Steps in the Data Analysis Process 

Step 1  Pose a Question  
Step 2  What to Measure and How Measurement generally refers to the assigning of numbers to indicate different values of variables. If your research involved determining if there was a relationship between height and weight of dogs, it would make sense to measure the height and weight of dogs using a scale. But if you were attempting to determine if there is a correlation between selfesteem and achievement of fifth grade girls, what scale could you use? Would you look at grades, scores on a IQ test, how well each girl created a project? How can such abstract concepts like intelligence or selfesteem be measured?


Step 3  Collecting Data Once you have decided what types of data you need for your study, you can determine whether your data can be gathered from existing sources/databases or whether you need to collect new information through other means. Even when using existing data, it is important to know how the data was collected so that the limitations of the generalizability of results may be determined and the proper analyses may be performed. The researcher should be able to select and defend an appropriate method of data collection.


Step 4  Summarizing and Displaying Data Measures of Central Tendency indicate what is typical of the average subject Measures of Variance indicate the distribution of the data around the center. Correlation refers to the degree to which two variable move in sync with one another. Graphical Displays are powerful tools for teaching and persuasion. A picture is really worth a thousand words as many people understand pictures better than a lecture.


Step 5  Analyzing Data and Interpreting Results Hypothesis Testing: Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. At the beginning of your research project, you no doubt started with an idea you thought might be true. Perhaps you think that the oral polio vaccine causes HIV. This is your hypothesis. No matter how much data you collect, there is always a chance that someone might contract HIV due to "chance" (all other reasons besides the vaccine). So in order to gather support for your claim, you have to first nullify the idea that the phenomenon occurred due to chance. In hypothesis testing, we look to reject or fail to reject the null hypothesis. Rejecting the null hypothesis gives credibility to your hypothesis being true, but at this point more research would probably still be in order. Failing to reject the null hypothesis does not mean the null hypothesis is true. It simply means that there is not enough data to prefer one hypothesis over the other. Hypothesis Testing
Still confused about conducting a hypothesis test? There are lots of software applications that can do the test for you and display the results in top notch fashion. See some statistical analysis packages, here. 