Parental autonomy support and child executive function skills
Year: 2026
Type: preprint
Abstract: Preregistration: Autonomy Support and Executive Function Skills Lutfiye Ulusahin, Ramla Chowdhury and Sabine Doebel Background. This project replicates and extends prior work on autonomy support and executive function skills. Have any data been collected for this study already? No. What are the main hypothes(es) being tested in this study? Autonomy support will be correlated with a composite index of executive function skills The relation will be significantly weaker in a condition in which instructions aim to reduce expectations that child & parent performance is being evaluated. Are there exploratory analyses that you plan to run? We will explore associations between EF, autonomy support, and children’s activities at home. We will explore links between autonomy supportive parenting beliefs and autonomy support assessed behaviorally. Describe the key variable(s) specifying how they will be measured. Autonomy Support (behavior). Parents’ autonomy-supportive behavior will be assessed using an adapted observational coding scheme developed by Whipple, Bernier, and colleagues (Bernier et al., 2010; Whipple et al., 2011). Parent–child interactions during a brief free-play task will be video recorded and coded across four dimensions: concern for the child’s sense of competence and task adaptation, verbal autonomy support, flexibility and perspective-taking, and following the child’s pace and providing choices. For each dimension, coders provide global ratings of autonomy support, control, and laissez-faire behavior on 5-point scales. Coders assign one rating per scale based on the overall interaction. Executive Function Tasks Dimensional Change Card Sort (DCCS). Children completed a five-level version of the DCCS, a measure of cognitive flexibility. Children were asked to sort cards according to one rule (e.g., color) and then switch to sorting according to a different rule (e.g., shape). The primary analytic variable was the highest level completed correctly (range: 0–5), with higher scores indicating greater cognitive flexibility. Grass–Snow Task. Children completed the Grass–Snow task, a measure of inhibitory control. Children were instructed to point to a white card when hearing “grass” and to a green card when hearing “snow,” requiring them to inhibit a prepotent response. The primary analytic variable was the number of correct responses across 26 trials (range: 0–26), with higher scores indicating better inhibitory control. Backward Digit Span. Children completed a backward digit span task assessing working memory. The experimenter read sequences of digits ranging from two to six digits in length, and children were asked to repeat each sequence in reverse order. The primary analytic variable was the length of the longest sequence recalled correctly across 10 trials, with ‘1’ assigned to children who could not recall a 2-item sequence correctly (span range: 1-6), with higher scores indicating better working memory. Head–Toes–Knees–Shoulders (HTKS). Children completed the HTKS task, a behavioral measure of executive function requiring inhibitory control, working memory, and cognitive flexibility. Children were instructed to perform the opposite of a stated action (e.g., touch their toes when told to touch their head). The primary analytic variable was the number of correct responses across 20 trials, including self-corrections (1) and fully correct (2) responses (total possible points = 40 and score range range: 0-1), with higher scores indicating stronger executive function. 6. Specify exactly which analyses you will conduct to examine the main question/hypothesis. H1: We will use linear regression models implemented in R to test whether autonomy support predicts EF, controlling for age. We will create a composite EF score where justified and will also report analyses by task. Specifically: lm(EFcomposite ~ autonomy_support_score + age, data) lm(grass_snow_summary_score ~ autonomy_support_score + age, data) lm(dccs_summary_score ~ autonomy_support_score + age, data) lm(htks_summary_score ~ autonomy_support_score + age, data) lm(backward_digit_span ~ autonomy_support_score + age, data) H2: We will use linear regression to test if condition interacts with autonomy support scores to predict EF: lm(EFcomposite ~ autonomy_support_score*condition + age, data) lm(grass_snow_summary_score ~ autonomy_support_score*condition + age, data) lm(dccs_summary_score ~ autonomy_support_score*condition + age, data) lm(htks_summary_score ~ autonomy_support_score*condition + age, data) lm(backward_digit_span ~ autonomy_support_score*condition + age, data) Outliers and Exclusions. Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. No participants with valid data for variables in an analysis will be excluded. Sample Size. How many observations will be collected or what will determine sample size? The target sample size is 88 participants, 44 per condition. This sample size is based on a power analysis using an effect size from previous work by Distefano et al, who found an r of .41. We would need N of approximately 44 dyads to detect r = .41 with 80% power, two-tailed α = .05.
Authors: Sabine Doebel, Lütfiye Uluşahin
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7166651620