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Syntax

1. Introduction (PDF & R-Code) The independent variable – other terms include the treatment (0,1), predictor, or exogenous variable – is the mechanism in the population that we hypothesize causes changes in the dependent variable. That is, we believe that when a cases’ score on the independent variable increases or decreases, a cases’ score onContinue reading “Entry 14: Level of Measurement of the Independent Variable”

Just a warning, this is a long entry! Overall message, operationalize variables on a continuous scale when possible and consider the ramifications of transforming continuous distributions. 1.Introduction (PDF & R-Code)[i] I can see no place more fitting to start a discussion of Statistical Biases and Measurement than focusing on the measurement of our dependent variablesContinue reading “Entry 13: Level of Measurement of the Dependent Variable”

Introduction (PDF) Welcome to what I believe will be one of the most important sections of the Sources of Statistical Biases Series: Measurement. Besides the existence of confounders, I strongly believe that the measurement of a construct represents one of the largest sources of statistical bias in all scientific disciplines. This belief stems from theContinue reading “Statistical Biases and Measurement: Introduction”

Introduction (PDF & R-Code) The previous entries have focused on the biases that can exist when generating causal inferences through methodological and statistical approaches. I know it is considerably difficult for researchers trained in experimental methods – such as myself in criminology – to discern that causal inferences can be generated without randomly assigning participantsContinue reading “Entry 12: The Inclusion of Non-Causally Associated Constructs and Reverse Causal Specifications”

Introduction (PDF & R-Code) Generating causal inferences is a difficult process. We can run a true experiment (i.e., a randomized controlled trial), however that can be ethically concerning when randomizing certain treatments. Alternatively, we can develop a Directed Acyclic Graph (DAG) and reduce the influence of confounders and colliders on our association of interest. OrContinue reading “Entry 11: Instrumental Variables”

Introduction (PDF & R-Code) You might have the thought of: I know the constructs that can confound, mediate, or moderate the association I am interested in, and I am surely not going to include a collider as a covariate in my regression model! You also, like a great student of causal inference, have embraced theContinue reading “Entry 10: The Inclusion and Exclusion of Descendants”

PDF & R-Code A variety of techniques can be used to simulate linear associations between a continuous independent variable and a normal continuous dependent variable in R. I, however, rely on the lesser employed process of specifying linear directed equations. Briefly, a linear directed equation can be simply thought of as a regression formula but,Continue reading “Simulating Linear Associations with Normal Continuous Variables”

Introduction (PDF & R-Code) When I state “X is a cause of Y”, I am inherently implying that variation in X caused variation in Y. This statement, however, does not provide any indication of how X caused variation in Y. The how is just as important, if not more important, than knowing the causal association.Continue reading “Entry 9: The Inclusion and Exclusion of Mediating and Moderating Mechanisms”

Introduction (PDF & R-Code) You might be saying that Entry 7 provides good evidence for why we should include numerous variables in a statistical model and hope they adjust for confounder bias. I mean…  in some sense… climate change can confound the association between unemployment rates and crime rates. While it does suggest that, itContinue reading “Entry 8: The Inclusion of Colliders (Collider Bias)”