which the research is focused? • 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

  11. Who to test? • Population • Set of all individuals of interest • Typically no access to whole population • Sample • Subset of population data collected from Test sample & generalize results to population as a whole Getting experimental participants

  12. Absence makes the heart grow fonder • How could we go about testing this? • What data should we collect? • Who should we test? • How should we make our observations? • Observational study • Observe & measure variables of interest to find relationships • No attempt to manipulate or influence responses • Experimental methodology • 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

  13. “The world of statistics starts with a question, not with data” Keller 2006, Tao of Statistics • Statistics: tools, used to make data based decisions • Data: numbers with a context • Understanding the context in which the observations are made (e.g., How were numbers measured? Who did they come from? What do they mean?) is critical for both doing statistical analyses as well as interpreting the results. What are Statistics?

  14. Learning the basics of SPSS including entering data Today’s Lab

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

  16. 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

  17. 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

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