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Data Assessment Process

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Data Assessment Process

Data allows researchers and respondents to achieve different research goals depending on the topic under study. For my research proposal, I focused on capital punishment, more specifically, the death sentence. The study aimed to measure three main hypotheses based on cost efficiency; the effects of deterrent measures and socioeconomic factors on murder rates are independent of the death penalty status; most people in the US support the death penalty because they are not knowledgeable of the debatable issues, such as the expenses involved, and informed American public would not support the death penalty. It is important to note that following data collection procedures to the latter results in a successful assessment and analysis.

The first step is to identify issues and opportunities for collecting data. Since my research was a qualitative one, it entailed conducting preliminary assessments of the groups that would participate in focus groups as well as preview previous studies on the death penalty (Peersman, 2014). It was established that identifying appropriate grounds for collecting data makes it an easy task to implement research experiments and collect meaningful information for analysis. Consequently, an ideal scenario would be to review all policies, practices, and procedures applicable to victims of capital punishment and any other appropriate audience.

The second step is to select opportunities and set goals. Once data is collected, it is important to identify major areas of concern, set goals and objectives. On the topic of capital punishment, the researcher focuses on the death penalty, with the main objective being to determine whether the death penalty is a cost-effective measure of reducing crimes in the US. A major point of contention is the loss of life, which is also an ethical dilemma.

The third step is to plan an approach and establish appropriate methods. The research proposal on capital punishment highlights participants to be involved, how data will be collected, sources of data, and the duration of data collection. I chose a flexible project plan consisting of methods and approaches in line with project objectives, which will vary depending on the project’s context, size, opportunities, purpose, complexity, and resources (Sutton, & Austin, 2015). Since this step is vital, the proposal delineates groups of interest and comparison groups, discrimination issues, data collection methods, and scope of data collection.

The fourth step is data collection. A researcher must be aware of practical considerations and best practices for addressing logistical challenges when collecting data. For instance, one should get buy-in from senior-most leadership and key stakeholders, identify who will conduct interviews, identify resources, and ensure confidentiality.

The last step is to analyze and interpret data. Analysis can be done using qualitative or quantitative methods. Qualitative data delineates any information depicted in the form of words and not numerically: sound recordings, photographs, and videos (Alshenqeeti, 2014). The data is collected through observation, focus groups, interviews, and case studies and analyzed using the qualitative method; describes specific context broadly and seeks to explain attitudes, behavior, and thoughts. On the other hand, quantitative data is represented in numerical form and analyzed using quantitative methods such as questionnaires, surveys, and statistical data. Researchers must acknowledge that numbers cannot be interpreted as they are, but one must understand assumptions that underlie them.

Validity and Reliability in Research

Researchers consider validity and reliability with each new study they design, for this case, an experimental design. This is because validity and reliability are not fixed but rather reflect a particular study’s unique variables, research design, instruments, and participants. In the context of research design, two types of validity, which speak to the quality of different research process features, are considered: internal validity and external validity (Taherdoost, 2016). A research study’s findings are internally valid if the researcher has used controls to determine that the outcome is indeed due to manipulation of the independent variable or the treatment. Consequently, external validity refers to the extent to which the findings can be generalized from the sample to the population or to other settings and groups. On the other hand, reliability refers to the replicability of the findings; the measure of stable and consistent research results (Taherdoost, 2016). A researcher can use test-retest reliability, parallel form reliability, inter-rater reliability, and internal consistency reliability to ascertain trends in analysis results.

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Data assessment process
Data Assessment Process

In my research proposal, I achieve data validity and reliability by using a systematic and transparent data collection approach to collect information from primary and secondary sources. Data collected from previewing previous studies and focus groups are documented in order to maintain a clear flow of processing and auditing. Moreover, my interactions with respondents are based on well-established research proceedings, and a focus on selected groups will help determine research consensus on research representation.

Data Processing

The final stage in research entails transforming data into a common format that is interpretable and understandable. Final data can be used in subsequent analyses or as input in other computational models. For my research project, data is first collected using a qualitative approach, use of focus groups, and interviews. I gathered most data in form filled questionnaires and recordings. This information should therefore be represented in tables and charts after that, provide analysis through discussion. This allows readers to understand the basis of research, the results and make decisions based on the findings.


Alshenqeeti, H. (2014). Interviewing as a data collection method: A critical review. English linguistics research3(1), 39-45.

Peersman, G. (2014). Overview: Data collection and analysis methods in impact evaluation. UNICEF Office of Research-Innocenti.

Sutton, J., & Austin, Z. (2015). Qualitative research: Data collection, analysis, and management. The Canadian journal of hospital pharmacy68(3), 226.

Taherdoost, H. (2016). Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. How to test the validation of a questionnaire/survey in a research (August 10, 2016).

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