In attachment an R script for a possible way to analyse your data. There are 2 fabricated data sets. The first one is for the DV (heinous crime: yes/no) depending upon the number of combined IV's: 0, 1, 2 or 3. The second data set is for the dichotomy of combined IV's: 3 vs. Other (0, 1 or 2). The counts of heinous crimes depending upon IV's are analyzed with chi-square tests. You can run R script for free online: https://rdrr.io/snippets/

You can do a multiple standard regression analysis and then based on the squared part correlation you find out how many % of the dependent variable (crime) is explained by each of the 3 IV. You see which IV explains most of the variance in DV.

First, thank you for your responses and the R file. I am still fuzzy. I am including an example Strains Table with some questions that need clarification. These questions may seem basic so I apologize in advance.

Would the combination of (predictor variables) PV1+PV2+PV3 = a new PV of PV4?PV1 + PV2 + PV3 = PV4 (Cumulative Strain)

The presence of all three variables equate to cumulative strain (PV4) and the lack of one or more of the variables would not equate to cumulative strain (Other). If PV4 consists of a large % then it indicates a strong correlation with heinous crime.

Which analysis would be used to add all three PVs to determine the number and % of subjects (participants) that equate to PV4 or would that have to be tallied by hand - a YES/NO situation? After PV4 and Other are ascertained which analysis would be used to determine the strength of the PV4 and Other to heinous crime.

Or is cumulative strain a second dependent variable? The cumulative strain is dependent on the presence of the 3 PVs… If PV1+PV2+PV3 = YES or 1 – There is the presence of Cumulative Strain

If any of the PVs is no or 0 then PV1, PV2, PV3 = NO or 0 – Cumulative Strain is not present. Then would Cumulative strain become a predictor variable since the prediction is that the presence of all three strains results in cumulative strain which correlates to heinous crime.

Hopefully, this is not terribly confusing!!

Thank you in advance for your help!

Beside Jos Feys 's great proposition, have you tried to consider partial least square structural equation models (PLS-SEM) [1]? These models are often applied in sociology and economy, and can be useful when complex relationships between independent variables exist. If I am not mistaken, this sounds pretty much what you are after. The paper in [1] is more an introductory paper, easy to read and with examples in R.

[1] Hamdollah Ravan, Purya Baghaei. 2016. Partial Least Squares Structural Equation Modeling with R. Article Partial Least Squares Structural Equation Modeling with R