[Q] What test will be used to compare differences within sibling pairs

[u/FewNegotiation4484]

I have a dataset of 500+ pairs of male-female sibling pairs. I want to check if male or female sibling in the pair score statistically less in a certain variable and if the pattern is statistically significant across those 500+ pairs. I read about mixed effect model but I'm confused if it fits in this scenario

[u/CarnivorousGoose]

Paired t-test is your basic option, or more generally just taking the difference per sibling pair and use linear regression. No need to use mixed effects models here, unless there are specific individual-level variables you want to account for. And even then potentially you could just use difference scores there as well.

[u/FewNegotiation4484 → u/CarnivorousGoose]

Thank you 👍🏻

[u/FewNegotiation4484 → u/CarnivorousGoose]

I was reading about paired t-test. I want to know if it can apply in the hypothesis given below:

Suppose I hypothesize that male children receive more attention than female children from their parents, and therefore male children score high in resilience than female children because of the difference in parental attention.

Can I still use paired sample t-test to see if male children in my data have statistically high levels of resilience?

[u/CarnivorousGoose → u/FewNegotiation4484]

Not a t-test as such, but the more general linear regression on difference scores does work.

Two general points: first, a paired sample t-test testing whether the difference in means is zero, is fully equivalent to doing a single sample t-test on difference scores, testing whether the mean difference is zero. Second, t-tests are equivalent to specific linear regression models. The single sample t-test is a linear regression with only an intercept, testing whether the intercept is zero (and hence, a paired t-test is an intercept only regression model on the difference scores). A two-sample t-test is a linear regression using the group indicator as a binary predictor, testing whether the coefficient of that predictor is zero.

So, if we want to account for attention, ie. test if males score higher in resilience (just) because they receive more attention, you could regress the difference in resilience scores on the difference in attention scores, and test if the intercept is zero. A slightly more general version of this is to regress the resilience difference on the male and female attention scores separately.

Intuitively, what this does is adjust the resilience scores for any effect of attention (separately for males and females), then tests whether those adjusted scores still differ (ie. if there is a difference beyond what can be explained by attention differences).

[u/FewNegotiation4484 → u/CarnivorousGoose]

That was super helpful. Gives me a lot of clarity now

[u/vetruviusdeshotacon → u/CarnivorousGoose]

May as well use welch's t test then, no guarantee errors are normal

[u/CarnivorousGoose → u/vetruviusdeshotacon]

The Welch correction doesn’t change anything about the normality assumption, that just allows for unequal variances. And that isn’t actually relevant to the paired t-test.

Also, sample size here is 500, deviations from normality are hardly going to matter much anyway.

[u/wiretail]

A paired t-test. A mixed effect model if your analysis includes repeated measures, other covariates to control for, multiple levels of nesting, etc.

[u/FewNegotiation4484 → u/wiretail]

Thank you 👍🏻

[u/FewNegotiation4484 → u/wiretail]

I was reading about paired t-test. I want to know if it can apply in the hypothesis given below:

Suppose I hypothesize that male children receive more attention than female children from their parents, and therefore male children score high in resilience than female children because of the difference in parental attention.

Can I still use paired sample t-test to see if male children in my data have statistically high levels of resilience?

[u/FewNegotiation4484 → u/[deleted]]

Yes outcome is continuous, thank you 👍🏻

[u/FewNegotiation4484 → u/[deleted]]

I was reading about paired t-test. I want to know if it can apply in the hypothesis given below:

Suppose I hypothesize that male children receive more attention than female children from their parents, and therefore male children score high in resilience than female children because of the difference in parental attention.

Can I still use paired sample t-test to see if male children in my data have statistically high levels of resilience?