Data collection methods

entropy aggregation is the physical process of gathering and measuring reading on variables of interest, in an established systematic fashion that enables one to act stated research questions, test hypotheses, and evaluate revealcomes. Data Collection Techniques intromit the pastime Personal Interviews Conducting personal interviews is probably the best mode of selective entropy assemblage to gain first hand information. It is however, unsuitable in cases where on that point ar m each people to be interviewed and questioned.Questionnaires Questionnaires atomic number 18 good methods of selective information assemblage when there is a need for a articular physical body of people to be questioned. The police detective fag prepargon a questionnaire agree to the information he requires and send it to the responders. Detailed observation Data pile also most effectively be obtained with means of observational skills. The police detective can visit a place and take down details of all that he observes which is actually required for aiding in his research. Here, the tec has to mystify sure that what he is observing is real.Group Discussions Group banters ar good techniques where the researcher has to know what the people in a host think. He can come to a conclusion based on the group discussion hich may even involve good debate topics of research. Internet Data The Internet is an ocean of selective information, where you can line a substantial aggregate 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 handed-down ones that are still employ in todays 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 misgiving 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 understanding of the subject related to the research. at that place are many some 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 particular data and Secondary data. Primary 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, conglomerated or written by other researchers eg. ooks, journals, newspapers internet etc. The following steps are spendd to collect data Review compile secondary source information Plan design data collection instruments To gather primary information Data collection Data abbreviation and interpretation Siddiqui, S. A. (2012) Key questionnaire design principles . Keep the questionnaire as short as possible. 2. Ask short, artless, and understandably worded questions. 3. Start with demographic questions to help respondents get started comfortably. 4. drill dichotomous (yes I no) and multiple choice questions. . Use open-ended questions cautiously. 6. negate using leading-questions. 7. Pretest a questionnaire on a small number of people. 8. intend 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 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 possible the variables are being validly measured.Statistical inference permits us to draw conclusions about a nation based on a en ideal. Sampling (i. e. selecting a sub-set of a livelong universe) is often done for reasons of cost (its less expensive to savor 1,000 idiot box viewers than 100 million TV viewers) and practicality (e. g. performing a take apart test on every automobile produced is impractical). The judged population and the target population should be similar to one a nonher. Types of take strategies Probability Why is it used? To generalize to population.Some examples Simple ergodic specimen Stratified sample clod 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 meth ods of sampling are Simple ergodic Sampling Stratified Random Sampling glob 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 of it is equally likely to be chosen. Drawing lead names from a hat containing all the names of the students in the partitioning is an example of a simple random sample any group of three names is as equally likely as select any other group of three names. A stratified random sample is obtained by separating the population into mutually exclusive sets, or strata, and past drawing simple random samples from each stratum.Strata 1 Gender manful Female Strata 2 Age 20 20-30 31-40 41-50 51-60 60 Strata 3 assembly line professional clerical blue collar other We can wonder about the total population, make inferences within a stratum or make comparisons across strata Cluster Sampling A cluster sample is a simple random s ample 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 ontogenesis sampling error out-of-pocket 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 oms kes made in the eruditeness 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 incorre ct responses, due to incorrect easurements being taken because of improper 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. he sample observations that are collected may not be representative of the target population. The Response Rate (i. e. the relation of all people selected who complete the survey) is a key survey literary argument and helps in the nderstanding in the validity of the survey and sources of nonresponse error. The importance of ensuring accurate and steal 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 t o maintaining integrity of data collection Most, Craddick, Crawford, Redican, Rhodes, Rukenbrod, and Laws (2003) cite quality trust and quality deem as two memory accesses 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) feeling assurance activities that take place before data collection begins Quality control activities that take place during and after(prenominal) data collection Quality arrogance 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 military man subjects, researchers are taught to ncorporate one or more secondary measures that can be used to verify the quality of information bei ng collected from the homosexual subject. For example, a researcher conducting a survey might be elicit in gaining a better insight into the occurrence of risky behaviors among girlish adults as well as the social conditions that increase the likelihood and relative 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 entire data set. Quality control While quality control activities (detection/ observe 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 required pre-condition for establishing monitoring systems. There should not be any uncertainty about the flow of information between principal inves tigators 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 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 mistily written and the necessary steps to minimize recurrence are not implemented through feedback and education.