How to Run Ordinal Logistic Regression in SPSS? 

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You are most welcome. Do Share in your research circle.

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Thanks for watching. It is an example, there is no particular reason.

I hope i can soon make a video on that.

Pleasure. I am glad you liked the data. If the data is not normal, why not use Bootstrapping procedure in regression analysis. Otherwise you can transform the data as you have. Not all categories have to be significant.

Thanks for watching. In some of the categories there is no data.

Thanks for watching. Use Linear Regression.

Thanks for watching. No nothing yet.

Not yet!

Thanks for watching. You can write to me here.

​@@researchwithfawad Thank you so much Professor! My case is as follows: I have a set of demographic variables just like the one you used. My other set: 1-Contains multiple variables, each one representing a photo. 2-The photos go from most unnatural to most natural. 3-We asked every participants to rate each photo on a Likert scale (1-5) that went from most unnatural to most natural. 4-Every participant got half the photos picked randomly, not all, and in random order. 5-I want to know which photo (so which variable, as a whole) acts as the cut-off for every demographic group. For example: Photo #3 is the natural Cut-off for middle age group, photo #5 is the cut-off for high salary group, and so on.

I would like to see how you entered the data, can you share a sample on my email kh.fawad83@gmail.com

Normally, the regression technique is robust to minor violations of normality. Please check for skewness and kurtosis.

Yes, you may also interpret it like that.

Yes we will.

A redundant parameter is one that doesn't impact the outcome. I do not recommend its removal rather i recommend you to explain what a certain factor may be insignificant in the context of your study.

Thanks for your interest. You will need to take the sum of the individual items. Let say, i have Organizational Commitment measured using 4 items COM1, COM2, COM3, COM4 If you have it in SPSS, Go to Transform -> Compute Variable In the Target Variable Enter the Name of the New Variable that is to be created based on taking the average, let say COMM. In the numeric expression type in Mean(COM1, COM2, COM3, COM4) Press OK. The new variable is created at the end of the Data View and is also visit in the variable view. You have now composite score for each respondent that you can use in regression.

Hi. No. IVs can be continuous or discrete. DV in the ordinal regression has to be in ordinal.

Thank you

I am glad you liked it. Not every relationship is significant. If the impact is insignificant, you will have to provide justification as to the possible reasons for the insignificant results. This video may also help ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-xPZymvNG-gk.html

In this case Ordinal Regression is not performed. You will perform linear regression.

You can if the hypothesis is directional.

Do not use ordinal Logistic Regression when the DV is continuous as in your case with mean score.

@@researchwithfawad Which regression analysis should we use in above case (continuous)?

Hi. Are there any missing values in your data?

@@researchwithfawad Thanks for your answer, there are no missing values in my data. I checked for those...

The regression procedures for categorical dependent variables do not have collinearity diagnostics. However, you can use the linear Regression procedure for this purpose. Collinearity statistics in regression concern the relationships among the predictors, ignoring the dependent variable. So, you can run REGRESSION with the same list of predictors and dependent variable as you wish to use in LOGISTIC REGRESSION (for example) and request the collinearity diagnostics.

If p value is over 0.05 it is insignificant.

Yes, it shows if the results are significant or not.

If you take mean than it will no longer be ordinal.

OK dear. If take Median than remains catagorical?

Please refer to the test of parallel lines in the video.

0 would mean no effect.

@@researchwithfawad You are such a big help for my research! May Allah bless you! by the way is the result okay if the all parameter estimates are non significant? Thank for answering

In statistics, nonparametric tests are commonly used when working with ordinal data. Parametric tests, such as the Wald test, make assumptions about the underlying distribution of the data. The Wald test is a parametric test commonly used in regression analysis, including ordinal regression, which assumes that the dependent variable follows a particular distribution (e.g., a logistic distribution in the case of ordinal regression). The theoretical justification for using the Wald test in ordinal regression lies in the asymptotic properties of the estimator. The Wald test relies on the central limit theorem, which states that under certain conditions, the distribution of the estimator becomes approximately normal as the sample size increases. In the case of ordinal regression, the Wald test is based on maximum likelihood estimation, which has been shown to have asymptotic normality properties.

@@researchwithfawad thank you very much!

Yes, you can create interaction effect. You may standardize continuous variables and create interactions. Or for categorical no need to standardize.

@@researchwithfawad Thank you Prof for your feedback

Thanks. If you are looking for model development, you may watch the video in the attached playlist. How to Find Research Gaps/New Topic for Research: ru-vid.com/group/PLb7vm6tsQ3KslXSw1BOb3Q0yJnksLyi2Z

This means a negative relationship provided the IV is continuous.

The DV has to be on ordinal scale. IVs can be continuous.

If there a multiple items in the DV, you will need to take its mean and if the predictor categorical variables have more than two categories you will need to create dummy variables and use linear regression.

@@researchwithfawad yes sir i measure it with five item likert scale ..can i apply ols after taking mean

Yes

Hi. For instance, you have 5 dummy variables. The one that you do not add to the model serves as reference. That can be any dummy variable.

Thanks for watching. Use Ordinal when the DV is a categorical ordinal variable and for continuous DV, use Linear.

The dependent variable needs to be on ordinal scale.

Can you please provide some details? Are your referring to the fits?

For Odds Ratio, please watch ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-tlslGucrD_c.html

Which Likert Scale? If your DV is on Likert scale use Multiple Linear Regression?

@@researchwithfawad My DV is ordinal, a Likert scale (not motivated to highly motivated) how do i know if male (or for married) for example are moderately motivated or fairly motivated)? And whats the difference between parameter estimates and log of odd. thanks Its urgent huhu thanks for answering! God bless

What is your sample size?

@@researchwithfawad Actually the sample size is small (90 observations) but I changed IVs from factors to covariates and now it seems much more clear although most effects are not significant

Thanks for watching. Which threshold please

There was a threshold value including its estimates. I think it is just above to the coefficient estimates @@researchwithfawad

A positive or negative sign with the estimate in the Parameter Estimates table shows the likelihood of falling into a particular category. + sign is associated with an increase likelihood of case falling into a higher category in the dependent variable. - sign is associated with an increase likelihood of case falling into a lower category in the dependent variable. For instance, a Positive Sign with and IV with 5 Categories would mean that with increase in the value of that variable, there is a higher chance of falling into a higher category on the DV.

@@researchwithfawad Thank you for the response sir, very much appreciated. Perhaps my question was not clear enough, what I was intending to ask is if the variable has different signs at the same time at different categories. For instance a construct “happiness” with five categories and some categories have - sign while some have + sign in the estimates. How would one interpret happiness? When it has some categories that are + and some that are - in the estimate at the same time.

Yes we should

Thanks for watching. In Variable view that is only for identification purposes and doesn't affect the analysis.

@@researchwithfawad thank you so much sir one request sir can you share any article with ordinal logistic regression for interpretation purpose pls

Please put in the following string in Google Scholar, and you will find a number of studies. allintext: "Ordinal Logistic Regression"

@@researchwithfawad thank you professor

Please visit researchwithfawad.com/index.php/download-research-resources/

No it is not acceptable. The difference should be insignificant.

My email is kh.fawad83@gmail.com

@@researchwithfawad thanks dr

Just check your email Dr.