My data have 4 demographic characteristics, each with two variables e.g gender ( Male, Female) age groups (up to 30, up to 60 years) and similar. My Questions (dependent variable) have three responses Agree, disagree and I don't know.
I want to know which Analysis I can use to explain my data. other than descriptive statistics.
Any suggestions will be appreciated.
Anam Khalid, you have not really provided enough information to be able to attract really informative advice, but given the simplicity of your independent variables (all dichotomous, it seems), perhaps you could start with something such as multiple regression using dummy variables.
If you provide more information about your research questions, that would help.
With regard to your dependent variable(s), "Don't know" is usually considered to be missing data. That would leave you with dichotomous , Agree/Disagree responses. Can say more about the kinds of statements that the participants are responding to?
Hello Anam,
Your specific research question(s) should be the guide for informing appropriate analysis/analyses of your data set. Without that information, I'm only able to make general suggestions (as Robert's response notes).
First, though, descriptive statistics are always a good idea, no matter what kind of variables you're working with!
Second, if you're interested in whether any of the demographic characteristics relate systematically with responses to an individual question (A, D, DK), then a chi-square test of independence would offer some insight. As David Morgan has posted many times, though, you tend to get a better-behaved variable from combining individual items to get some sort of total/summated/composite score (where it makes sense to do so) than by treating each individual item as a distinct DV.
Good luck with your work.
Hello Dear Anam Khalid
First of all, you need to determine how many sub-dimensions your dependent variable consists of. For this, I suggest you to do a factor analysis (explanatory or confirmatory). Then you need to do the normality test, which is the prerequisite of many analyzes. Depending on whether the data is normally distributed or not, you can analyze differences between demographic variables and dependent variables.
In addition, you can perform cholesterol analysis on sub-dimensions you determine with factor analysis in your dependent variable or multiple regression analysis as Robert Trevethan said. But the important thing is "what is your research question"? You must determine our research model before proceeding to the tests. You can choose one or more of the tests listed above under the research model
Best wishes...
Omer, I recommended your answer because I thought you made some useful suggestions, but I can't work out what you mean by "cholesterol analysis". Might you have mis-typed something? If so, what might you have intended, please? You might be making a good point, but it could be obscured.
Dear Anam Khalid
There are many methods you can use. It depends on your study objectives and study design. I mean if you need to identify the interaction between Gender and age, you can use Chi-square Test Identify differences between groups you can use ANCOVA.
Best regards
Dear Anam Khalid,
If you're already done with descriptive statistics in analyzing your data, then inferential statistics is what you can consider.
But as you know, the choose if your statistical tool is dependent on your Hypothesis.
So, if for example you're assessing the influence of age on the knowledge of your respondents.
You can use Chi-square test. But if you're comparing more than one demographic variable, you could also consider ANOVA.
I hope this helps you too.
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