Data collection best instruments in research
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 collection
Regardless 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
Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.
To collect data, we must first identify what information we need and how we will collect it. We can also evaluate a hypothesis based on collected data. In most cases, the primary and most important step for research. The approach to data collection is different for different fields of study, depending on the required information.
What is a Research Instrument?
A Research Instrument is a tool used to collect, measure, and analyze data related to your research interests. These tools are most commonly used in health sciences, social sciences, and education to assess patients, clients, students, teachers, staff, etc. A research instrument can include interviews, tests, surveys, or checklists. The Research Instrument is usually determined by researcher and is tied to the study methodology. This document offers some examples of research instruments and study methods.
Choosing a Research Instrument
1. Select a topic
2. Formulate a thesis statement
3. Choose the types of analyses
4. Research and write a literature review
5. Formulate the research questions
6. Conceptualize a topic, refine thesis
7. Choose research method and research
8. Operationalize concepts construct
9. Formulate the data collection strategy
10. Perform a pilot study
11. Collect data
12. Prepare the data for processing &
13. Process & analyze data
14. Interpret & make inferences about data
15. Write the research paper
16. Publish data
Characteristics of a Good Research Instrument
Valid and reliable
● Based on a conceptual framework, or the researcher’s understanding of how the particular variables in the study connect with each other
●Must gather data suitable for and relevant to the research topic
● Able to test hypothesis and/or answer proposed research questions under investigation
● Free of bias and appropriate for the context, culture, and diversity of the study site
● Contains clear and definite instructions to use the instrument
Types of Research Instruments: Interviews
- Interviews or the interaction where verbal questions are posed by an interviewer to elicit verbal responses from an interviewee.
- Structured Interview: A formal set of questions posed to each interviewee and recorded using a standardized procedure.
- Unstructured Interview: A less formal set of questions; the interviewer modifies the sequence and wording of questions.
- Non-Directive Interview: An unguided interview, including open-ended questions and use of spontaneous engagement.
- Focus Interview: An emphasis on the interviewees subjective and personal responses where the interviewer engages to elicit more information.
- Focus Group Interview: A group of selected participants are asked about their opinion or perceptions concerning a particular topic.
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):
- Quality assurance – activities that take place before data collection begins
- Quality control – activities that take place during and after data collection
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.
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.