Help to Develop a Data Analysis Approach/Strategy for Research Papers

To make meaningful insights from collected data, it is essential to understand what data analysis approach for research papers is. Based on the type of research, one may apply statistical analysis methods or logical techniques to describe, condense, evaluate, and analyze data. Both quantitative and qualitative research papers require unique data analytics to make them useful.

Qualitative data is collected using techniques such as focus groups and interviews. Analyzing data from such sources entails common pattern identification within responses and thoroughly examining them to reveal meaningful qualitative insights to achieve specific aims and objectives.

Quantitative research involves collecting numerical values and analyzing them using data analysis software/tools to make statistical inferences and conclusions. This article contains details of some of the factors we consider when developing a data analysis approach/strategy for research papers.

Factors We Consider When Developing a Data Analysis Strategy for Research Papers

Industries such as research institutions, businesses, healthcare, education, fashion, and all other sectors consider data analysis important today. Studies show that there will be 175 zettabytes of data by 2025, thus, the most appropriate data analysis techniques and strategies must be developed to extract the value hidden in such data. Some of the factors we consider when developing a data analysis approach for research papers include:

1. Research design adopted

Data collection methods and the research designs adopted by researchers determine the type of responses they elicit from the participants. Before developing a data analysis strategy for research papers, we have to determine whether the study design was qualitative; explanatory, narrative, phenomenology, ethnographic, and historical data interpretation, or quantitative; correlational, descriptive, causal-comparative, and experimental.

2. The research question

The general research topic/question/scope determines the type of data collection conducted and the analysis method required to extract meaning from such data. The questions/objectives may be exploratory, explanatory, comparative, predictive, interpretive, or relationship based. Each type of research question or objective can be addressed by collecting appropriate data types and developing unique analysis approaches to derive meaning and draw rational conclusions.

3. Data types to be analyzed

Research data can be qualitative or quantitative in nature. Qualitative data is descriptive and contain categorical variables which can be nominal or ordinal. Quantitative data are numerical in nature and contains continuous and discrete variables. Continuous data can be further classified into interval and ratio.

4. Type of data analysis required

The type of data analysis to be conducted depend on the goals and objectives of the research. The most common types of analysis include:

• Descriptive analysis.
• Diagnostic analysis.
• Predictive analysis.
• Prescriptive analysis.

Data analysis types can also be classified based on research methods as either qualitative or quantitative as discussed below.

a). Qualitative data analysis methods

Qualitative research is focused on words, ideas, descriptions, or concepts. Different qualitative data analysis methods are used depending on the nature of the research question being answered and the aims and objectives of the study. The main categories of qualitative data analysis methods include:

• Narrative analysis.
• Thematic analysis.
• Discourse analysis.
• Content analysis.
• Grounded theory.
• Interpretive phenomenological analysis.

Each of the qualitative data analysis methods has its unique strengths and weaknesses and serves different purposes in research.

b). Quantitative data analysis methods

Quantitative data analysis in research involves the use of descriptive and inferential statistics. The process involves using specific data analytics tools and software and applying critical and rational thinking to turn raw data into meaningful information. Descriptive statistics uses statistical techniques to summarize data, find patterns, and provide a basic understanding of numerical information.

Inferential statistics allow researchers to make predictions by analyzing data from a sample and generalizing the research findings to an entire population. Inferential statistics are useful in estimating parameters and hypothesis testing.

The factors we consider when developing a quantitative data analysis technique include:

• Types of variables in a data set; continuous or discrete.
• The research question, which may be correlational, causal, or comparative.
• The study design adopted, such as correlational, experimental, quasi-experimental, regression analysis,
• The level of measurement for the dependent variable or the independent variables such as intervals and
• The distribution of data and underlying assumptions of parametric and non-parametric tests.
• The level of statistical significance for observations.

The type of data analysis process/approach to use should be determined during the planning stages to conduct a seamless research practice and make informed decisions from the findings.

5. Reliability and validity of data analysis methods

When designing a data analysis approach for research papers, we take into account the challenges to the validity and reliability of the process. All the operational processes of data collection, data cleaning, data mining, and data visualization up to the time one draws meaningful conclusions must demonstrate stability, reproducibility, and accuracy.

6. The extent of analysis required

The extent of data analysis required depends on the complexity of the research question and the aims or objectives. Some questions can be answered by calculating descriptive statistics while others may require advanced statistical advice and the use of various data analysis tools to achieve the objectives of the study. Our data analysts and statisticians ensure they understand the extent to which the collected data is to be analyzed to develop the most appropriate strategy to achieve the purpose.

Whether statistical or text analysis techniques are used, every analyst must be committed to ensuring data integrity at all times. We develop the best data analysis approaches for research papers to help students and scholars in the cleaning, reduction, analysis, and interpretation of the collected data to achieve set goals and objectives.