Dear Ms. Beck!

I am a HUN&FIN researcher of supply chain mgt, not psychology. Still I want to help YOU and I think I can into a certain extent. I found an article that applies hierarchal regression analysis and this work is from within your field:

Vermote, B., Waterschoot, J., Morbée, S. et al. Do Psychological Needs Play a Role in Times of Uncertainty? Associations with Well-Being During the COVID-19 Crisis. J Happiness Stud (2021). https://doi.org/10.1007/s10902-021-00398-x Free access: Article Do Psychological Needs Play a Role in Times of Uncertainty? ...

I found also open access- case-studies:

a) Grossi, N.R., Batinic, B. & Moharitsch, S. Sleep and health: examining the relation of sleep to burnout and well-being using a consumer fitness tracker. Health Technol. (2021). https://doi.org/10.1007/s12553-021-00603-0 Open accss

Article Sleep and health: examining the relation of sleep to burnout...

b) Warrier, U., Shankar, A. & Belal, H.M. Examining the role of emotional intelligence as a moderator for virtual communication and decision making effectiveness during the COVID-19 crisis: revisiting task technology fit theory. Ann Oper Res (2021). https://doi.org/10.1007/s10479-021-04216-8 Open access:

Article Examining the role of emotional intelligence as a moderator ...

Hope my work is of some value for you in searching solutions for your problem.

Have a Nice DAY!

Yours sincerely, Bulcsu Szekely

Siobhan Beck The global alternative hypotheses H1 for a hierarchical linear regression would be that the overall R-squared value is greater than 0 (H1: R^2 > 0) and/or that the change (increase) in R^2 between two models > 0.

More specific alternative hypotheses would be that individual regression coefficients B_j for specific predictor (independent) variables X_j are either > 0 or < 0, depending on the hypothesized linear relationship with the dependent variable. For example, for your predictor conscientiousness, you would probably formulate the alternative hypothesis H1 as B_consc. > 0, assuming that the linear relationship between your dependent variable academic performance and conscientiousness is positive (the higher the conscientiousness score, the stronger the academic performance).

Good on you on asking a very pertinent question.

Two things

1) what do you mean by hierarchical regression: there are at least two (very different) meanings?

2) put simply a hypothesis is a tentative research question that can be answered by empirical evidence - the data - you have or you can collect; so you need a list of answerable research questions which are about relations between the outcome and an 'exposure' The null hypothesis of no effect is that there is no relation, put simply , the conditional regression coefficient (B1) is zero There are all sort of variants on this, but given where your at, this is appropriate.

More important is what you think is going on - theory! This a very useful tutorial on the roles of covariates: http://dagitty.net/learn/graphs/roles.html

The consequences are important:

"the covariates in a statistical analysis can have a variety of different roles from a causal inference perspective: they can be mediators, confounders, proxy confounders, or competing exposures. If a suitable set of covariates can be identified that removes confounding, we may proceed to estimate our causal effect using a multivariable [IE Standard] regression model. In linear regression models, there are only two types of variables: the dependent variable (DV) and independent variables (IVs, or predictors). No further distinction is made between the IVs – specifically, the exposure is by no means a "special" IV and is treated just like any other IV. Thus, as you can see, there is a conceptual mismatch between causal theory (DAG) that leads us to formulate a multivariable regression model (that highlights the exposure-outcome relationship and associated statistical adjustment for confounding) and the regression model itself. This conceptual mismatch can easily lead to misinterpretation of the results from a multivariable regression model. "

(source: http://dagitty.net/learn/graphs/table2-fallacy.html)

There is no escaping of the need to think; you just cannot press a button or let a best fitting algorithm get to you a dodgy answer that will not bare scrutiny.

This is my answer taken from https://www.researchgate.net/post/When_do_we_use_multiple_regression_analysis

In my view regression is better for calibrating a model than 'discovering' a good one.