Data Saturation and Variability

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Table of Contents

Introduction
Body
Conclusion
Reference

Introduction

From a theoretical perspective, data saturation is a critical technique in determining the sample sizes required in qualitative research. Data saturation is a technique used in standardizing sample sizes especially in health science researches. Apparently, data saturation is critical in determining purposive samples. From the article, it is evident that an effective qualitative project requires several samples that derive the correct results. In this regard, data saturation is used as a guideline for numerical analysis.

Body

According to Guest, Bunce & Johnson (2006), it is necessary to utilize probabilistic sampling methodology in qualitative research. In addition, the process of utilizing probabilistic methodologies, non-probabilistic sample sizes are derived. From this perspective, the article reveals that most of ethnographic studies or qualitative projects are effective when subjected to 36 interviews.

However, the minimum number considered for a qualitative research is 15 interviews. However, the number of interviews ranges depends on whether the sample is homogenous or heterogeneous. Majority of scientific studies on social behaviors use self-reports. In addition, the collection of data using semi-structured and open-ended interviews is critical in identifying information patterns from the respondents. From this perspective, the accuracy of qualitative analysis on data derived from self-reports is significant.

When conducting a study on a qualitative project, sampling the research population is critical. However, this becomes essential if a non-probabilistic and purposive sampling technique is preferred. Sample characteristics of the identified research population must be effectively presented in a graph using numerical variables. In this context, data collection and analysis is made easier.

However, the method of data collection and analysis must be based on predetermined guidelines to derive the expected results. To ensure that data collected is valid and tamper proof, the use of tape recorded interviews is necessary. In addition, the recorded interviews can be translated from local dialects into standardized transcription protocols for accuracy and future revisions.

Sometimes, qualitative projects require a codebook for data analysis. The codebook’s relevance is to help a data analyst understand the collected information from various perspectives. Moreover, the use of content-based coding scheme is critical in determining data saturation. During qualitative experiments, it is critical to utilize procedures and methods that operationalize, as well as, document data saturation. Prior to data collection, it is important to identify the form of data saturation to be used. From this perspective, various data saturation techniques applicable for qualitative projects include theoretical saturation.

As indicated earlier, the use of a codebook is critical for code development, defining changes, and thematic prevalence. From the codebook, the data analyst understands that sometimes, codes in data exist as variations. From this perspective, variability code frequency and stability is critical to data saturation and the qualitative research outcome. The purpose of consensus analysis in qualitative research r project is undeniably important. In this context, consensus analysis is considered as a technique for sampling.

Conclusion

The consensus theory is based on knowledge and not perception. From this perspective, it is understandable that qualitative projects do not thrive on assumptions. In addition, reality and shared experiences are a significant factor in determining the quality of data collected, as well as analysis of the same. From the article, the homogeneity of the sample population is critical in determining the research outcome. Moreover, data saturation is influenced by data complexity, the number of researchers’ and experience.

Reference

Guest, G., Bunce, A. & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59-82.

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Data Saturation and Variability

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