There are many different ways to approach data analysis preparation for quantitative studies. This post will provide some insight into how to do this. In particular, we will look at the following steps in quantitative data analysis preparation.
- Scoring the data
- Deciding on the types of scores to analyze
- Inputting data
- Cleaning the data
Scoring the Data
Gender: Male(1)____________ Female(2)___________
I think school is boring
- Strongly Agree
- Strongly Disagree
In the example of above, the first item about gender has the value 1 for male and 2 for female. The second item asks the person’s perception of school from 1 being strongly agree all the way to 5 which indicates strongly disagree. Every response was given a numerical value and it is the number that is inputted into the computer for analysis
Determining the Types of scores to Analyze
Once data has been received, it is necessary to determine what types of scores to analyze. Single-item score involves assessing the results from how each individual person responded. An example would be voting, in voting each individual score is add up to determine the results.
Another approach is summed scores. In this approach, the results of several items are added together. This is done because one item alone does not fully capture whatever is being measured. For example, there are many different instruments that measure depression. Several questions are asked and then the sum of the scores indicates the level of depression the individual is experiencing. No single question can accurately measure a person’s depression so a summed score approach is often much better.
Difference scores can involve single-item or summed scores. The difference is that difference scores measure change over time. For example, a teacher might measure a student’s reading comprehension before and after teaching the students basic skills. The difference is then calculated as below
- Score 2 – Score 1 = Difference
Inputting data often happens in Microsoft Excel since it is easy to load an excel file into various statistical programs. In general, inputting data involves giving each item its own column. In this column, you put the respondent’s responses. Each row belongs to one respondent. For example Row 2 would refer to respondent 2. All the results for respondent 2 would be in this row for all the items on the instrument.
If you are summing scores are looking for differences, you would need to create a column to hold the results of the summation or difference calculation. Often this is done in the statistical program and not Microsoft Excel.
Cleaning data involves searching for scores that are outside the range of the scale of an item(s) and dealing with missing data. Out range scores can be found through a visual inspection or through running some descriptive statistics. For example, if you have a Lickert scale of 1-5 and one item has a standard deviation of 7 it is an indication that something is wrong because the standard deviation cannot be larger than the range.
Missing data are items that do not have a response. Depending on the type of analysis this can be a major problem. There are several ways o deal with missing data.
- Listwise deletion is the removal of any respondent who missed even one item on an instrument
- Mean imputation is the inputting of the mean of the variable wherever there is a missing response
There are other more complicated approaches but this provides some idea of what to do.
Preparing data involves planning what you will do. You need to consider how you will score the items, what type of score you will analyze, input the data, and how you will clean it. From here, a deeper analysis is possible.