Framing, Analysis, Interpretation and Two Supplemental Tests for Exploring Serial Mediation Theories with Cross-Sectional Data

Nonexperimental cross sectional data are frequently used to evaluate mediation and serial mediation hypotheses despite well established limitations for such tests. Associational Variable Analysis (AVA) was introduced to address this problem by reframing mediational language in terms of conditional associations and incremental variance. This paper extends the framing, analysis options, and interpretation guidance in AVA to help researchers improve the analysis and interpretation of studies exploring serial mediation type theories with cross-sectional data. Two supplemental analyses are proposed to aid in the interpretation of such data. One symmetric conditional attenuation, measuring redundancy among the proposed set of variables. The second conditional centrality focused on the proposed mediating variables to examine if they account for a disproportionate share of the conditional dependence relative to the putative independent and dependent variables. Two empirical illustrations are used to demonstrate the framing, set of additional analyses needed to help frame alternative interpretations, and the utility of the two analytic summaries in terms of fostering both the completeness and accuracy of interpretation of such tests and foster incremental science. The first example draws on the data used in the initial AVA paper and the second evaluates a published serial mediation model linking family atmosphere to suicidal ideation through attachment and cognitive flexibility. Code and details for conducting the analyses are provided in supplemental materials. Findings illustrate how the suggested estimates help clarify redundancy, differentiation, and structural organization within multivariable theories thereby providing interpretive guidance for cross sectional analyses where serial mediation conclusions are not defensible.