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Research in Child Psychopathology

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Psychology 2320A/B
Elizabeth Hayden

09/18/2012: Lecture 2 Research in Child Psychopathology: Background  Good research tests specific hypotheses based on theory  Hypothesis: prediction about relationship between constructs o Can’t be proven, only supported (or not) by data o Can (and should) test many different ways o Construct: a psychological abstraction that must be operationalized for research  Once you’ve operationalized your constructs (and received ethics approval), you next: o Collect data (i.e. scores on the constructs of interest) o Conduct a statistical test o Yields a probability that results due to chance (p < 0.5 is convention)  Doesn’t mean findings are important  Clinical versus statistical significance Research on Etiology in Psychopathology: Background  Usually, correlational designs (rather than strictly experimental) are used o Examples: stressful life events cause depression or lead exposure causes ADHD Correlational Designs  Correlation – measure two variables and estimate their degree of relationship  Typically calculate a correlation coefficient ®, a number between -1.00 and +1.00  r = 0 (and around zero): variables are unrelated  r = +1.00 or -1.00: perfection relationship, knowing the value of one variable allows you to predict precisely the value of the other  Negative correlation – increases in one variable associated with decreases in the other  Positive correlation – increases in one variable associated with increases in the other Correlations Often Misinterpreted to Infer Causality  Example – parent-child interaction is associated with children’s school success  What type of correlation is this? o Positive correlation – more interaction means more school success Research on Etiology in Psychopathology  Two broad, basic design distinctions typically seen in psychopathology research: o Cross-sectional – studies in which all data are collected at the same time o Longitudinal – studies that occur over time Cross-Sectional VS Longitudinal Designs  Cross-sectional designs – most common, easy to implement o Cannot infer causality  Longitudinal designs – attempt to address causality. If IV precedes DV, causality is supported but not established o Third variable problem o Cohort effects “Pseudo-Longitudinal” and Longitudinal Designs  Retrospective o Inexpensive but may be biased o “Effort after meaning”  Follow-up o Follow clinical group over time  Follow-back o Identify clinical group with historical records  Prospective o High risk o Community samples o Expensiv
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