Data Analysis Methods in Research: A Comprehensive Guide

In 2023, 91.9% of organizations achieved exceptional results from investing in analyzing data for business decision-making. Data analysis is the application of tools and techniques to a set of raw data with the aim of gaining meaningful insights. Our data analysis experts offer customized solutions, whether it is analyzing numerical data, descriptive information, or a combined dataset. This post gives a comprehensive guide to the types of data analysis methods, quantitative, qualitative, and mixed methods, highlighting their types, differences, and examples for reference.

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Understanding the correct data analysis method approach to use for your raw dataset is one of the key factors in acquiring reliable results. Factors that determine the analysis procedure to be used are the unit of analysis, the objective of the study, and how the variables are measured. Alternatively, you can choose to hire a data analysis methods expert for guidance on the method of choosing and carrying out the analysis. Our professionals offer customized solutions in multiple fields of research ensuring you get accurate analysis results useful for decision making. In this section, we give a detailed overview of each of the methods of data analysis in research:

1. Quantitative data analysis methods

Quantitative analysis methods are grouped according to the research objective that they are trying to achieve. This can be analyzing the pattern of a phenomenon, determining the effect of one variable on another one, measuring the relationship between two variables, or establishing the cause-and-effect relationship between variables. Some of the statistical methods for data analysis are:

(a). Descriptive/Summary statistics

Descriptive statistics comprise tools and techniques for evaluating data that reveal the trends and patterns of the data set in one way. It involves compressing data into one number, which is known as the summary statistic. Common descriptive statistics are mean, median, standard deviation, range, variance, and kurtosis. This type of quantitative data analysis method is ideal for a researcher who wants to conduct a brief analysis.

(b). ANOVA and MANOVA analysis

When the objective of the research is to compare two or more variables, then one can use either a t-test, ANOVA, or MANOVA analysis. The first one is ideal for comparing two groups, the second one for more than two groups, and the third one is for multiple groups with two or more dependent variables.

(c). Correlation coefficient analysis

The correlation coefficient quantitative analysis method is used when a researcher wants to investigate the existence of a relationship between variables. Additionally, it also measures the strength of the association between the variables. The measure of a correlation ranges from 0-1. 0 indicates the absence of a linear connection, while 1 signifies the presence of a perfect association.

(d). Regression analysis

Regression analysis is used when a researcher wants to examine the impact of multiple independent variables on the dependent variable. The dependent variable is the one being measured by the researcher, while the independent is the one that is being changed.

(e). Causality analysis

A researcher may want to know the cause-and-effect relationship between two variables. In this case, they would use the causality analysis method.

2. Qualitative data analysis methods

Qualitative data is a source of detailed descriptions and comprehensive explanations of human actions. Analyzing qualitative data helps the researcher to identify the events that lead to certain actions and gain meaningful explanations. Below are some of the qualitative data analysis methods:

(a). Content analysis

This is a qualitative data analysis method that analyzes text data through the systematic classification procedure of coding and identification of patterns and themes. It allows the researcher to understand social reality in a scientific way.

(b). Narrative analysis

A narrative is a transcribed experience. This type of analysis involves sorting out and presenting qualitative data obtained from observations, interviews, or focus groups to understand individuals’ different experiences in different contexts.

(c). Discourse analysis

Discourse analysis evaluates the ordinary way people communicate and interact with each other. It is a method of analyzing an interaction, be it verbal, non-verbal, or written.

(d). Framework analysis

Framework analysis involves coding a framework that is developed from emerging issues and then using codes (numerical or textual) to reveal data sets that correspond to different themes.

(e). Grounded theory

Grounded theory uses an analytic induction approach, meaning that the theories are obtained from collected data rather than being formed and tested. It is mainly used when there is little to no information about a theory.

3. Mixed methods data analysis

Mixed methods research involves integrating qualitative and quantitative techniques, approaches, and methods into one study. Below are the types of mixed methods data analysis:

(a). Convergent parallel mixed methods

In the convergent parallel mixed methods approach, the researcher collects both qualitative and quantitative data, analyzes it separately, and then compares the findings to see if the results are aligned or misaligned with each other.

(b). Explanatory sequential mixed methods

The explanatory sequential mixed methods approach involves a two-phase project where the researcher first collects quantitative data, analyzes it then uses the findings from the research to plan the qualitative procedure. The intention of this type of mixed method analysis is to have the qualitative data expound on the quantitative data results.

(c). Exploratory sequential mixed methods

The exploratory sequential mixed methods are like a reversal procedure of the explanatory approach where the researcher begins with collecting qualitative data and analyzes it to use the findings in determining the type of quantitative data to be collected. For example, the researcher would (a) collect data from a focus group, (b) analyze the results, (c) develop an instrument based on the findings, and (d) give it to a sample population.

(d). Embedded mixed methods

In the embedded mixed methods, one or multiple data forms support the other within a much larger design, such as an ethnography or an experiment.

A Quick Reference Guide to the Three Methods

Method	Qualitative	Quantitative	Mixed methods
Purpose	Identifies patterns from concepts and insights.	It tests hypotheses and then uses the data to support conclusions.	Integrates both analysis methods to gain more understanding of the research objective.
Type of data	Non-numerical	Numerical	Both numerical and non-numerical
Software tools	Uses tools such as NVivo, ATLAS.ti, and Dedoose.	Uses tools such as SPSS, SAS, MATLAB, and Stata.	Integrates both tools used in qualitative and quantitative methods, such as MATLAB and NVivo.
Data collection methods	The main methods of data collection are interviews, focus groups, and observation.	Uses methods such as questionnaires, surveys, and conducting experiments.	Uses both methods of data collection.
Analysis	Aims to identify patterns and themes and then interpret occurrences.	Aims to test hypotheses and find value behind numeric data.	Combines the findings of either method to support the analysis of the other.
Examples	A company wanted to identify how they could improve customer satisfaction for their clients for them to recommend their products to other people. They collected data through focus groups and conducted a thematic analysis using NVivo software to evaluate the consumers’ attitudes and feelings toward their products.	A researcher wanted to evaluate whether there is a relationship between screen time and the sleeping hours of an individual. They conducted descriptive statistics and calculated the mean and standard deviation of screen time and sleeping hours then conducted a correlation-coefficient analysis to evaluate the strength of the relationship between the two variables.	A data analyst wanted to evaluate how cultural factors influence consumer behavior in purchasing certain products. They conducted in-depth interviews to gain insights into the feelings and then conducted a t-test on the statistics to evaluate the relationship between the two.

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Summary

Data analysis is the application of tools and techniques to a set of raw data to gain meaningful insights that can be used for decision-making. Three analysis approaches can be used to analyze data depending on the type of data and research objective and they are qualitative, quantitative, and mixed methods analysis. The first analysis method deals with non-numerical data, quantitative deals with numerical data, and mixed-methods works with both types of data. Choosing the right approach is a key factor in achieving meaningful insights. Hiring a data analysis methods expert from our services can be a strategic decision to get the most out of your data. Request a free quote now and get started, or visit our order page to review our prices and make an order.

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