data collection methods

Data Collection Methods.
Introduction
Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.
Data Collection Techniques include the following:
Personal Interviews
Conducting personal interviews is probably the best method of data collection to gain first hand information. It is however, unsuitable in cases where there are many people to be interviewed and questioned.
Questionnaires
Questionnaires are good methods of data collection when there is a need for a particular class of people to be questioned. The researcher can prepare a questionnaire according to the data he requires and send it to the responders.
Detailed observation
Data can also most effectively be obtained with means of observational skills. The researcher can visit a place and take down details of all that he observes which is actually required for aiding in his research. Here, the researcher has to make sure that what he is observing is real.
Group Discussions
Group discussions are good techniques where the researcher has to know what the people in a group think. He can come to a conclusion based on the group discussion which may even involve good debate topics of research.
Internet Data
The Internet is an ocean of data, where you can get a substantial amount of information for research. However, researchers need to remember that they should depend on reliable sources on the web for accurate information.
Books and Guides
These data collection techniques are the most traditional ones that are still used in today 's research. Unlike the Internet, it is sure that you will get good and accurate information from books and published guides.
Using Experiments
Sometimes, for obtaining the full understanding of the scenario, researchers have to conduct actual experiments on the field. Research experiments are usually carried out in fields such as science and manufacturing. This is the best method for gaining an in-depth understanding of the subject related to the research.

There are many other methods of data collection which may help the researcher to draw statistical as well as conceptual conclusions. For obtaining accurate and dependable data, researchers are suggested to combine two or more of the above mentioned data collection techniques. http://www.buzzle.com/articles/data-collection-techniques.html Types of Data
Data types are categorized into two types: Primary data and Secondary data.
Primary data
This is data that is collected by the researcher himself. The data is gathered through questionnaires, interviews, observations etc.
Secondary data
This is data that is collected, compiled or written by other researchers eg. books, journals, newspapers internet etc.
The following steps are used to collect data
Review & compile secondary source information
Plan & design data collection instruments
To gather primary information
Data collection
Data analysis and interpretation
Siddiqui, S.A. (2012)
Key questionnaire design principles
1. Keep the questionnaire as short as possible.
2. Ask short, simple, and clearly worded questions.
3. Start with demographic questions to help respondents get started comfortably.
4. Use dichotomous (yes|no) and multiple choice questions.
5. Use open-ended questions cautiously.
6. Avoid using leading-questions.
7. Pretest a questionnaire on a small number of people.
8. Think about the way you intend to use the collected data when preparing the questionnaire.
Which data collection method should the researcher use?
Because of the biases inherent in any data-collection method, it is sometimes advisable to use more than one method when collecting diagnostic data. The data from the different methods can be compared, and if consistent, it is likely the variables are being validly measured.
Statistical inference permits us to draw conclusions about a population based on a sample.
Sampling (i.e. selecting a sub-set of a whole population) is often done for reasons of cost (it’s less expensive to sample 1,000 television viewers than 100 million TV viewers) and practicality (e.g. performing a crash test on every automobile produced is impractical).
The sampled population and the target population should be similar to one another.
Types of sampling strategies:
Probability:
Why is it used?
To generalize to population. Some examples:
Simple random sample
Stratified sample
Cluster sample
Systematic sample
Non probability:
When should it be used? Where generalizability not as important. Researcher wants to focus on “right cases.” Some examples:
Quota sample
“Purposeful” sample
“Convenience” or “opportunity” sample
Sampling Plans
A sampling plan is a method or procedure for specifying how a sample will be taken from a population.
Three methods of sampling are:
Simple Random Sampling
Stratified Random Sampling
Cluster Sampling.
Random sampling is often the most common one used.
Simple Random Sampling…
A simple random sample is a sample selected in such a way that every possible sample of the same size is equally likely to be chosen.
Drawing three names from a hat containing all the names of the students in the class is an example of a simple random sample: any group of three names is as equally likely as picking any other group of three names.
A stratified random sample is obtained by separating the population into mutually exclusive sets, or strata, and then drawing simple random samples from each stratum.
Strata 1 : Gender :
Male
Female Strata 2 : Age
< 20
20-30
31-40
41-50
51-60
> 60
Strata 3 : Occupation professional clerical blue collar other We can enquire about the total population, make inferences within a stratum or make comparisons across strata
Cluster Sampling
A cluster sample is a simple random sample of groups or clusters of elements (vs. a simple random sample of individual objects).
This method is useful when it is difficult or costly to develop a complete list of the population members or when the population elements are widely dispersed geographically.
Cluster sampling may increase sampling error due to similarities among cluster members.
Sampling and Non-Sampling Errors…
Two major types of error can arise when a sample of observations is taken from a population: sampling error and nonsampling error.
Sampling error refers to differences between the sample and the population that exist only because of the observations that happened to be selected for the sample. Reduce when sample size larger.
Nonsampling errors are more serious and are due to mistakes made in the acquisition of data or due to the sample observations being selected improperly. Most likely caused be poor planning, sloppy work, etc.
Errors in data acquisition…
…arises from the recording of incorrect responses, due to:
— incorrect measurements being taken because of faulty equipment,
— mistakes made during transcription from primary sources,
— inaccurate recording of data due to misinterpretation of terms, or
— inaccurate responses to questions concerning sensitive issues.
Nonresponse Error…
…refers to error (or bias) introduced when responses are not obtained from some members of the sample, i.e. the sample observations that are collected may not be representative of the target population.
The Response Rate (i.e. the proportion of all people selected who complete the survey) is a key survey parameter and helps in the understanding in the validity of the survey and sources of nonresponse error.
The importance of ensuring accurate and appropriate data collection
Both the selection of appropriate data collection instruments (existing, modified, or newly developed) and clearly delineated instructions for their correct use reduce the likelihood of errors occurring.
Issues related to maintaining integrity of data collection:
Most, Craddick, Crawford, Redican, Rhodes, Rukenbrod, and Laws (2003) describe ‘quality assurance’ and ‘quality control’ as two approaches that can preserve data integrity and ensure the scientific validity of study results. Each approach is implemented at different points in the research timeline .Whitney, Lind, Wahl, (1998)
Quality assurance - activities that take place before data collection begins
Quality control - activities that take place during and after data collection
Quality Assurance
Since quality assurance precedes data collection, its main focus is 'prevention ' (i.e., forestalling problems with data collection). Prevention is the most cost-effective activity to ensure the integrity of data collection. In the social/behavioral sciences where primary data collection involves human subjects, researchers are taught to incorporate one or more secondary measures that can be used to verify the quality of information being collected from the human subject. For example, a researcher conducting a survey might be interested in gaining a better insight into the occurrence of risky behaviors among young adults as well as the social conditions that increase the likelihood and frequency of these risky behaviors.

Two main points to note:
1) cross-checks within the data collection process and
2) data quality being as much an observation-level issue as it is a complete data set issue. Thus, data quality should be addressed for each individual measurement, for each individual observation, and for the entire data set.
Quality control
While quality control activities (detection/monitoring and action) occur during and after data collection, the details should be carefully documented in the procedures manual. A clearly defined communication structure is a necessary pre-condition for establishing monitoring systems. There should not be any uncertainty about the flow of information between principal investigators and staff members following the detection of errors in data collection. A poorly developed communication structure encourages lax monitoring and limits opportunities for detecting errors.
Quality control also identifies the required responses, or ‘actions’ necessary to correct faulty data collection practices and also minimize future occurrences. These actions are less likely to occur if data collection procedures are vaguely written and the necessary steps to minimize recurrence are not implemented through feedback and education (Knatterud, et al, 1998)

References
Knatterud.,G.L., Rockhold, F.W., George, S.L., Barton, F.B., Davis, C.E., Fairweather, W.R.,
Honohan, T., Mowery, R, O’Neill, R. (1998). Guidelines for quality assurance in multicenter trials: a position paper. Controlled Clinical Trials, 19:477-493.

Most, .M.M., Craddick, S., Crawford, S., Redican, S., Rhodes, D., Rukenbrod, F., Laws, R.
(2003). Dietary quality assurance processes of the DASH-Sodium controlled diet study. Journal of the American Dietetic Association, 103(10): 1339-1346.

Siddiqui, S.A. (2012). Comprehensive Economics (Statistics for Economics and Indian Economic Development). India: Laxmi Publication Private

Whitney, C.W., Lind, B.K., Wahl, P.W. (1998). Quality assurance and quality control in longitudinal studies. Epidemiologic Reviews, 20(1): 71-80.

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References: Knatterud.,G.L., Rockhold, F.W., George, S.L., Barton, F.B., Davis, C.E., Fairweather, W.R., Honohan, T., Mowery, R, O’Neill, R. (1998). Guidelines for quality assurance in multicenter trials: a position paper. Controlled Clinical Trials, 19:477-493. Most, .M.M., Craddick, S., Crawford, S., Redican, S., Rhodes, D., Rukenbrod, F., Laws, R. (2003). Dietary quality assurance processes of the DASH-Sodium controlled diet study. Journal of the American Dietetic Association, 103(10): 1339-1346. Siddiqui, S.A. (2012). Comprehensive Economics (Statistics for Economics and Indian Economic Development). India: Laxmi Publication Private Whitney, C.W., Lind, B.K., Wahl, P.W. (1998). Quality assurance and quality control in longitudinal studies. Epidemiologic Reviews, 20(1): 71-80. http://www.buzzle.com/articles/data-collection-techniques.htm