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Secondary analysis pertains to the use of secondary data in research. It is a useful research method which saves time and money. Primary data is an exhaustive endeavour that requires time and ample resources to collect data. There are various methods to interpret secondary analysis for qualitative and quantitative data. Valid interpretation of data necessitates knowing the right methods of interpretation. Therefore, this article will provide you with the guidelines on how to interpret secondary analysis for qualitative and quantitative data.       

What is the secondary analysis of qualitative data?

Secondary analysis of qualitative data refers to finding solutions to research questions distinct from those posed in the initial research. It encompasses qualitative data gathered by another party or data gathered to address a different research issue. The data includes answers to open-ended questionnaire questions, field observations, and journal articles. Qualitative data analysis provides the possibility to enhance data usefulness. It is useful when the subject population is out of the researcher’s reach, and collecting primary first-hand data is costly. However, to comprehend, analyse, and assess the study findings to the best of one’s ability, qualitative secondary analysis approaches call for thorough attention and precise description. Therefore, hiring a dissertation writing service UK becomes really important for it.

How to interpret secondary analysis for qualitative and quantitative data?

Step 1: Clearly define your objectives

Any secondary analysis has the issue of creating a question or objective that is worthwhile, meaningful, and able to be answered using data that was not gathered with that reason in mind. Numerous quantitative datasets are vast by structure to enable researchers to answer various queries. Hunting excursions for correlations are difficult to conduct on qualitative data, in contrast to quantitative data. However, answering a question based solely on qualitative or quantitative data without additional supporting evidence is likely to produce a study with a weak justification and difficulties producing a high-quality analysis. It is crucial to align the research goal with the existing qualitative data. It necessitates reviewing existing studies and available data in stages. Additionally, it requires devoting some time to fine-tune the initial study question or objective.

Step 2: Develop a Research Design

For secondary analysis, your research strategy will probably take the form of a step-by-step manual defining the categories of data you need and a list of prospective sources where you may get them. Expectations for the ultimate analysis results may also be included if they are feasible. Based on an initial analysis of the data sources’ reliability requires deciding on the appropriate research design. You will be much better able to focus on prospective secondary data sources after devising your purpose and study design statement. After finalising the statement of purpose and research design, you can start the data gathering process.

Step 3: Choose an effective sampling strategy

A collection of numerous linked interviews or other sorts of information that address the same issue are common sources for secondary qualitative analysis. Even in a huge study, there may occasionally be considerably more interviews conducted than most researchers do. There are various methods for choosing a subsample. Given that the point of randomisation in selection or assignment is tied to the practice of random sampling for generalisation to a population, random sampling of qualitative data is not the appropriate method. In contrast, the usual justifications for sampling in qualitative research emphasise having access to essential participants.

Step 4: Data collection

Planning and carrying out a comprehensive strategy for gathering primary data can take a lot of effort. However, the information you gather will be very pertinent to your research issue. Meanwhile, secondary data collection minimises the difficulty of specifying a study approach. It does, however, present, extra difficulties. Finding the data’s location is one of these. Given the sheer number of secondary data sources available, this is not an easy process. So, your task is to eliminate possible sources. As was stated earlier, exercising caution is vital to avoid overburden.

Step 5: Select appropriate analysis strategies

Secondary data utilisation may be well suited for more adaptable qualitative inquiry techniques, such as case studies, generic or descriptive methods, and mixed methodologies. Some of these encourage the inclusion of additional data kinds, which could help make up for some of the sources’ shortcomings. For instance, archival newspaper articles about the same incident or period may be combined with oral histories detailing that same event or era. You can collect secondary data from the following sources:

• Government Reports, census data and official surveys
• Reports compiled by non-governmental organisations
• Journal articles
• Literature review of books, articles, reports and newspapers
• Online sources such as public archives

Step 6: Data evaluation

You might imagine that once you have a dataset, you are ready to delve in with a thorough analysis because secondary data is typically well-structured. That is not the case, regrettably! You must carefully examine the facts in the beginning. Why? to make sure they are suitable for your requirements. There are two primary tasks in this:

• Assessing the usefulness of the secondary dataset
• Evaluating its overall integrity

How can you evaluate the relevance of secondary data?

Determining whether a secondary dataset is appropriate for your purposes is the main goal of the evaluation process. It is imperative to conduct various in-depth inquiries regarding the data, such as:

• Purpose of Secondary Data

You can learn much about the data’s applicability for your current research by understanding the reasons they were initially obtained. For instance, was the research completed by a commercial corporation or a government organisation for commercial reasons? The response could offer helpful details regarding the demographics of the data, the population sample, or even the wording of particular survey questions. All of this can assist you in determining whether the numbers are accurate for you or if there is any bias present.

• Data Origins

Secondary analysis requires further evaluation of data and asking questions about the data origins. Populations and demographics fluctuate over time. Knowing when the data were first gathered can offer priceless insights. For example, a dataset that initially seems to fit your requirements may be outdated.

Conclusion

Secondary analysis for qualitative and quantitative research requires aligning your research goals with the existing data. The available data might differ from your research objectives. Therefore, it is imperative to ascertain that the dataset meets your research objectives.