DATA ANALYSIS (recommended for every student)

in #education5 years ago

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DATA ANALYSIS

I am writing this post in other to help millions of students such as myself and also students below and above me..
The importance of data analysis can not be over emphasised whether in;

management
science
technology
engineering
philosophy
accounting
finance E. T. C..

Data analysis is so broad that we need it in our much areas of our life but we will only be focusing on the educational aspect of data analysis in this article. This article is very useful for the high school students, college, masters students and PhD students


Let's firstly define what data
Data can be defined as a raw fact or unprocessed information. Data is short hand for ‘information’, and whether you are collecting, reviewing, and/or analysing data this process has always been part of Head Start program operations. In 1996 Webster’s II New Riverside Dictionary Revised Edition defines

data as information, especially information organized for analyses.

Taking from the above definitions, a practical approach to defining data is that data is numbers, characters, images, or other method of recording, in a form which can be assessed to make a determination or decision about a specific action.

Analysis refers to breaking a whole into its separate components for individual examination.

Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users.

Data is collected and analyzed to answer questions, test hypotheses or disprove theories (Neil & Schutt, 2013). Statistician John as cited in Neil and Schutt (2013) also defined data analysis in 1961 as:

Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.


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Types of data analysis

They are three types of data analysis, which are quantitative data analysis, qualitative data analysis and mixed data analysis. They are explained further below;

  • Quantitative Analysis : Quantitative analysis uses numerical data to identify statistical relationships between variables. Quantitative data are numerical, ordinal, and nominal. For example, surveys, questionnaires, and evaluations that include multiple choice items and ratings (e.g., Likert scale) provide quantitative data for analysis.
  • Qualitative Analysis: Qualitative analysis uses descriptive data to understand processes (e.g., how students learn in a group), develop insights into the form of sensitizing concepts, and present the view of the world from the point of view of the participants (e.g., the teachers, students and others related to the classroom). Qualitative data are descriptive. For example, field notes, interviews, video, audio, open-ended survey questions all provide qualitative data for analysis.
  • Mixed data analysis: this involves the combination of the use of both quantitative and qualitative data analysis techniques to analysis a data which can be used to get a tangible result or information.

Forms of data analysis

When you begin your data analysis project, you typically begin by analyzing each variable independently to describe the data you have and assess its quality. No data analysis however is effective until it leads to a decision or action step.
The types of data analysis are explained below:

  • Descriptive analysis: the simplest form of data analysis is descriptive analysis. Descriptive analysis lists and summarize the values of each variables in a data set for example if a survey respondents provided a rating from 1 to 1o for a particular question, a descriptive analysis might show the number and percentage of respondents for each rating, the average and median ratings, the mode or most common rating and some measures of central tendency such as standard deviation.
    Descriptive analysis helps you become familiar with a data set and to identify problems with data, such as respondents who didn’t provide any rating at all or data shows a response of 99.

  • Exploratory analysis : once you understand the data you have, the next step is to start looking for relationships among data elements. This is called exploratory data analysis and typically focuses on correlations among variables. For example, one data set shows an extremely high correlation between the number of cavities a child has and the size of her vocabulary. However, this does not suggest that if you allow your child to get more cavities, her vocabulary will also grow, there might be other factors that are driving the results, such as age, and that you don’t have an idea of in the data set.

  • Inferential analysis : it aims to test theories about the nature of the world in general (or some part of it) based on samples of subjects taken from the world (or some part of it). That is, use a relatively small sample size of data to say something about a bigger population. Inference is commonly the goal of statistical model. Inference involves both estimating quantity you care about and your uncertainty about your estimate. Inferential analysis depends heavily on both the population and the sampling scheme

  • Predictive analysis : the various type of methods that analyze current and historical facts to make predictions about future events, in essence ton use the data on some objects ton projects values for another object. The model predicts, but also it does not mean that the independent variables cause. Accurate predictions depend heavily on measuring the right variables.


Data collection is the process of gathering and measuring information on targeted variables in an established systematic fashion, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a component of research in all fields of study including physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same


reference 1

reference 2

[reference 3]: Judd, Charles and, McCleland, Gary (1989). Data Analysis. Harcourt Brace Jovanovich. ISBN 0-15-516765-0)

[reference 4] :O'Neil, Cathy and, Schutt, Rachel (2013). Doing Data Science . O'Reilly.
ISBN 978-1-449-35865-5

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