# HPEB 818

 Threats to Validity ConstructExternalInternalStatistical Conclusion Threats to Statistical Conclusion Validity Low Statistical PowerViolated Assumptions of Statistical TestsFishing and the Error Rate ProblemUnreliability of MeasuresRestriction of RangeUnreliability of Treatment ImplementationExtraneous Variance in the Experimental SettingHeterogeneity of UnitsInaccurate Effect Size Estimation SCV: Low Statistical Power An insufficiently powered experiment may incorrectly conclude that the relationship between treatment and outcome is not significant. SCV: Violated assumptions of Statistical Tests Violations of statistical test assumptions can lead to either overestimating or underestimating the size and significance of an effect. SCV: Fishing and the Error Rate Problem Repeated tests for significant relationships, if uncorrected for the number of tests, can artifactually inflate statistical significance SCV: Unreliability of Measures Measurement error weakens the relationship between two variables and strengthens or weakens the relationship among three or more variables. SCV: Restriction of Range Reduced range on a variable usually weakens the relationship between it and another variable. SCV: Unreliability of Treatment Implementation If a treatment that is intended to be implemented in a standardized manner is implemented only partially for some respondents, effects may be underestimated compared with full implementation. SCV: Extraneous Variance in the Experimental Setting Some features of an experimental setting may inflate error, making detection of an effect more difficult. SCV: Heterogeneity of Units Increased variability on the outcome variable within conditions increases error variance, making detection of a relationship more difficult. SCV: Inaccurate Effect Size Estimation Some statistics systematically overestimate or under estimate the size of an effect. Ways to increase statistical power use matching, stratifying, or blocking measure, and correct for covariates use larger sample sizes use equal cell sample sizes improve measurement (e.g. increase range of measurements/reduce dichotomized variables, add additional waves of measurement) increase the strength of treatment (e.g. increase dose differential)Use a within-participants design Use homogeneous participants selected to be responsive to treatment Reduce random setting irrelevancies Ensure that powerful statistical tests are used and their assumptions are met. (e.g. transforming the data) Internal Validity Definition Inferences about whether observed variation between A and B reflects a causal relationship from A to B in the form in which the variables were manipulated or measured. Threats to Construct Validity Inadequate Explication of ConstructsConstruct ConfoundingMono-Operation BiasMono-Method BiasConfounding Constructs with Levels of ConstructsTreatment Sensitive Factorial StructureReactive Self-Report ChangesReactivity to the Experimental SituationExperimenter ExpedienciesNovelty and Disruption EffectsCompensatory EqualizationCompensatory RivalryResentful DemoralizationTreatment Diffusion AuthorAnonymous ID229350 Card SetHPEB 818 Descriptionfor the quals Updated2013-08-07T00:21:10Z Show Answers