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For the third misidentification, we regressed Y1 on X1, EX1, and LEN7 using the base LM command in R (illustrated in the Figure below). This was replicated 10,000 times – using R-loops – while randomly varying the effects for each causal pathway between the constructs in the network excluding the direct effect of X1 onContinue reading “Misidentification 3: Y1 regressed on X1, EX1, & LEN7”

For the second misidentification, we regressed Y1 on all of the variables in the system using the base LM command in R (illustrated in the Figure below). This was replicated 10,000 times – using R-loops – while randomly varying the effects for each causal pathway between the constructs in the network excluding the direct effectContinue reading “Misidentification 2: Y Regressed on All Variables in the System”

Introduction (PDF & R-Code) As introduced in Entry 5, the random assignment of respondents to a treatment or a control is fundamental to the process of conducting a true experiment. We assume that random assignment will remove the possibility of an unknown variable confounding the association between exposure to the treatment and an outcome ofContinue reading “Entry 6: Covariate Imbalance (Randomized Controlled Trials)”

This post was updated on 1/15/2021. A problem was identified in the specification of the seed for the structural model (i.e., Lavaan seed command) in the initial loop. It appears that the sample command does not explore the full distribution of potential values. As such, the seed in the updated code now equals runif(1,0,1000000000). IContinue reading “Misidentification 1: Bivariate Regression of Y on X”

Corresponding Publication: Silver, Ian A. and Kelsay, James D. 2021 “The moderating effects of population characteristics: A potential biasing factor when employing non-random samples to conduct experimental research.” Journal of Experimental Criminology. https://doi.org/10.1007/s11292-021-09478-7. Introduction (PDF & R-Code) Conducting a randomized controlled trial is the foremost strategy for estimating a causal association between a treatment variableContinue reading “Entry 5: Unknown Interactions (Randomized Controlled Trials)”

The next section of the Sources of Statistical Biases Series will provide simulated explorations of key assumptions of causal analysis. It is important, however, to understand that two frameworks are primarily used to evaluate for causal associations: Causal Inference Through Experimental Design and Causal Inference Through Directed Acyclic Graphs (DAG). Distinct from popular belief, theseContinue reading “Statistical Biases When Examining Causal Associations”

Introduction (PDF & R-Code) An outlier is a case, datapoint, or score meaningfully removed from the mass of the distribution as to be recognizably different from the remainder of cases, datapoints, or scores. Consistent with this definition, outliers are conditioned upon the observed data and can vary between samples. For example, an individual with 10Continue reading “Entry 4: The Normality Assumption- Outliers”

Introduction (PDF & R-Code) Regression based techniques are one of the most frequently used statistical approaches for hypothesis testing. The primary benefit of regression techniques is the ability to adjust estimates for the variation across multiple independent variables – otherwise known as the statistical control approach. This is extremely valuable when we are interested inContinue reading “Entry 3: The Collinearity Assumption”

Introduction (PDF & R-Code) Homoscedasticity and heteroscedasticity are not just difficult words to pronounce Homoscedasticity and heteroscedasticity are not just difficult words to pronounce (homo·sce·das·tic·i·ty & hetero·sce·das·tic·i·ty), but also terms used to describe a key assumption about the distribution of error in regression models. In statistics we regularly make assumptions about the structure of error.Continue reading “Entry 2: The Homoscedasticity Assumption”