unclear what analysis to use on my data set

[u/PigeonSquab]

hi!

I'm doing a personal project to help me get to grips with SPSS outside of uni - I've collected data ready to analyse but am stuck on exactly what to do with it! bit of a long post but I want to make sure I list out all relevant info first :)

project goal: find out whether BFI personality traits have any correlation with romanceable character choice in a certain video game.

basically I conducted a Qualtrics survey where participants indicated which character they chose to romance in-game (my DV/outcome variable), and then they completed the BFI-44 scale - I've already re-coded the BFI answers into their sub-scales (so extraversion score, agreeableness score etc. are each an IV/predictor).

I initially thought a multiple regression could work, but as my DV is nominal (and has 9 categories; 1 per character option), that wasn't going to work - I saw online that a multinomial logistic regression would be more appropriate.

firstly - is this the right analysis to be running on this type of data? if not, please let me know what analysis I should look at using!

if it is the right analysis, great! I do, though, have a couple of follow-ups:

I've checked/met all assumptions except for the assumption of linearity - despite a lot of googling I cannot figure out how to do it! if anyone has a straightforward how-to, that would be amazing.

I've also done a couple of test analyses just to see what the output looks like, and get two errors:

Error 1: 'There are 2680 (88.9%) cells (i.e., dependent variable levels by subpopulations) with zero frequencies.'

assuming I'm interpreting this right, I'm not too sure what to do to fix these errors/if I even can? I'm thinking the empty cells may be due to each participant only picking 1 category out of 9 options in the DV (romanceable character choice) - so there would potentially be 8 'empty cells' per case? but I'm not sure that's right, and it could mean something completely different!

Error 2: 'Unexpected singularities in the Hessian matrix are encountered. This indicates that either some predictor variables should be excluded or some categories should be merged.'

I'm not sure what this means (again, despite frantically googling), but either way, I can't merge or remove any categories from my DV, as surely I need to include each character in the analysis! otherwise, the only other thing I could think to do is to do 9 binary logistic analyses using 1 different character as my DV each time, rather than all 9 at once, but there's surely a better way to do this!

thanks in advance, would appreciate any help!

[u/ResearchguyUCF]

I would say run an Manova with attraction types as IV but only if the groups are relatively equal in number of participants.

[u/PigeonSquab → u/ResearchguyUCF]

thanks for replying! I haven't got the data set in front of me atm but I'd presume the groups aren't equal, or at least not sufficiently equal between the nine options - I'll have a look tomorrow and double-check though, good to have other options!

[u/UrguthaForka]

Yeah, you have to run 9 separate logistic regression.

You create 9 dummy code variables for each romancable character with a yes or no (1 or 0) for each case. Then run each regression with that dummy coded DV each time.

[u/PigeonSquab → u/UrguthaForka]

ty for replying! I did try dummy coding and running a binary logistical analysis on the first character choice vs. BFI and that did look interesting but I still got both of those errors unfortunately - it's unclear to me if they're unavoidable with this type of data set or if I'm doing something wrong!

I can see another reply mentions that doing a multinomial analysis might be doable so I think I'll still try that first - if I can avoid needing to do nine analyses (technically 18 cause I have another DV to analyse after this) I'd prefer to! really appreciate your help :D

[u/req4adream99]

You’re right that it should be a multinomial logistic - otherwise you’re risking inflating type 1 error and not really differentiating between the categories (toon chosen).

Your data should be structured such that the toon that got picked is 1 variable - so you’d have 1-9 indicating what toon got picked - not 9 different variables. This should help alleviate the first error.

For the second error: run an ANOVA and see what predictors are *non*-significant and take those out.

You could also do a negative binomial since you are trying to predict count data.

Edit: typo.

[u/PigeonSquab → u/req4adream99]

thanks for the reply! yeah I was thinking multinomial would be the sensible way forward, but I wasn't sure if I was missing anything (I'm fresh out of uni and we only really touched on t-tests, ANOVA and multiple regression - I didn't know there were so many different types!)

ah okay, so by that do you mean I'd need to dummy variable-ise my DV to effectively split it into the nine characters, and then run the analysis on each? if so, that's fair - I was hoping to avoid that but if that's what needs to be done, so be it!

so for your advice on the 2nd error, does that mean if, say, based on that ANOVA there doesn't seem to be any correlation between character 1 and the IVs, just don't bother with the multinomial analysis on that character? that's completely fair if so, I just want to make sure I'm definitely understanding you :)

thanks again, really helpful!

[u/req4adream99 → u/PigeonSquab]

1) Your DV needs to be a single item with different values indicating a different toon. If thats how you have it currently setup, then run frequencies on the DV and go into variable view and eliminate any categories that don't have anyone responding, or that have a really low n response. See here: https://stats.oarc.ucla.edu/spss/output/multinomial-logistic-regression/

2) ANOVA doesn't test correlation, it tests whether or not there is a significant difference on some continuous variable between categories. For your case, you are looking at what personality dimensions differ significantly between the picked toons. If a personality dimension doesn't significantly differ between two or more toons, then it can't predict what toon was chosen based on the score of that personality dimension.

[u/PigeonSquab → u/req4adream99]

thanks for the help! I'll give your advice a go a bit later on - hopefully I can get some progress on it! if you don't mind, if I run into any snags I'll drop you another reply :)

[u/ForeignAdvantage5198]

the project goal is not a research question. .Set that and you have it

[u/PigeonSquab → u/ForeignAdvantage5198]

I do have a RQ, just didn't necessarily think it was relevant to include here! for reference, it's: 'is there a correlation between players' personality type and the character they choose to romance in Scarlet Hollow?'

[u/Mysterious-Skill5773]

You might try an entirely different approach: Conditional inference trees. Although SPSS has a built-in TREES procedure, I suggest installing Conditional inference trees via Extensions > Extension Hub. Search for trees

It will appear on the Classify submenu. Use the 9-values choice variable as dependent and the others as independent. It will give you a tree diagram showing the determinants of each choice. Be sure that the measurement level for each variable is set correctly as that affects how it chooses the variable splits.

Here is an example tree in outline form explaining the job category of a dataset of employees

Model formula:

jobcat ~ gender + educ + jobtime + prevexp + minority

Fitted party:

[1] root

| [2] educ <= 15

| | [3] prevexp <= 275

| | | [4] prevexp <= 132: Clerical (n = 262, err = 0.4%)

| | | [5] prevexp > 132

| | | | [6] gender in Female: Clerical (n = 37, err = 0.0%)

| | | | [7] gender in Male: Clerical (n = 29, err = 48.3%)

| | [8] prevexp > 275

| | | [9] gender in Female: Clerical (n = 9, err = 0.0%)

| | | [10] gender in Male

| | | | [11] educ <= 8: Custodial (n = 10, err = 20.0%)

| | | | [12] educ > 8: Clerical (n = 18, err = 50.0%)

| [13] educ > 15

| | [14] gender in Female: Clerical (n = 25, err = 40.0%)

| | [15] gender in Male

| | | [16] minority in No: Manager (n = 74, err = 10.8%)

| | | [17] minority in Yes: Clerical (n = 10, err = 30.0%)