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Evaluating Data - Theories Notes.docx

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Department
Psychology
Course
PSY 1071
Professor
Joel Sheffield
Semester
Fall

Description
September 6, 2011 Evaluating Data and Theories Notes I. It’s not enough to see the data, we must understand the pitfalls that separate good data vs bad Theory – proposed model of reality • Often invisible, thus models are necessary to see the inner workings of reality • Good models make testable predictions, or hypotheses • Must be falsifiable (you must be able to imagine data that could disprove a theory) Ex: It is possible to read minds as long as there are no non-believers around who might cause bad vibes (not falsifiable, people cannot disprove it) Ex: “Attention to a demanding task will induce blindness for unattended events.” – Hypothesis: “All subjects will see the gorilla.” – Falsifiable! Testable theories and hypothesis • Measures must be reliable (must give similar results each time they are used) • Measures must be valid (test what they are supposed to test) Reliability and Validity -Clark & Hatfield study on casual sex • Women are more likely to respond favorably to “go out” • Men rank higher in all parts (70% respond yes to “will you have sex with me”) • Study redone later on, the results are roughly the same What’s the best way to gather data? • Descriptive studies o Describe the behavior of an individual or set of people without investigating the relationships between specific variables o This is used when the systematic manipulation is not an option o Ex: Naturalistic observation (Jane Goodall’s study of chimps and tool-making) o Great for inspiring new studies, but they cannot inform us of the cause and effects of phenomena o Descriptive tools may include: tests and surveys • Correlational Studies o Measure of the relationship between two variables outside the investigator’s control  Ex: Height, weight, socio-economic level, number of years of education o Positive correlation – as one variable increases, the other increases o Negative correlation – as one variable increases, the other decreases o No correlation – (zero or near zero) the variables have no relationship with each other o The value of the “correlation coefficient can range from -1.00 to +1.00  The higher the absolute value, the stronger the relationship is, regardless of the direction  -.89 OR + .15 – first shows a greater relationship o Note: Correlations do not indicate causation  Correlations only reveal if two variables are related 
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