Data collection methods

entropy collection is the process of gathering and measuring culture on variables of interest, in an established systematic hammer that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. entropy Collection Techniques include the following Personal Interviews Conducting personal interviews is probably the best rule of entropy collection to gain first hand information. It is however, unsuitable in cases where there are many community to be interviewed and questioned.Questionnaires Questionnaires are good methods of data collection when there is a need for a articular class of people to be questioned. The research worker can prepare a questionnaire according to the data he requires and send it to the responders. Detailed observation Data can also almost effectively be obtained with means of observational skills. The researcher can go out a place and take down details of both that he observes which is actually required for aiding in hi s research. Here, the researcher has to generate sure that what he is find 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 base on the group discussion hich 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 affaird in todays research.Unlike the Internet, it is sure that you leave get good and accurate information from books and published guides. Using Experiments Sometimes, for obtaining the full understand of the scenario, researchers have to onduct 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 concord 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 This is data that is collected by the researcher himself. The data is gathered through questionnaires, interviews, observations etcetera Secondary data This is data that is collected, compiled or written by other researchers eg. ooks, 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 D ata collection Data analysis and interpretation Siddiqui, S. A. (2012) Key questionnaire design principles . 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. using up dichotomous (yes I no) and multiple choice questions. . Use open-ended questions cautiously. 6. Avoid using leading-questions. 7. Pretest a questionnaire on a small number of people. 8. Think nearly 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 dvisable to use more than one method when collecting diagnostic data. The data from the different methods can be compared, and if consistent, it is potential the variables are creation validly measured.Statistical inference permits us to draw conclusions about a universe of discour se based on a exemplification. Sampling (i. e. selecting a sub-set of a whole population) is often done for reasons of cost (its little expensive to sampling 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 a nonher. Types of have 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. Quota sample purpose-made 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 m ost 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 communication channel 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 di fficult or costly to develop a complete list of the population members or when the population elements are wide 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. tighten up when sample size larger. Nonsampling errors are more serious and are due oms kes made in the acquisition ot 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 easurements being taken because of defective equipment, mistakes made during transcription from primary sou rces, 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. he 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 nderstanding in the validity of the survey and sources of nonresponse error. The importance of ensuring accurate and appropriate data collection some(prenominal) 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 impartiality of data collection Most, Craddick, Crawford, Redican, Rhodes, Rukenbrod, and Laws (2003) describe qual ity assurance and quality control as two approaches that can preserve data integrity and ensure the scientific validity of take results. Each approach is implemented at different points in the research timeline . Whitney, Lind, Wahl, (1998) prize 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. . , 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 ncorporate 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 occurren ce 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 ach individual observation, and for the unblemished 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 li mits opportunities for notice errors. Quality control also identities the required responses, or actions necessary to correct taulty 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.