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Clinical Psychology
Week 4 Powerpoints
| Question | Answer |
|---|---|
| Kazdin (1982) | Symptom substitution and Generalization |
| Symptom Substitution | Treatments that focus on symptom reduction with targeting the "underlying causes" of dysfunction risk merely having reduced symptoms replaced by non-targeted symptoms |
| Generalization | Changes in one behavior during treatment relate to changes in other, similar domains of behavior |
| Empirically testing Symptom Substitution | Operationally define construct, identify constituent parts, post alternative explanations, review studies to determine evident support |
| New Model: Response Covariance | Alternative to symptom substitution. Two or more correlated behavioral responses to treatment. Response to treatment affects other responses. Responses can go in either direction (one behavior improves but other worsens/improves) |
| Kazdin (1982) Take-Home Message | Treatment produces changes in sets of behaviors that relate changes (both positive and negative) in related or distinct sets of other behaviors. |
| Examples of poor decision making? | False Dilemma and Appeal to Ignorance |
| False Dilemma | Situation in which only two alternatives are considered |
| Appeal to Ignorance | Something is true because it has not yet been proven false. |
| What can lead to poor decision-making? | "gut" feelings |
| Correlation | Relation between two variables, no necessary direction of the relation (variable X leads to variable Y, or vice versa) |
| Causation | Levels of a variable directly/indirectly influence a second variable's levels |
| Mediation | Two variables are related in some way, and a third variable explains WHY the relation exists. |
| Moderation | Two variables are related under some circumstances, and a third variable influences the DIRECTION or MAGNITUDE of this relation. |
| Prevention | Decreasing likelihood that an outcome occurs |
| Intervention | Decreasing an outcome that has already occurred |
| Stages of Ethics | -Institution approval -Consent -Compensation -Debriefing -Reporting -Publication -Sharing data |
| Psychologists cannot address research Q's strictly using: | Experimental Approaches |
| How do designs vary? | Ability to draw inferences between "cause" all the way to "effect" Also vary by number of participants. |
| Two factors involved in understanding outcomes of research. | Internal Validity & External Validity |
| Internal Validity | Does the data tell you what you think it tells you? |
| Impact 1 on Internal Validity | History: what participants went through during the study but had nothing to do with variable of interest |
| Impact 2 on Internal Validity | Maturation: participants changed over course of study in ways that had nothing to do with study |
| Impact 3 on Internal Validity | Testing: completing measures might change things |
| Impact 4 on Internal Validity | Instrumentation: changing measures over course of study, esp. as participants aged. |
| Impact 5 on Internal Validity | Statistical Regression: how many times did participants complete the measures. |
| Impact 6 on Internal Validity | Selection Bias: recruiting participants or assigning them to conditions |
| Attrition | Form of selection bias due to a lot of participants dropping out before end of study. This causes bias in data interpretation. |
| External Validity | Will the study's outcomes apply to people who were not in the sample; generalization |
| Impact 1 on External Validity | Sample Characteristics: matching between sample and rest of people who are targets. |
| Impact 2 on External Validity | Stimulus Characteristics/Settings: what if study was conducted somewhere else, would there be similar outcomes? |
| Impact 3 on External Validity | Reactivity: change in participants behavior b/c they are in a study. |
| Impact 4 on External Validity | Timing: assessments at other periods would have same results? |
| Case Studies | are detailed descriptions about someone, usually with a new treatment. |
| Case Studies are great for generating what? | Research hpyotheses |
| Case studies cannot rule what out? | Threats to internal validity |
| Single-Case studies can partially rule out? | Internal Validity issues |
| Single-Case studies | Have multiple measurements of outcome (before,after, during). Exact manipulated variable Need to introduce and then remove treatment. |
| Single-Case studies detect what kind of patterns? | Patterns between manipulated variable and measurement outcomes |
| Correlation designs are not the same as? | Correlational Anallyses |
| Correlational Designs have no: | Experimental manipulation or random assignment |
| Quasi-Experimental Designs | Reseacher-based manipulation of a variable, such as treatment condition |
| Quasi-Experimental Designs have no: | Random assignment to experimental conditions |
| Quasi-Experimental Designs cannot rule out: | Extraneous influences (variation among participants in different conditions) |
| Experimental Designs do have: | Random assignment and manipulation |
| Experimental Designs allow for: | unambiguous interpretations of effects of manipulation on outcome |
| Which study design provides the best protection against internal validity | Experimental Design |
| Randomized controlled trials | Treatment studies |
| Which is a quantitative review | a Meta-Analysis |
| Meta-Analysis is a: | Group of studies addressing the same topic; main findings. |
| Effect Size | The average results across studies using a common scale |
| Probability Sampling | Interest in ensuring that research sample represents population |
| Non-probability Sampling | No specific interest in representing a population |
| Sample size needs to be large enough to: | Ensure statistical power to detect hypothesized effects |
| Measurement Reliability | The degree of consistency in measurement |
| 3 Examples of Measurement Reliability | Internal Consistency, Test-retest reliability, and interrater reliability. |
| Internal Consistency | Items on test relate highly with each other |
| Test-retest Reliability | Measure are stable over time |
| Interrater Reliability | Different observers provide similar scores about the same person's behaviors |
| Measurement Validity | Degree to which the construct of interest is accurately measured |
| 3 Examples of Measurement Validity | Face Validity, Predictive Validity, Convergenet Validity |
| Face Validity | Does it look like a measure of the construct? |
| Predictive Validity | Predicting the development of construct from child to adult; does childhood diagnosis predict adult diagnosis? |
| Convergent Validity | Does the measure relate to other measures of the same and similar contructs? |
| Common Kinds of Measures | Self-Report, Informant report, trained rater, observation, psychophysiological, archives, performance-based. |
| Statisitical Conclusion Validity | Aspects of data analysis that impact validity of conclusions drawn |
| Threats to Statistical Conclusion Validity | Low Statistical Power, Multiple Comparisons, Measurement Unreliability. |
| Low Statistical Power | No significant effects b/c the sample size was too small |
| Multiple Comparisons | Significant effects due to chance, given the number of tests conducted |
| Measurement Unreliability | No significant effects b/c the measures were unreliable |
| Statistical Significance | p < .05 |
| An effect that is not statistically significant: | Reveals little about how meaningful a finding is |
| Psychological measures scores | Measures yield scores that do not have direct relation to the real world |
| Definition of Statistical Significance | The degree an effect had a meaningful impact on the "real world" functioning of participants |
Created by:
roxandsocks
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