Which of these refers to the systematic collection of data related to past occurrences brainly

Grounded theory, ethnographic, narrative research, historical, case studies, and phenomenology are several types of qualitative research designs.  The proceeding paragraphs give a brief over view several of these qualitative methods.

Grounded theory is a systematic procedure of data analysis, typically associated with qualitative research, that allows researchers to develop a theory that explains a specific phenomenon.  Grounded theory was developed by Glaser and Strauss and is used to conceptualize phenomenon using research; grounded theory is not seen as a descriptive method and originates from sociology.  The unit of analysis in grounded theory is a specific phenomenon or incident, not individual behaviors.   The primary data collection method is through interviews of approximately 20 – 30 participants or until data achieves saturation.

Which of these refers to the systematic collection of data related to past occurrences brainly

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Ethnographic studies are qualitative procedures utilized to describe, analyze and interpret a culture’s characteristics.  Ethnography was developed in the 19thand 20th centuries and used by anthropologists to explore primitive cultures different from their own; it originated from Anthropology.  Ethnography is used when a researcher wants to study a group of people to gain a larger understanding of their lives or specific aspects of their lives.  The primary data collection method is through observation over an extended period of time.  It would also be appropriate to interview others who have studied the same cultures.

Phenomenology is used to identify phenomena and focus on subjective experiences and understanding the structure of those lived experiences.  It was founded in the early 20th century by Edmund Husserl and Martin Heideggar and originated from philosophy.  Phenomenology is used to describe, in depth, the common characteristics of the phenomena that has occurred.   The primary data collection method is through in-depth interviews.

Case studies are believed to have originated in 1829 by Frederic Le Play.  Case studies are rooted in several disciplines, including science, education, medicine, and law.  Case studies are to be used when (1) the researcher wants to focus on how and why, (2) the behavior is to be observed, not manipulated, (3) to further understand a given phenomenon, and (4) if the boundaries between the context and phenomena are not clear.  Multiple methods can be used to gather data, including interviews, observation, and historical documentation.


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. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.

The importance of ensuring accurate and appropriate data collectionRegardless of the field of study or preference for defining data (quantitative, qualitative), accurate data collection is essential to maintaining the integrity of research. 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.

Consequences from improperly collected data include

  • inability to answer research questions accurately
  • inability to repeat and validate the study
  • distorted findings resulting in wasted resources
  • misleading other researchers to pursue fruitless avenues of investigation
  • compromising decisions for public policy
  • causing harm to human participants and animal subjects

While the degree of impact from faulty data collection may vary by discipline and the nature of investigation, there is the potential to cause disproportionate harm when these research results are used to support public policy recommendations.

Issues related to maintaining integrity of data collection:

The primary rationale for preserving data integrity is to support the detection of errors in the data collection process, whether they are made intentionally (deliberate falsifications) or not (systematic or random errors).

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):

  1. Quality assurance - activities that take place before data collection begins
  2. 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. This proactive measure is best demonstrated by the standardization of protocol developed in a comprehensive and detailed procedures manual for data collection. Poorly written manuals increase the risk of failing to identify problems and errors early in the research endeavor. These failures may be demonstrated in a number of ways:

  • Uncertainty about the timing, methods, and identify of person(s) responsible for reviewing data
  • Partial listing of items to be collected
  • Vague description of data collection instruments to be used in lieu of rigorous step-by-step instructions on administering tests
  • Failure to identify specific content and strategies for training or retraining staff members responsible for data collection
  • Obscure instructions for using, making adjustments to, and calibrating data collection equipment (if appropriate)
  • No identified mechanism to document changes in procedures that may evolve over the course of the investigation .

An important component of quality assurance is developing a rigorous and detailed recruitment and training plan. Implicit in training is the need to effectively communicate the value of accurate data collection to trainees (Knatterud, Rockhold, George, Barton, Davis, Fairweather, Honohan, Mowery, O'Neill, 1998). The training aspect is particularly important to address the potential problem of staff who may unintentionally deviate from the original protocol. This phenomenon, known as ‘drift’, should be corrected with additional training, a provision that should be specified in the procedures manual.

Given the range of qualitative research strategies (non-participant/ participant observation, interview, archival, field study, ethnography, content analysis, oral history, biography, unobtrusive research) it is difficult to make generalized statements about how one should establish a research protocol in order to facilitate quality assurance. Certainly, researchers conducting non-participant/participant observation may have only the broadest research questions to guide the initial research efforts. Since the researcher is the main measurement device in a study, many times there are little or no other data collecting instruments. Indeed, instruments may need to be developed on the spot to accommodate unanticipated findings.

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.

Detection or monitoring can take the form of direct staff observation during site visits, conference calls, or regular and frequent reviews of data reports to identify inconsistencies, extreme values or invalid codes. While site visits may not be appropriate for all disciplines, failure to regularly audit records, whether quantitative or quantitative, will make it difficult for investigators to verify that data collection is proceeding according to procedures established in the manual. In addition, if the structure of communication is not clearly delineated in the procedures manual, transmission of any change in procedures to staff members can be compromised

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)

Examples of data collection problems that require prompt action include:

  • errors in individual data items
  • systematic errors
  • violation of protocol
  • problems with individual staff or site performance
  • fraud or scientific misconduct

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 adult as well as the social conditions that increase the likelihood and frequency of these risky behaviors.

To verify data quality, respondents might be queried about the same information but asked at different points of the survey and in a number of different ways. Measures of ‘ Social Desirability’ might also be used to get a measure of the honesty of responses. There are two points that need to be raised here, 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.

Each field of study has its preferred set of data collection instruments. The hallmark of laboratory sciences is the meticulous documentation of the lab notebook while social sciences such as sociology and cultural anthropology may prefer the use of detailed field notes. Regardless of the discipline, comprehensive documentation of the collection process before, during and after the activity is essential to preserving data integrity.

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.

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

What is Historical Research? The systematic collection and evaluation of data to describe, explain, and understand actions or events that occurred sometime in the past. There is no manipulation or control of variables as in experimental research.
Historical research describes past events, problems, issues and facts.
Historical Review Historical literature reviews focus on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline.

Which of the following types of qualitative research is designed to focus on the commonality of a lived experience?

Phenomenology is used to identify phenomena and focus on subjective experiences and understanding the structure of those lived experiences.