# Improving Causal Inferences in Child and Family Behavioral Research > **NIH NIH R03** · OKLAHOMA STATE UNIVERSITY STILLWATER · 2022 · $74,800 ## Abstract Project Summary/Abstract Most empirically supported treatments for conduct problems in children are implemented by parents, but a recent meta-analysis found them to be half as effective now as 50 years ago. Basic parenting research has failed to improve these treatments partly because of its inadequate causal validity. Of the two basic methods for analyzing longitudinal change, ANCOVA-type residualized-score analyses have been preferred for causal estimates, but they are biased by stable pre-existing differences on outcome scores, according to recent critiques. But the alternative, difference-score analysis, has less statistical power and cannot easily test crucial Pretest X Treatment interactions. This study intends to address those disadvantages by testing extensions of a promising innovation called dual-centered ANCOVA to multi-occasion data. This is a modification of Huitema’s quasi-ANCOVA that centers posttest scores (new) as well as pretest scores (quasi) around their pretest group means. Centering pretests make the two change-score results consistent with each other, whereas centering posttest scores around pretest group means then keeps all change scores unchanged. It avoids the bias in standard ANCOVA by duplicating the results of difference-score analyses, but retains ANCOVA’s advantages: more statistical power and ability to test Pretest X Treatment interactions. For “true” causal effects to compare for testing bias, this research simulates two 3-occasion datasets to fit the null hypotheses of each traditional change-score analysis. It also tests longitudinal data on corrective actions by parents or professionals shown to be effective in randomized trials. Depression treatments (medications and therapy), child hospital visits, and timeout will each be tested in two longitudinal datasets. Research questions include: (1) Do analyses across three occasions still get contradictory results from the two standard change-score analyses? (2) Does dual-centered ANCOVA duplicate the results of pure within-person difference-score analyses, parallel to the robust consistency across both change-score analyses after pretest matching? (3) If standard residualized analyses and pretest matching continue to contradict the known effectiveness of these corrective actions, do Pretest X Treatment interactions reconcile the discrepancies by showing effectiveness for the most at-risk cases? (4) Can the identification and control of Time-1 confounds be improved by similar focusing on pure within-person changes and group-mean centering? We expect this study to document biases that explain why all corrective actions appear to be harmful in residualized analyses, whether implemented by parents (disciplinary responses, talking to youth about deviant behavior and peers) or by professionals (therapy, prescription medications, child care subsidies, foster care, job training programs). Removing these pervasive biases have the potential to improve the ability of basic p... ## Key facts - **NIH application ID:** 10495247 - **Project number:** 5R03HD107307-02 - **Recipient organization:** OKLAHOMA STATE UNIVERSITY STILLWATER - **Principal Investigator:** ROBERT Earl LARZELERE - **Activity code:** R03 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $74,800 - **Award type:** 5 - **Project period:** 2021-09-24 → 2024-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10495247 ## Citation > US National Institutes of Health, RePORTER application 10495247, Improving Causal Inferences in Child and Family Behavioral Research (5R03HD107307-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10495247. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*