Explanatory research

Explanatory research goes beyond exploratory or descriptive research by trying to find the reasons why certain relationships occur. It seeks to provide explanations for what has been observed. Explanations are based on interpretation of findings in terms of broader concepts and accepted theory.

Example. Using extensive interviews and observation, Dei (1992) developed an explanation for how Ghanian villagers survived prolonged drought and related hardships. He found they got through hard times by shifting from farm production for a market economy to shared production with other villagers for household consumption. A strong sense of community solidarity developed and allowed the villages to survive with what they could produce together. The explanation for their survival was the successful return to earlier farming methods.

Regardless of the purpose of your research, you will also need to decide on a technique for data collection. There are two contrasting yet complimentary techniques - quantitative and qualitative means of collecting data.

Quantitative and qualitative data

Many fields of science, particularly the physical sciences, report observations in terms of numbers. Observations reported in numbers are referred to as quantitative data. Social scientists obtain and analyze quantitative data, as you have seen with various studies we have cited. Social scientists also obtain and analyze data in qualitative form. These are observations recorded in words - descriptions of what persons said or did, how they interacted with one another, or what a researcher observed by watching their behavior. Each technique is useful; each also has certain strengths and weaknesses; each also presents a different set of design decisions.

Some researchers draw a sharp distinction between quantitative and qualitative research. A different view is presented in Types of Data. This discussion shows how quantitative data is based in part on qualitative judgments and that qualitative data can also be described and analyzed numerically.

Quantitative research

A quantitative approach requires a well-developed research design. Quantitative research is usually based on:

• Careful and precise specification of the question to be answered;
• Identification, definition, and measurement of the key variables;
• Selection and specification of one or more methods of collecting data;
• Development of a sampling plan; and
• Numerical analysis of the data, including use of appropriate statistical tests.

Numbers used in quantitative studies may be as simple as counting the number of "Yes" versus "No" responses to some question. More complex forms of measurement, however, are generally used. The descriptive research by Fattah (1981), for example, used a quantitative design. The many decision-making variables were defined and measures were developed for each. These measures, in the form of questions, were then combined in a questionnaire that was used in interviewing a random sample of farm families.

Quantitative data has important strengths. Using numerical measures provides more precise descriptions of variables. In addition, quantitative research permits use of larger samples. As you will see in Chapter 19, "Performing Inferential Statistical Analyses," larger samples provide a stronger basis for generalizations. Also, quantitative data can be combined and analyzed using various statistical techniques. You are certainly familiar with averages and percentages as ways of summarizing data. Later, in Part 4, you will learn about additional ways of analyzing quantitative data.

Most of this site focuses on planning and conducting quantitative research and analyzing data based on quantitative variables.