I have a 12 item Likert scale for my predictor variable [IV], and a 9 item Likert scale for my dependent variable [DV]. I used SPSS and did factor analysis on both scales, and found that the IV had 3 components, while the DV had 2.  I would like to report the relationship between the IV and the DV using regression analysis. How to accomplish this based of the finds of the factor analysis?  Thanks in advance.
123413
I have a question about how to interpret a regression analysis I did following a factor analysis.  I did principal axis factoring (direct oblimin)  I got a 3 factor solution. The three factors were (1) feelings (2) kinship and (3) interactions. (I'm looking at father-child relationships)  Regarding factor loadings, (1) feelings had all positive factor loadings, (2) kinship had all negative factor loadings and (3) interactions had all negative loadings.   I did a multiple regression analysis using the factor scores - saved as regression scores - as outcome variables. I'm not bringing them together to form a total scale, so I don't think I need to do a MANOVA instead, or adjust the p-value.  For (1) feelings, all of my predictor variables have positive beta weights  For (2) kinship, one of my predictors (participation in healthcare) has a negative beta weight  For (3) interactions, again, one of my predictors (participation in healthcare) has a negative beta weight  I'm confused about whether the predictors with negative beta weights actually imply that less participation in healthcare means a higher score on kinship and on interactions. Or whether, because those two outcome variables have all negative factor loadings, should the negative beta be interpreted as a positive beta? (i.e. do the negatives cancel each other out?).  If the latter is the case, do the predictors with a positive beta for outcome variables (2) and (3) actually have negative betas?
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I am using a scale which consists of discrete values 0 (normal), 1 (mild), 2 (moderate), 3 (severe). I have used this scale for 200 patients.   I am going to find the correlation of this scale with some continuous variables, these variables also measured for 200 patients.   I don't think I can use multivariate or multiple regression because the dependent variable is discrete and the independent variables continuous. Can you please suggest a method to find this correlation?
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The task is to build a regression model for individuals. I have all the independent variables for each individual, but the dependent variable only as an aggregates on group-level.  Lets say, I am trying to predict the score a student will achieve at some test. I have information about the student that can be used as predictor-variable (f.e. time spent studying). But the results of the test are only given as aggregated sums for each class. I can link every student to a class, but I don't know individual test-results.  One potential way I can think of would be to aggregate the independent variables too and run the regression completely on the aggregated data.  But it's probably rarely the case that correlation on aggregated level and individual level are the same. So I don't know how to make any judgement about the validity of such an approach.  Is there any 'good'(or less bad) approach to this problem?
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It looks like you can use coding for one categorical variable, but I have two categorical and one continuous predictor variable. Can i use multiple regression for this in SPSS and if so how? thanks!
118755
I have a ordinal probit model. The dependent variable, say walkability, is a Likert scale variable (1,2,3). The main independent variable, say connectivity, is also a Likert scale variable (1,2,3). The other variables are dummies. There are two things that I am getting confused by:   When I run the ordinal probit model in Stata, it gives one coefficient, std. error, p-value for the independent variable (connectivity). Shouldn't I have statistics for all options? Is there a specific command for it? Or is it ok to have one value? How do we compute marginal effects when both dependent and independent variables are discrete (1,2,3 scale variables)?
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I'd like to estimate the correlation between:  An ordinal variable: subjects are asked to rate their preference for 6 types of fruit on a 1-5 scale (ranging from very disgusting to very tasty) On average subjects use only 3 points of the scale.  A continuous variable: the same subjects are asked to quickly identify these fruits, which results in an mean accuracy for the 6 fruits.   Is Spearman rho the best method to analyze these data and/or are there other good methods I could consider?
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Lets assume you are a social science researcher/econometrician trying to find relevant predictors of demand for a service. You have 2 outcome/dependent variables describing the demand (using the service yes/no, and the number of occasions). You have 10 predictor/independent variables that could theoretically explain the demand (e.g., age, sex, income, price, race, etc). Running two separate  multiple regressions will yield 20 coefficients estimations and their p-values. With enough independent variables in your regressions you would sooner or later find at least one variable with a statistically significant correlation between the dependent and independent variables.   My question: is it a good idea to correct the p-values for multiple tests if I want to include all independent variables in the regression? Any references to prior work are much appreciated.
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I found a reference in an article that goes like:     According to Tabachnick &amp; Fidell (1996) the independent variables with   a bivariate correlation more than .70 should not be included in   multiple regression analysis.   Problem: I used in a multiple regression design 3 variables correlated >.80, VIF's at about .2 - .3, Tolerance ~ 4- 5. I cannot exclude any of them (important predictors and outcome). When I regressed the outcome on the 2 predictors which correlated at .80, they remained both significant, each predicted important variances, and these same two variables have   the largest part and semipartial correlation coefficients amongst all 10 variables included (5 controls).   Question: Is my model valid despite high correlations? Any references greatly welcomed!    Thank you for the answers!  I did not use Tabachnick and Fidell as a guideline, I found this reference in an article dealing with high collinearity amongst predictors.   So, basically, I have too few cases for the number of predictors in the model (many categorical, dummy coded control variables- age, tenure, gender, etc) - 13 variables for 72 cases. The Condition Index is ~ 29 with all the controls in and ~23 without them (5 variables).   I cannot drop any variable or use factorial analysis to combine them because theoretically they have sense on their own. It is too late to get more data. Since I am conducting the analysis in SPSS perhaps it would be best to find a syntax for ridge regression (although I haven't done this before and interpreting the results would be new to me).   If it matters, when I conducted stepwise regression, the same 2 highly correlated variables remained the single significant predictors of the outcome.  And I still do not understand if the partial correlations which are high for each of these variables matter as an explanation for why I have kept them in the model (in case ridge regression can't be performed).   Would you say the "Regression diagnostic: identifying influential data and sources of collinearity / David A. Belsley, Edwin Kuh and Roy E. Welsch, 1980" would be helpful in understanding multicollinearity? Or might other references be useful?
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Here is the article that motivated this question:   I liked this article, and it nicely demonstrates the concept of “controlling for other variables” (IQ, career, income, age, etc) in order to best isolate the true relationship between just the 2 variables in question.    Can you explain to me how you actually control for variables on a typical data set?      E.g., if you have 2 people with the same impatience level and BMI, but different incomes, how do you treat these data?  Do you categorize them into different subgroups that do have similar income, patience, and BMI?  But, eventually there are dozens of variables to control for (IQ, career, income, age, etc)  How do you then aggregate these (potentially) 100’s of subgroups?  In fact, I have a feeling this approach is barking up the wrong tree, now that I’ve verbalized it.  Thanks for shedding any light on something I've meant to get to the bottom of for a few years now...!
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I am doing a simple marketing project that has the following types of variables:   X1 - continuous (e.g. income) X2 - categorical (e.g. gender) Y - continuous (e.g. number of a product type purchased such as tubs of ice-cream)    I am interested in the relationship between income (X1) and product purchase (Y) but also the effect of gender (X2) on this relationship. (i.e. interaction or moderation effect).  I have centered X1 and have used the general linear model in SPSS. The result on Y is as follows:   X1 - significant X2 - not significant X1*X2 - not significant   How do I interpret this result in terms of main effect and interaction?
53181
I am trying to analyze data based on a sample size of around 50 and would appreciate advice on which statistical test would be most appropriate. Participants, on a single questionnaire, were asked to complete two different sections. Variable 1: rating Likert items on a scale from 1-5 (strongly agree to disagree). Variable 2: rating Likert items of a validated tool on a scale of 1-9, which is averaged to obtain a single number.  My research question is: Do those who 'agree' with the statement in the first section significantly differ in terms of their scores on the variable 2, compared to those who 'disagree'? (ex. Do participants tend to 'agree' with "I like to read books for pleasure." have higher IQs (variable 2) than those who 'disagree'?)  I was advised by a colleague to run a one-way ANOVA with variable 1 (categorical) as IV and variable 2 (continuous) as DV.   Question 1: My independent variable is the three groups: 'agree', 'neutral', and 'disagree.' (I combined strongly agree with agree because of low frequencies and likewise for strongly disagree and disagree.) However, there are many more participants in the 'agree' category than in the 'disagree' category. The distribution is, on average, around 44 agrees, 3 neutrals, and 3 disagrees. Do the (very) unequal sample sizes prevent me from performing ANOVA, or any other tests? Which test would be most appropriate for this analysis?  Question 2: I would like to run analyses on all of the Likert items, which is more than 10 statements. (ex. Seeing how IQs correlate with agreement to many statements including “I like to read books for pleasure,” “I watch a lot of TV,” “I exercise more than 30 minutes each week,” etc.) I was planning to do a Bonferroni correction to adjust for the multiple tests. Would this be correct, and help make my analyses more robust? Note: The continuous data are not normally distributed (there is negative skewness), but I seem to be able to correct the skewness using logarithmic transformation.  Any advice or suggestions for papers to read would be appreciated. Thank you!
33215
I have a dataset with a large number of Yes/No responses. Can I use principal components (PCA) or any other data reduction analyses (such as factor analysis) for this type of data? Please advise how I go about doing this using SPSS.
68512
I have an independent variable called "quality"; this variable has 3 modalities of response (bad quality; medium quality; high quality). I want to introduce this independent variable into my multiple linear regression. When I have a binary independent variable (dummy variable, I can code 0 / 1) it is easy to introduce it into a multiple linear regression model.  But with 3 modalities of response, I have tried to code this variable like this :  Bad quality      Medium quality      High quality       0                1                  0      1                0                  0      0                0                  1      0                1                  0   But there is a problem when I try to do my multiple linear regression: the modality Medium quality gives me NA:    Coefficients: (1 not defined because of singularities)    How can I code this variable "quality" with 3 modalities? Do I have to create a variable as a factor (factor in R) but then can I introduce this factor in a multiple linear regression?
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I have multiple datasets that I am attempting to use principal component analysis (PCA) on in order to infer the underlying structure of the data.  I'm attempting to predict growth increment of conifers (the response which apparently needed to be cube root transformed for my original linear models).  My predictor variables include: continuous (some log-transformed like size-related variables, some compositional, and some traditionally scaled); binary; ordinal; and nominal (of which I'm pretty sure I can order with respect to site productivity).  I'm mostly attempting to make inferences because some of my previous models from linear regression have not produced significant terms corresponding to categories like health or climate and I feel that they should have.  I've read in Joliffe (2002) that I might want to leave my compositional data uncentered.  Forgive my naiveté, but I'm wondering if I should also do this for my binary data.  It also seems that I might not want to scale these since they're already on a 0 - 1 scale but I'm not sure.  After only using my continuous predictors (including compositional variables), it appears that a size principal component makes up the largest portion of the variability and Joliffe also mentions that one might want to attempt a "double-centering" of the log-transformed data if this is the case.  I don't know if this is even possible since the log-transformed data only makes up a small portion of my datasets.  Finally, I should ask if having such a combination of data types will complicate analysis so much that it will make inferences unreliable.  I am currently using R for this analysis.  Thank you.
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A few days ago, a psychologist-researcher of mine told me about his method to select variables to linear regression model. I guess it's not good, but I need to ask someone else to make sure. The method is:      Look at correlation matrix between all variables (including Dependent   Variable Y) and choose those predictors Xs, that correlate most with Y.     He didn't mention any criterion.  Q: Was he right?   [I think that this selection method is wrong, because of many things, like it's the theory that says which predictors should be selected, or even  omitted variable bias (OVB).]
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I have 4 IVs in my model that directly effect the DV. The  of the correlation &amp; regression analyses showed that:  IV1&amp;DV:   Pearson Correlation Coefficient: insignificant  Regression Beta&amp;t values: significant  IV2&amp;DV:  Pearson Correlation Coefficient: significant  Regression Beta&amp;t values: insignificant  IV3&amp;DV:   Pearson Correlation Coefficient: significant  Regression Beta&amp;t values: significant  IV4&amp;DV:   Pearson Correlation Coefficient: significant  Regression Beta&amp;t values: significant  No multicollinearity problem was detected and also all the regression assumptions have been perfectly met!  Why the results of the regression analysis turned out to be completely opposite of correlation analysis for IV1 and IV2?!! Why they're being contradictory?! Does it make any sense? Is it acceptable? TQ.
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I did a principal component analysis, which resulted in 7 components that I am now using for a principal component regression on my independent variable.  However, I want to add 2 control variables, but these are not component scores, but "normal" variables (on a Likert scale, the same sort of variables my independent variables were).  Is that okay to do or do I have to make these into components as well? I have done the regression both with those two as variables and combined in a component and the results are practically the same.