Three Claims, Four Validities - Chapter 3 - Part 1 PDF

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Dr. Prichett's Research Methods chapter 3, part 1....

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Three Claims, Four Validities - Chapter 3 Variables ● Variables vs. constants ○ Measured or manipulated ○ IMPORTANT: Dependent variables = measured, Independent variables = manipulated ○ Constants are controlled ● From conceptual variable to operational definition ○ Broad statement to specific measure Measured and Manipulated Variables ● Measured variables are observed and recorded. ● Manipulated variables are controlled. ○ Some variables can only be measured, not manipulated. ○ Some variables can be either manipulated or measured. From Conceptual Variable to Operational Definition ● Conceptual variables are common in psychology (anxiety; temperament). ● Operational definitions help define abstract concepts. Three Claims ● Frequency claims ● Association claims ● Causal claims ● Not all claims are based on research. Frequency Claims ● Frequency claims describe a particular rate or degree of a single variable. ● Frequency claims involve only ONE  MEASURED VARIABLE. ● Example of Freq. Claim: 1 in 25 U.S teens attempt suicide. Association Claims ● Association claims argue that one level of a variable is likely to be associated with a particular level of another variable. ● Association claims involve at least TWO  MEASURED VARIABLES. ● Variables that are associated are correlated  . ● Example of Association Claim: Single people eat fewer vegetables.

Positive Association

● ○ When the points are closely packed together going upward towards the right in a straight line. Negative Association

● ○ When the points are closely or loosely packed together going downwards towards the right in a straight line Zero Association

● ● A zero association has no association with the variables and no prediction. The points are scattered randomly

● IMPORTANT: With correlations, direction tells you if the relationship is positive or negative. Strength tells you how well the 2 variables correlate. A strong negative correlation means that as one variable increases the other decreases and they patter is highly consistent across participants. A strong positive correlation means the 2 variables constantly change in the same direction (i.e., as one increases so does the other one) Causal Claims (only in experiments) ● One or more variables are manipulated and one or more variables are measured ○ It will have a manipulated variable and a measured variable. ● The manipulated and measured variables must covary. ● Temporal  precedence must be established. ○ timing ● There are no alternative explanations (proper control) ● MUST HAVE ALL THREE ● Example of Causal Claim: Music lessons enhance IQ. Making Predictions Based On Associations ● Some association claims are useful because they help us make predictions. ● The stronger the association between the two variables, the more accurate the prediction. ● Both positive and negative associations can help us make predictions, but zero associations cannot. . Not All Claims Are Based On Research ● Not all claims we read about in the popular press are based on research. ● Some claims are based on experience, intitution, or authority. Interrogating the Three Claims Using the Four Big Validities ● Reliability: consistency ● Interrogating frequency claims ● Interrogating association claims ● Interrogang causal claims The Four Big Validities ● Construct: How well are the variables in a study measured or manipulated? ○ Accuracy and reliability of measures and operational definition. ○ IMPORTANT: in freq. Claims ● External: How generalizable is the study? ○ Meaning: how well did the researchers randomly picked their participants to represents the population?

● Statistical: How strong is the effect and status of statistical significance? ○ Consider the effect size ■ Significance support: bigger sample size ○ Consider the p-value ■ Meaning: what is the probably that we did not find these results by chance? P- value of .05 or below is good! ●

Internal: What is the nature of the effect of variable A on variable B, and no other variable(s) are responsible for the A -> B effect?...