Inter-Rater Reliability In Qualitative Research – 5 Top Things To Know

Have you ever participated in any writing or singing competition? Or, do you have experience exhibiting your talent in front of a panel? If yes, you might wonder on what basis different member of a panel decides a winner from all candidates. No doubt, all members of a jury have different views and remarks about the participants. To solve disagreement issues between different examiners and raters, there is a credible statistical procedure named ‘inter-rater reliability. This method is a standard way to solve any agreement-related issue to make a valid decision. If you want to know more about this method, you must read this article till the end. This article is all about describing the use of inter-rater reliability in qualitative research.

Inter-Rater Reliability: A Brief Introduction

Inter-rater reliability (IRR) simply refers to the extent to which two or more raters or observers agree. Other names for this method include the inter-coder reliability (ICR) and inter-observer reliability (IOR) methods. It refers to solving issues of consistency of the rating system’s implementations. We can use a number of different statistical methods to measure the degree of agreement between different entities. Examples of such statistical methods include interclass correlation coefficient, product-moment correlation, percentage agreement, and kappa. The higher the value of inter-rater reliability, the greater will be the chances of agreement between the selected examiners and vice versa. All in all, in statistics, this type of reliability is a method to measure the level of agreement between the responses and judgment of all members of a jury.

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Inter-Rater Reliability And Qualitative Research:

Qualitative research is the study design that helps researchers in solving a particular research problem by taking into account experiences and motivation. In qualitative research, researchers design open-ended questions in the form of surveys, polls, or questionnaires and distribute them to the respondents to record answers. However, case studies, observations, interviews, and focus groups are also methods for conducting qualitative research. Mainly, qualitative research designs are selected when research aims to see setbacks or find flaws in a process that need improvements in the light of an expert’s advice.

As described earlier, inter-rater or inter-coder reliability helps find agreement between the rating, opinions, and responses of different observers. Thus, the qualitative research goals can be easily achieved by using inter-coder reliability (ICR) as a statistical method.

For applying inter-rater reliability as a statistical method to analyse the qualitative data, a researcher must know about its different models. The following is a brief description of the different models, scope and application of ICR:

Percentage Agreement For Two Raters:

It is simply the average percentage of agreement between observers or respondents in the qualitative research. Percentage agreement gives a clear-cut evaluation by suggesting binary outcomes. It simply means percentage agreement does not highlight shades of grey; it only states whether observers are agreed or not. To calculate it, you need to find the sum of all number of agreements and divide it by the total number of possible agreements.

Kappa Statistics:

Like percentage agreement, kappa statistics also measure absolute agreement between responses by treating all disagreements equally. However, it is not restricted to giving only binary ratings; rather, it also works well for ordinal or categorical ratings. Moreover, kappa statistics itself available in two forms: Cohen’s kappa and Fleiss’s kappa. The first compares two raters, and the latter expands Cohen’s kappa to take into consideration more than two raters. Additionally, for testing the kappa statistics, hypothesis testing by formulating null and alternative hypotheses is commonly used.

Interclass Correlation Coefficient:

Interclass correlation coefficient (ICC) is another method of finding ICR. It works by measuring the proportion of variance of observation by testing between-subject variability in the true scores. The range for ICC can be ranged between 0.0 and 1.0, and its value will be high when variations between scores are less. Typically, Pearson’s and Spearman’s correlational coefficients are helpful in finding this variation.

Current Practices Regarding ICR:

Approximately a few decades ago, very little information regarding ICR was known to society. It is a relatively new statistical method that became popular in 2002 after content analysis literature was published. People actually started using it in qualitative studies back in 2016 when Wu et al. published a articles in some renowned journals using the inter-rater reliability method for authoring and reviewing qualitative studies. However, there are still many dissensuses regarding the most effective model of conducting ICR in a qualitative study.

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Many Statistical Tools Use The ICR Method For Statistical Assessment:

Do you know ICR suggests an integrated working principle for many statistical analysis tools? Yes, NVivo, QDA Miner, and Dedoose contain an integrated inter-observer reliability calculating tool. However, SPSS and Minitab do not have such integrated tools for calculation.   The integration of ICR in qualitative data analysis tools is enough to give you confidence about the use of ICR even manually in your qualitative research designs.

The points mentioned above gave enough information about popular inter-rater reliability models, current practices, and its uses. This information is necessary to make a final decision that whether ICR is the best suitable method for your research. However, once you select this method, you must need proper guidance from the step-by-step guide. In case you are planning to use this method for completing a dissertation, you can seek guidance from an advisor at the university and buy dissertation online from a reputed dissertation writing service.

Final Thoughts:

In a nutshell, collecting scores from multiple judges or responses from multiple research participants is not difficult. Rather, making a decision based on the agreement between them is the task that puts many researchers into trouble. Thus, the article described five important points about inter-rater reliability that will help a qualitative researcher to extract useful inferences. The models described above will help you find the method for ICR that seems convenient for you, and the current practices as well as their application in statistical tools will help you in trusting the method.