Chapter 4 Analyses quant

You have collected all of your quantitative data, entered it into SPSS (or downloaded it), and double checked your data entry. Great… now what?? It can feel intimidating to view your first real data set and know you need to figure out what to do with it. I like to start with getting an overall feel for what is going on by calculating means and frequencies. Let’s take a moment and review what to use when.

If your variable is continuous, meaning there are no categories that you set up previous to the study you can calculate means and standard deviations. An example of a continuous variable would be where you asked people to enter their age today (e.g., 32, 56, etc.). Examples of categories would be gender: 1 = female, 2 = male or age: 1 = 20-30, 2 = 30-40, etc.). Hopefully, these terms are sounding familiar, if not go back to your stats book and review. A good reference for SPSS is

Pallant, J. (2013). SPSS survival guide, 4th ed. Berkshire England: McGraw Hill

For categorical variables (e.g., gender, education level) you can do a frequency table and get a feel for how your data looks. Make sure you don’t have any data entry errors- they will show up as a weird number, e.g., you have gender coded as 1 & 2 and a 21 shows up in the frequency table. Go back to the original data and double check it.

One issue you may need to consider is what to do with missing data, for example from people skipping questions. There are a number of ways to deal with this issue. Check with your methodologist to see what they prefer. They will probably ask you how many cells (individual data points) are missing, for which variables are they missing, and what is the largest number missing per individual, so be prepared for those questions.

Once missing data issues are resolved, my usual next step is run correlations between my variables just to get a feel for what is going on. I then do scatterplots for any that show up as significant. Again, I am just trying to get a feel for the data. My old undergrad stats professor used to say that you need to “take a bath in your data.” I like that idea, you need to understand the relationships before getting immersed in the formal data analysis.

You should have developed an analysis plan in your proposal, so now is the time to go to that. What happens if you just can’t figure things out? Contact your committee members and ask for help. As a committee member I sometimes have students send me their data set and I play with it a little, then I can talk them through issues.

Sometimes students decide to hire statistical consultants. Personally, I am not a fan of this. I prefer that the student figure out the stats with the help of his or her committee. The problem with a consultant is that you don’t really understand what they did and why. Even if they explain it, you really don’t have the level of understanding that you should. It misrepresents your knowledge level. People reading your dissertation will assume that you did the analyses and are capable of doing it (and perhaps teaching it!) again. If you must use a stats consultant, my advice is to rerun all of the analyses that they do, so you understand them too.

Another aspect to consider, is keeping track of all the analyses that you run and what they show. There are several ways to do this. You can simply save all of your SPSS outputs in a separate file on your computer (my least favorite, because then you have to reopen each to see it). Another way is to print out all of your data outputs and save them in a file or binder. My own favorite way to keep my data output is to copy it or rewrite it as I go into a word file. The advantage of this is that I am keeping everything together that is relevant (you will generate a lot of irrelevant info as you go). Do keep in mind that SPSS tables are not in APA format, so any that you want to use in your paper will need to rewritten.

I also find it helpful to think through what I am finding with each analysis (even though this technically goes in c. 5, I find it helpful to think about it at the analysis stage). Let’s work through an example, I find that my variable education level is correlated with my dependent variable, emotional intelligence (EI) total score. So my first question is which education level has a higher EI score? I could do a scatterplot or could just calculate the means for each education level (use analyze/ descriptive stats/explore). I then find that people with a graduate degree have a higher emotional intelligence score than people with a high school diploma in the sample. Is that what previous research has found? What if this is not the relationship other researchers have reported? I need to consider why my sample may be different. I check what else is correlated with education- perhaps I find that for this sample, gender is highly related to emotional intelligence. Do another scatterplot between gender and education. Whoa- all of the graduate level participants are female. Could that be the cause of the education and emotional intelligence score correlation?

Remember your methodologist can help with any issues you may find. There are also statistical consultants available for tricky issues, a faculty member can ask questions on your behalf. 

Next time we will have a guest author for Christmas. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu