Data Analysis Methods and Techniques in Research

Data can be seen as raw materials that needs processing before it can be meaningful and useful. Data analysis according to Ashirwadam (2014) is the act of putting facts and figures to solve research problems. This definition identifies the need to collect “facts and figures” and to solve a research problem which is an important feature of research. It does not, however, define the need for these facts to be processed, interpreted and which is also very essential.

Therefore, we describe Data analysis in research as a systematic process by which a researcher gathers relevant data, organizes, processes and interprets the data to give it structure and meaning in order to solve a research problem.

Having defined data analysis, it is pertinent to know that data used for analysis varies and the methodology used for various research also varies. In this study, Hey Research will itemize the methods used in data analysis. This would enable our readers to have an understanding about data analysis techniques and the type suitable for their research. However, the concept of data, the types and why data analysis is needed in a research is not within the scope of this article.

Regardless, we will digress a little by discussing the types of data, although data can be in different forms, “primary data” types will be discussed.

Types of Data

1. Qualitative data: These are data represented as words/texts, opinions, experience, texture and descriptions. Although these types of data are observable, it is subjective and harder to analyze such data in research, especially for comparison. This type of data is usually collected through focus groups, personal interviews or using open-ended questions in surveys.

2. Quantitative data: These are data represented in numerical figures. This type of data is always associated with a scale measure. The data may be represented by ordinal, interval or ratio scales and lends themselves to most statistical manipulation. It is usually collected through Outcomes Measurement Systems (OMS) questionnaires.

3. Categorical data: It is data presented in groups. An item included in the categorical data cannot belong to more than one group. A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data.

Having discussed the types of data, let’s progress to the methods used in data analysis.

Methods of Data Analysis used for Qualitative Data

Here are some commonly used methods for data analysis.

1. Content Analysis: It can be used to analyze the documented information from text, images, and sometimes from the physical items. In this type of analysis, the research questions predict when and where to use it.

2. Narrative Analysis: A type of qualitative analysis that uses field texts such as stories, interviews, letters, conversations, photos, journals, autobiography, field notes, etc. as units to analyze for validating the grounds for the research question.

3. Discourse Analysis: It is used to analyze the interactions with people. It is different from narrative analysis because it considers the social context under which or within which the communication between the researcher and respondent takes place. Also, it stresses on the lifestyle and day-to-day environment while deriving any conclusion.

Methods of Data Analysis used for Quantitative Data

The methods of data analysis for a qualitative data is categorized into two namely:

1. Descriptive statistics: This method is used to describe the basic features of versatile types of data in research. The conclusions derived are based on the hypothesis the researchers have formulated. Unlike qualitative analysis method, the descriptive analysis does not go beyond making conclusions. Measures of Frequency (Count, Percent & Frequency), Measures of Central Tendency (Mean, Median & Mode), Measures of Dispersion or Variation (Range, Variance & Standard deviation), Measures of Position (Percentile ranks & Quartile ranks) are some of the major types of descriptive analysis methods.

2. Inferential statistics: Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you ask 50 people if noodles advertisement on TV makes them purchase noodles, inferential statistics is then used to reason that a certain percent of the respondents purchases noodles because of the advert and a percentage increment in the advert could affect the rate of purchase. Correlation, Chi-square, Cross-tabulation, Regression, Analysis of variance (ANOVA) are some of the major types of inferential statistics.

Hey Research will further elaborate on these analysis techniques in other discussion. For further enquires, contact Hey Research on Heyresearch.edu@gmail.com or through our Facebook page “Hey Research”.