How to Work On Trustworthiness of Data in Thesis Writing?

Trustworthiness of Data

Thesis writing is a complex matter, and the trustworthiness of data is an essential part of it. The data provides the basis pertaining to both findings, and results of a study. Therefore, it is important for ensuring the trustworthiness of data. Trustworthiness of data is an important part of research. There are different aspects regarding trustworthiness of data. Quantitative and qualitative studies are different regarding the trustworthiness of data. For quantitative studies, trustworthiness of data can be measured through validity and reliability. This concept is obscure for qualitative research studies. In qualitative studies, there are no established metrics like validity and reliability. In qualitative studies, researchers ensure that findings are credible transferable and confirmable. This article by a top PhD dissertation writing service briefly discusses the trustworthiness of data for both quantitative and qualitative studies. Quantitative studies relate to trustworthiness which indicates validity and reliability. In qualitative studies, trustworthiness indicates transferability, credibility, confirmability, and dependability.  

Quantitative Studies

Validity

Validity refers to the extent to which a concept is measured accurately in a quantitative study. Let’s take the example of a survey focused on measuring depression in a population. Now the questionnaire should contain questions that can gauge depression. However, if the questions are related to anxiety, it is not a valid survey. The survey is designed to measure depression, not anxiety. As it is not able to measure depression, it is not a valid survey.

There are three major types of validity. The first type is content validity. This category of validity ensures that the instrument used in the study covers all the content. This content should also be relevant to the variable. In simple words, an instrument is valid in terms of content if it covers entire domain of the variable. Face validity is a subset of content validity. Under this category, experts share their opinion about the instrument. If experts think that the instrument measures what it is intended to, it is called face validity.

The second type of validity is construct validity. Construct validity suggests if one can draw inferences about the test scores related to a subject matter. Let’s discuss the example of a depression survey. Let’s assume a test score which suggests that a person is depressed. The construct validity will tell if that person actually is depressed. The final type of validity is criterion validity. Criterion validity indicates if another instrument can measure the same variable. It implies that different instruments can measure a single variable. There are three forms of criterion validity. These include convergent, divergent, and predictive validity.  

Reliability

Reliability indicates the consistency of a measure. Let’s assume that an instrument measures motivation. Reliability implies that a participant’s responses should be similar. And this applies for every time he completes the questionnaire. Homogeneity, stability, and equivalence are three attributes of reliability. Homogeneity indicates internal consistency, and can be measured through item-to-total correlation. Test-retest reliability measures the stability implying similar results with more than one attempt. Equivalence implicates a level of agreement between two or more observers. Inter-rater reliability assesses equivalence.

Validity and reliability are two measures pertaining to trustworthiness of data in quantitative studies. Depending on nature of the study, different types of validity and reliability should be measured. A dispersion from validity and reliability measures indicates that the research process needs improvement.

Qualitative Studies

Credibility

In qualitative studies, researchers assess the findings. Credibility indicates how confident a researcher is in truth of the findings. In simple words, credibility questions how sure you are in terms of knowing if the findings are true, and accurate. Researchers can indicate the credibility of findings through triangulation. Triangulation is the process of understanding a phenomenon. In triangulation, different methods are used for developing an understanding of the phenomenon.

Transferability

Transferability is an attribute of the trustworthiness about qualitative data. This is the data that targets the application of findings. Transferability indicates if a researcher finds the results of a study applicable within other contexts. Other contexts can mean a number of things. It can mean a similar situation, or population phenomenon etc. Researchers can use the thick description technique to indicate if the findings are applicable in other contexts. Thick description is a technique that provides detailed descriptions as observed by the researcher. A researcher can use detailed interpretations of situations observed as well. The thick description technique is widely used for ensuring the trustworthiness of data in qualitative studies.

Confirmability

The degree of neutrality in findings of the study is known as confirmability. This can be done by searching in Google. Confirmability ensures that findings are based on responses of the participants. Aside from that, confirmability suggests that findings are not based researcher’s personal motivation. Confirmability ensures that researcher’s opinion does not skew the interpretation of participants’ responses. Sometimes researcher has a particular viewpoint about a certain aspect. While developing a narrative in the findings, that bias can lead the interpretations in a particular direction. Researchers usually establish confirmability by providing an audit trail. This audit trail highlights all the steps of data analysis. In this way, a rationale for the decision-making process is provided. By using audit trail technique, researchers can achieve confirmability regarding qualitative data.

Dependability

Dependability suggests that the study can be repeated by the researchers’ consistent finding. In other words, dependability means that if another researcher wants to replicate your work, he can do that. It has enough information available. The findings of a study should have enough information so that another person can draw similar findings. To ensure dependability, a researcher can use an Inquiry audit. Inquiry audit is a process where another person reviews your findings, and examines the research process. This person reviews the research process and data analysis for consistent findings. In qualitative research, dependability is important. This ensures that the finding of the study can be generalised. Hence as per the discussion highlighted above, it can be said that four aspects of credibility, transferability, confirmability, and dependability, are essential for qualitative studies. And in qualitative research studies, the trustworthiness of data implies achievement regarding these four aspects. A researcher can use these aspects for ensuring the trustworthiness of data, which can lead to better, and more effective result generation aspects.