Hi Santosh,

If there is single  response and single predictor, it is known as simple regression.

If there is one response and multiple predictors, it is called multiple regression.

If there is multiple response variables and one predictor, it is called multivariate regression.

If there is multiple response variables and multiple predictors, it is called multivariate multiple regression.

Your problem falls in 3rd category, so it is  multivariate regression.

Analysis in SPSS:

Multivariate regression is done in SPSS using the GLM-multivariate.option. Here is a nice tutorial on the same:

 https://statistics.laerd.com/spss-tutorials/one-way-manova-using-spss-statistics.php#procedure

IBM support doc can also be useful: http://www-01.ibm.com/support/docview.wss?uid=swg21476743

Analysis in R:

If you prefer using R, the this link may be useful:

http://www.psych.yorku.ca/lab/psy6140/lectures/MultivariateRegression2x2.pdf

R package mvtnorm can be used for this purpose.

MANOVA: It is used when there are two or more dependent variables. It helps to answer : 1. do changes in the independent variable(s) have significant effects on the dependent variables; 2. what are the interactions among the dependent variables and 3. among the independent variables. [...] MANOVA is most effective when dependent variables are moderately correlated (.4 - .7). If dependent variables are too highly correlated it could be assumed that they may be measuring the same variable. It is also recommended to not include dependent variables that are supposedly measuring the same construct in MANOVA (Tabachnick & Fidell, 2007). (orig. wikipedia)

How many DVs do you actually have?

MANOVA: one categorical, IV and two or more continous, dependent variables

Not sure what type of variables you have, are they continuous or categorical? Well, if they are continuous you could try partial correlation as a first step then as others have said try Principal Component Analysis or perhaps Factor Analysis.

Thanks Peter

Hi Santosh,

If there is single  response and single predictor, it is known as simple regression.

If there is one response and multiple predictors, it is called multiple regression.

If there is multiple response variables and one predictor, it is called multivariate regression.

If there is multiple response variables and multiple predictors, it is called multivariate multiple regression.

Your problem falls in 3rd category, so it is  multivariate regression.

Analysis in SPSS:

Multivariate regression is done in SPSS using the GLM-multivariate.option. Here is a nice tutorial on the same:

 https://statistics.laerd.com/spss-tutorials/one-way-manova-using-spss-statistics.php#procedure

IBM support doc can also be useful: http://www-01.ibm.com/support/docview.wss?uid=swg21476743

Analysis in R:

If you prefer using R, the this link may be useful:

http://www.psych.yorku.ca/lab/psy6140/lectures/MultivariateRegression2x2.pdf

R package mvtnorm can be used for this purpose.

if we have 1 IV and 1DV but DV consisted two more categories which test should be applied?

I have a similar question. Is MANOVA also used when the dependent variables have multiple levels?

For example:

The independent variable has a control and experimental condition. One dependent variable is the intensity rating of different emotions. The levels for this dependent variable would be the type of emotion (i.e. fear, joy, surprise, etc.).

Additionally, I am measuring affect scores. The levels for this dependent variable would be positive and negative affect.

I would expect there to be some sort of relationship between the positive emotions and positive affect as well as the the negative emotions and negative affect.

Would MANOVA also be appropriate for this case? (1 IV, 2 DVs each with multiple levels)

Steve: I would consider your proposed example to have 4 different DVs in this case, instead of 2 DVs with two levels. On that regard, then a MANOVA would seem appropriate because you're examining 4 different main relationships (IV-DV1, IV-DV2 and so on).

I have a similar question that I was hoping someone may be able to help me with -

I have one independent variable (intervention or control group) and two dependent variables (which are both assessments) I also want to compare the intervention and control groups at three time points (baseline, immediately post-therapy and six weeks post-therapy) Which statistical test should I use? Thank you!

Should we not keep in mind the type of variable Nominal, ordinal, interval of the IV and DV while prescribing tests?

Is there any way to analyze the interaction effects among only outcome variables?

My question is also similar one.i have one independent variable (intervention) and three dependent variables.want to compare intervention group and control group at two points- pre-test post test.i also want to see if there is any interaction among 3 dependent variables. Which statistical analysis techniques I should use?

I have one dependent variable and 9 independent variables, which analysis should i perform to analyze my data?

Since you have more than 2 independent variables, you can perform a factorial Anova. It compares means across two or more independent variables.  Good luck.

I think linear regression will answer your problem.

Hafiz Muhammad Ahmad you can use the Pearson correlation analysis for all of the variables, It will give you the correlation of dependent with independent and inter-correlation as well.

Ahmad Ali Thanks