One-Way Analysis    Targeting    Two-Way Analysis    Regression

The first step to working with data is getting to know the data. It is not usually the case that the data analyst personally developed the survey instrument, collected the data, and performed the data entry. For this reason, becoming familiar with the data set and what it represents is a good starting point.

The easiest way to visualize the data is to print out a codebook, something you can mark-up and use to identify the variables that will be most useful for the analysis. In SPSS, making a codebook is simple using the File option to Display data info. It might be a bulky printout, but usually it is worthwhile to have on hand throughout the analysis.

Through the codebook, the characteristics of the data set start to emerge. This book will allow classification of the variables collected, so that it is clear what information is available and what is not.

The purpose is to determine what biases (errors) have been introduced into the data and how you can eliminate or control for some of these. The biases might be inherent in the survey instrument (either the question itself or due to interviewer bias), in the collection of the data (measurement error), or in data entry (keying errors). Usually once you are at the stage of analysis, you can do little more than identify that there was a problem with the question used or the measurements taken in the survey and make note of it.  Refer to the section in  Data Cleaning in Analysis Module of PANDA to aid you in cleaning techniques.

Data cleaning and characterizing involves more than just error detection and correction though, it involves TRANSFORMING as well. By this time (after using ANALYSIS PANDA), transforming data has become routine. So making bi-variates or other categories from many familiar variables (such as water source, toilet use, and access to health care, etc.) will not be re-visited.