LOG BOOKS
If you are working on a research paper, seriously consider using a research log to keep track of the progress of your research. Some of the reasons for keeping a research log are:
- to keep track of the sources you have used and those you still need to locate;
- to keep track of various catalogues, indexes, and other electronic tools you have consulted and the search terms you used in each;
- to record what you expect to find in a source and how the source actually panned out;
- to bring clarity to your thinking about specific issues you are researching;
- to try out new ideas and hypotheses;
- to record frustration, surprise or excitement as your research unfolds.
In all but the simplest of studies, you need to set up a procedure for logging the information and keeping track of it until you are ready to do a comprehensive data analysis. Different researchers differ in how they prefer to keep track of incoming data. In most cases, you will want to set up a database that enables you to assess at any time what data is already in and what is still outstanding. You could do this with any standard computerised database program (e.g. Microsoft Access). A database for logging incoming data is a critical component in good research record-keeping.
Collecting and Logging Data
Data Preparation involves checking or logging the data in; checking the data for accuracy; entering the data into the computer; transforming the data; and developing and documenting a database structure that integrates the various measures.
In any research project you may have data coming from a number of different sources at different times. For example:
- mail surveys
- coded interview data
- pre-test or post-test data
- observational data
In all but the simplest of studies, you need to set up a procedure for logging the information and keeping track of it until you are ready to do a comprehensive data analysis.
Checking Data for Accuracy
As soon as data is received you should screen it for accuracy. In some circumstances doing this right away will allow you to go back to the sample to clarify any problems or errors. There are several questions you should ask as part of this initial data screening:
- Are the responses legible/readable?
- Are all important questions answered?
- Are the responses complete?
- Is all relevant contextual information included (e.g., data, time, place, researcher)
In most social research, quality of measurement is a major issue. Assuring that the data collection process does not contribute inaccuracies will help assure the overall quality of subsequent analyses.
Developing a Data Base Structure
The database structure is the manner in which you intend to store the data for the study so that it can be accessed in subsequent data analyses. You might use the same structure you used for logging in the data or, in large complex studies, you might have one structure for logging data and another for storing it.
There are generally two options for storing data on computer -- database programs and statistical programs. Usually database programs are the more complex of the two to learn and operate, but they allow the analyst greater flexibility in manipulating the data.
In every research project, you should generate a printed code book that describes the data and indicates where and how it can be accessed. Minimally the code book should include the following items for each variable:
- variable name
- variable description
- variable format (number, data, text)
- instrument/method of collection
- date collected
- respondent or group
- variable location (in database)
- notes
The code book is an indispensable tool for the analysis team. Together with the database, it should provide comprehensive documentation that enables other researchers who might subsequently want to analyse the data to do so without any additional information.
Data Transformations
Once the data have been entered it is almost always necessary to transform the raw data into variables that are usable in the analyses. There are a wide variety of transformations that you might perform. Some of the more common are
- Missing Values
- Item Reversals
- Scale Totals
- Categories
Missing Values
Many analysis programs automatically treat blank values as missing. In others, you need to designate specific values to represent missing values. For instance, you might use a value of -99 to indicate that the item is missing. You need to check the specific program you are using to determine how to handle it. Many analysis programs automatically treat blank values as missing. In others, you need to designate specific values to represent missing values. For instance, you might use a value of -99 to indicate that the item is missing. You need to check the specific program you are using to determine how to handle missing values.
Item Reversals
On scales and surveys, we sometimes use reversal items to help reduce the possibility of a response set. When you analyse the data, you want all scores for scale items to be in the same direction where high scores mean the same thing and low scores mean the same thing. In these cases, you have to reverse the ratings for some of the scale items.
For instance, let's say you had a five point response scale for a self esteem measure where 1 meant strongly disagree and 5 meant strongly agree. One item is "I generally feel good about myself." If the respondent strongly agrees with this item they will put a 5 and this value would be indicative of higher self esteem. Alternatively, consider an item like "Sometimes I feel like I'm not worth much as a person." Here, if a respondent strongly agrees by rating this a 5 it would indicate low self esteem.
To compare these two items, we would reverse the scores of one of them (probably we'd reverse the latter item so that high values will always indicate higher self esteem). We want a transformation where if the original value was 1 it's changed to 5, 2 is changed to 4, 3 remains the same, 4 is changed to 2 and 5 is changed to 1. While you could program these changes as separate statements in most program, it's easier to do this with a simple formula like:
New Value = (High Value + 1) – Original Value
In this example, the High Value for the scale is 5, so to get the new (transformed) scale value, we simply subtract each Original Value from 6 (i.e., 5 + 1).
Scale Totals
Once you've transformed any individual scale items you will often want to add or average across individual items to get a total score for the scale.
Categories
For many variables you will want to collapse them into categories. For instance, you may want to collapse income estimates (in dollar amounts) into income ranges.
