Data are the raw materials for research. Investigators generate data through some processes of measurement, counting, or observation. The tools and techniques to be employed to collect data depend largely on the objectives of the study.
Whenever we plan for data collection, we must ask ourselves the following questions:
- What information do we want to collect to answer our research questions implied in our objectives of the research? This refers to the selection of the variable;
- What approach will we follow to collect this information? This refers to the choice of the study design;
- What techniques and tools will we use to collect data? This refers to the data collection technique;
- Where we want to collect data? This refers to the population to be sampled;
- What part of the population will be included in our investigation? This refers to the
- How many subjects will we include in our study? This refers to the sample size;
- How will we select our sample? This refers to the sample design.
This chapter is designed to concentrate on the third point, viz. the data collection techniques.
The collection of data may range from a simple observation at one location to a grandiose survey covering a wide area in any part of the universe. The method selected for data collection will largely determine how the data are collected.
Questionnaires, standardized tests, observational forms, tape records, or checklists are some of the devices used to record the data.
Data collection techniques enable us to systematically collect information about our objects of study (people, objects, phenomena) and about the setting in which they occur.
In the collection of data, we have to be systematic. If data are collected haphazardly, it will be difficult to answer our research questions conclusively.
The same measuring instrument, the same operational definition of variables, the same unit of measurement, etc., must be maintained at all stages of data collection.
There are many different ways to collect data. The approach selected largely depends on
- The study objectives
- The study design
- The availability of time, money and personnel
An important consideration in deciding on the best way to collect data is whether the study is intended to
- Produce relatively precise quantitative findings, or
- Produce qualitative, descriptive, or narrative information.
Keeping these two points in view, the data collection techniques in this text will be addressed under two broad headings;
- Quantitative Data Collection Technique,
- Qualitative Data Collection Technique.
Quantitative Data Collection Technique
Most social science researches are based on 3 survey methods of quantitative data collection technique.
- Personal interview method;
- Self-administered questionnaires method, and
- Telephone interviewing method.
Qualitative Data Collection Technique
The data collection techniques most appropriate for studies, whose objectives call for descriptive qualitative analysis, tend to be different from those most appropriate for quantitative methods.
Quantitative methods are important to obtain data for making predictions, probabilistic statements, and generalizations.
Methods of qualitative data collection techniques are;
Some additional data collection techniques are;
- Nominal group technique.
- Delphi technique.
- Life histories.
- Case studies.
- Rapid appraisal techniques or soundings.
- Panel study.
- Key informant approach.
A special application of the interview technique is the use of life histories. This technique allows people to tell stories, which provide insight into what they consider important in their views.
The life history technique is a special technique of interviewing, which employs a very limited sample, not exceeding 25. This technique fits well in traditional rural societies.
Issues that are especially suited for investigation using the life-history approach include, for example, the pattern of reproduction and women’s feelings about marriage, childbirth, and contraception.
Some anthropologists have used school children’s essays to explore the (hidden) values and aspirations of their subjects and the community they live in.
Essays may be analyzed to determine differences in beliefs concerning perceived causes of illness, the value of children in traditional societies, the benefits of having small family, the rationale for health-related behavior, and the like.
A case study involves detailed investigations of a few people, a family, a community, or a particular situation. It falls under a qualitative study.
According to Young, a case study is a method of exploring and analyzing the life of a social unit, be it a person, a family, an institution, a cultural group, or even an entire community.
Usually, several methods of data collecting information are used simultaneously. The subjects of study are often chosen using non-probability sampling.
For example, the cases may be selected in such a way that they are typical or illustrative of a particular phenomenon or group. A researcher uses case studies for the following reasons:
- To explore new areas and issues where little theory is available or measurement is unclear.
- To explain a complex phenomenon.
It is usual for researchers to combine case studies with quantitative analyses that use a larger data set.
Mapping is a valuable technique for visually displaying relationships and resources. It is also useful and often indispensable as a pre-stage to sampling.
In installing CNG stations, for example, the mapping may be viewed as an invaluable guide.
All national family planning organizations generate service statistics. Some organizations have established a management information system (MIS).
The quality of service statistics, however, varies from country to country and even within countries. Thus, they should be used with caution. Service statistics often help a researcher define the parameters of the problem he or she wants to study.
In some cases, they can be used to compare the results of a particular study with nationwide figures.
Often in operations research projects, it is necessary to design supplementary forms to provide data that are not available from the regular service statistics.
Surveys are generally conducted at one point in time for obvious reasons.
However, there are several disadvantages to collecting data at only a single point in time.
Chance fluctuations may well distort the data. Also, a cross-sectional survey, which is conducted at a single point in time, offers no way to study trends in the data or seasonal variations, or no way to tell whether a relationship found between the two or more variables will remain the same or will change with time.
If the sample is not too large, a longitudinal study is a neat solution to the problem.
A common longitudinal survey is the panel study, in which the same respondents are re-interviewed at two or more points in time regarding the same problems.
Panel study is one of the best ways to measure changes among the same subjects over time. In such a study, we first sample a group at one point in time and then return at a later time to ask the same questions again.
Then by bringing together the responses of subjects, we see whether a characteristic or attitude continues or whether it is taken up and dropped over time.
The disadvantages of a panel study, among others, are as follows:
- Since the interview is conducted several times, the cost involved in it is much greater than a cross-sectional study.
- The respondents may be reluctant to participate in repeated interviews.
- Some respondents may not be available for successive interviews.
Another longitudinal survey is the trend study, in which the same number of respondents from the same population is surveyed each time, but not necessarily the same respondents.
In this design, similar data collected in different years (and on different subjects) are compared.
The data from this year’s contraceptive usage could be set against data from the last 5, 10, or more years. In this way, the comparison could be made across time.
The disadvantage of this design is that difference in the data from one survey to the next may not be the result of a trend but merely the reflection of differences in the persons surveyed.
Key-informant Approach of Data Collection
The key-informant Interview (KII) approach to needs assessment is one that assumes that certain individuals are knowledgeable of the community and target population.
It further assumes that these individuals are in a position to accurately articulate the community needs to assist in program planning. Key-informants are often interviewed utilizing a semi-structured format.
The person or persons selected to be key-informants must have a broad knowledge of the community, its social structure, services to be provided, and its people.
It is an excellent way of recovering information about past events or ways of life that are no longer observable.
The objectives of the needs assessment can help to identify the most appropriate kind of person to act as a key-informant.
The researcher might consider public officials, long-time residents, business managers, administrators, church leaders, and persons representing a variety of lifestyles, ages, viewpoints, or ethnic backgrounds.
Few people in a community will be able to speak about everything; therefore, the problem should be in focus before the informant is selected.
A variety of methods can be derived from working with key-informant. Questions can be developed in advance; it may be outlined or may be unstructured.
The key-informant method is especially useful for
- Obtaining a deeper knowledge of minority viewpoints
- Raising citizen’s consciousness about a community problem.
- Involving citizens in public problem-solving who would be less inclined to answer a questionnaire, etc.
- Data Quality Check
There are several ways to check the quality of the interview data.
- Sometimes a researcher will deliberately ask two or more questions that give the same type of information. The first question might be asked at the beginning of the interview and the second in the end. The two questions are then examined for consistency of response. This is one way to check the reliability of the data.
- For difficult questions, sensitive questions, or questions, where the researchers want to be sure the information is correct, the interviewers can be instructed to probe. That is to say; the interviewer can repeat the question in a slightly different form or repeat the respondent’s answer and then ask if the information is correct. For example, a woman might report that she has two sons and three daughters. The interviewer might then say, ‘You have a total of five children, two males, and three females. Is that correct? Are there any other children whom you may have forgotten to tell me about?”
- Field supervisors should be used to help the interviewers with difficult situations and to check that they are doing their work. Some studies use a ratio of one supervisor to every five interviewers.
- Most studies that use an interview procedure attempt to reinterview a certain percentage of the respondents. Depending upon the size of the sample, a general rule is to re-interview between 5 and 10 percent of the sample. The data from the first interview are then checked against the data from the second interview for consistency. If there are major inconsistencies, particularly on such questions like age, marital status, and parity, then clearly there is a problem somewhere. The problem might be with the questionnaire, with the interviewers, with the tabulation procedures, or in some other areas.
- Once the data have been collected and tabulated, it is possible to do statistical checks for errors or consistency. For example, a frequency distribution of the parity of women may reveal that several women claim to have 18 or 19 living children. Since this is highly unlikely, the investigator is faced with the choice of discarding these questionnaires, eliminating the information from the question on parity, or going back and re-interviewing the women who claim to have 18 or 19 children.
The following table summarizes the advantages and disadvantages of various data collection techniques:
|(a) Observation (in particular of behavior)|
|(c) Small-scale, flexible interview|
|(d) Large -scale fixed interview|
|(e) Written or mailed questionnaire (selfadministered)|
|(f) Content analysis|
The distinction between data collection techniques and data collection tools:
|Data collection techniques||Data collection tools|
|(1) Content analysis||(1) Checklist, data compilation form|
|(2) Observation||(2) Eyes and other senses, pen, and paper watch scales, microscope, etc.|
|(3) Interview||(3) The interview schedule, checklist, questionnaire, tape recorder|
|(4) Administering written questionnaires||(4) Questionnaire|