Statistical models

Hypotheses 1 and 2 will be analyzed using ANOVA, where the conditions will be compared on the mean level of the indices, and ordinary least squares regression, whereby the accommodation and administrative burden indices are regressed on the condition variable. We will use the emmeans R package to test the marginal means of planned contrasts, using Tukey’s p-value adjustment. Note that the covariate for if one passed the comprehension check or not will be used throughout analyses. Gender and disability status will be added as covariates in some analyses to compare to a baseline model.

Transformations: No transformations will be done. The reference value for the manipulated factor will be the control.

Inference criteria: We will be using p-values and setting our alpha at .05. Two-tailed tests will be used. p-value adjustments for multiple testing will use Tukey’s HSD.

Data exclusion: Those who do not identify as over 18 and college students will be dropped from our survey respondents. Outliers will be included in analyses.

1. Preliminary Examination of Data

• Descriptive Statistics: Compute basic measures (e.g., means, medians, standard deviations) for survey responses, such as levels of support for accommodations and tolerance for administrative burdens. This will be done for each disability type (physical, psychiatric, and control).

• Demographic Data: Summarize the demographic characteristics (e.g., gender, disability status) provided by Forthright. This helps understand the composition of the sample and ensures representation across key subgroups.

• Disability Identity: Having a disability or receiving accommodation for a disability will act as an exploratory variable. Receiving accommodations may create a positive perspective of other students receiving academic support, while having a disability (and not receiving accommodations) may create a negative perspective of “undeservedness” towards students receiving academic support. 

2. Data Quality Checks

• Response Validity: Identify incomplete or inconsistent responses, such as missing values or illogical patterns in answers. As mentioned above, we will create a control variable based on those who fail the attention check (or have inconsistent responses) and include that in our regression analysis. 

3. Identifying Patterns and Trends

• Explore whether attitudes differ significantly based on demographic factors like gender or respondents' own disability status, including whether or not the student gets accommodations. Our preliminary hypothesis is that gender will act as a covariate such that women students will be more supportive of academic accommodations and less supportive of administrative burdens in comparison to men. Disabled students will be more supportive of academic accommodations and less supportive of administrative burden in comparison to non-disabled students.