Missing Data Handling: Thinking It Through James Peugh, Ph. D. Cincinnati Children’s Hospital Medical Center

Overview & Outline Examine Missing Data Handling in Four SEM-Based Analyses Categorical CFA with Covariate (MIMIC Model) Moderated Mediation (MacArthur Method) SEM with a Latent Variable Interaction Term Multilevel ANCOVA SEM Keep Two Questions in Mind: What are your options for missing data handling?: ML & MI What does MAR mean, and how can I make that more plausible in my analysis model?

Categorical Indicator CFA with Covariate (HA: Imputation) (Folder Model_1.zip)

HB_ss Sickle-Cell Disease Responsibility Scale Measured by 17 Items Assessed on a 1-3 Categorical Scale (10% - 20% Missing Data at the Item Level) HB_ss = Hemoglobin Type SS *Also have Gender, Age, and Pediatrics-QL Subscale Scores

HA: (Saturated) Model Imputation Multiple Imputation is a Model HA: Imputation, or Saturated Model Is a Model with 0 df Essentially imputes based on a correlation matrix Specifies all possible relationships among both analysis and auxiliary variables Why auxiliary variables in addition to analysis variables? MAR: Says missing on your analysis variables are related to variables included in your analysis model *Auxiliary: Adding extra information, but without biasing results

HA: Imputation Analysis Key Points for Analyzing Imputed Data Check the .OUT file from imputation for the order of the variables in the imputed datasets Mplus input for analysis of imputed data is a list file generated by imputation

Mplus pools imputed analysis results Extra material: Scale reliability (α = 0.967)

Moderated Mediation-Mac Arthur Method (Folder Model_2.zip)

Moderated Mediation: MacArthur Definitions: Z Moderates an X→Y relationship if the relationship is significant conditional on values of Z M Mediates an X→Y relationship if M explains how/why X relates to Y Temporal Order Requirements: Z is a Moderator ONLY if it Precedes X M is a Mediator ONLY if X Precedes M Moderation Requirements: The M*X interaction, “must always be included in the linear model” (Kraemer, et al., p.1680) Why? Kraemer, et al. (2008) VanderWeele (2015)

Perceived Media Influence: Tal-Or, et al., 2010 Reaction (Y): How willing consumers would be to buy a good/service based on a media story Import(ant) (M): How important a perceived shortage in a good/service would be in a decision to buy Cond(ition) (X): 0 for a less visible media story, 1 for a more visible story PMI (Z): How inclined you believe others would be to buy based on perceived media influence MZ: Importance * PMI interaction

The Mplus Syntax

SEM Latent Variable Moderator (HA: Imputation) (Folder Model_3.zip)

Why HA: Imputation?

Mplus

Multilevel ANCOVA (H0: Imputation) (Folder Model_4.zip)

Why H0: Imputation? WITHIN or LEVEL 1 (Students) BETWEEN or LEVEL 2 (Classes) Why H0: Imputation?

Mplus