Problem Set 1: Pearson Correlation Coefficient Analysis
Research Scenario:
A clinical psychologist would like to determine whether there is a relationship between observer ratings of children’s externalizing behaviors and scores on an established diagnostic interview assessing externalizing disorders (like ADHD, CD, etc.). He administers the diagnostic interview to 25 children and records these scores. He then trains an observer to independently rate carefully-defined externalizing behaviors for each of the 25 children. These scores are totaled for an overall “externalizing behavior index.” On both the interview and the behavioral ratings, a higher score indicates higher levels of externalizing behavior. These scores are listed in the table below.
Using this table, enter the data into a new SPSS data file and run a Pearson correlation coefficient analysis to test whether there is a relationship between the interview scores and the behavior ratings in this sample. scatterplot to show the relationship between the variables.
- Paste SPSS output.
- Results section based on your analysis including independent and dependent variables, confidence interval, effect size, 1 or 2 tailed and the p value.
Problem Set 2: Pearson Correlation Coefficient Analysis
Research Scenario: A social worker involved in suicide prevention efforts wants to study the relationship between social isolation and possible suicide risk. She collects scores from a sample of 13 patients on two measures: one is a measure of social isolation on which higher scores indicate higher isolation (possible range of scores = 0- 20), and the other is the Suicide Risk Scale (SRS, Plutchik et al., 1989), on which higher scores indicate a higher risk of suicide (possible range of scores = 0-15). These scores are listed in the table below.
Using this table, enter the data into a new SPSS data file and run a Pearson correlation coefficient analysis to test whether there is a relationship between social isolation and possible suicide risk. scatterplot to show the relationship between the variables.
Interview (Range 0to 9)Externalizing Behavior Index(Range 0 to 25)79692220IN A WOOD12109-JU161921224773131916129
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