Every responses derived during a research either primary sources or secondary sources researches data analysis so as to determine the outcome of the independent variable in the study on the dependent variable. Thus, data analysis is a very crucial element in every research and need to be known and practiced by every researcher including university undergraduates. Meanwhile, despite the importance of data analysis in a research, some scholars specifically university students still find it tedious and impossible on the approach to carry out data analysis. This article is therefore aimed at stating a step by step approach to data analysis during research.

What Is Data Analysis?
Data analysis is the process utilized by researchers to reduce large quantity of data and interpret it to obtain insights. It is the process of cleansing, converting, and evaluating data in order to identify project topics and guide usable information for commercial decision-making is known as data analysis. The data analysis method aids in the reduction of a large set of data into small parts, which is understandable. However to do be able to perform data analysis, researchers must be able to identify the types of data that can be collected in a research. Basically, there are three types of data, namely;
1. Qualitative data
2. Quantitative data and
3. Categorical data
Qualitative data is a type of data that are present in words and not in numerical forms. Although this data can be observed, it is subjective and difficult to examine in study, particularly for comparison. Quality data, for example, includes everything that describes flavor, experience, texture, or an opinion. This type of information is typically gathered through focus groups, personal interviews, or open-ended questions in surveys.
Quantitative data in the other-hand is a type of data that is expressed in numbers of numerical figures. This form of data can be classified, grouped, measured, calculated, or rated. For example, age, rank, cost, length, weight, scores, and so on are all examples of this type of data. This kind of data can be presented in graphical form, such as charts, or you can use statistical analytic tools to analyze it. Outcomes Measurement Systems (OMS) questionnaires in surveys are a key source of numerical data collection.

Finally, the categorical data is a kind of data data presented in groups. A categorical data item, on the other hand, cannot belong to more than one group. Categorical data includes, for example, a person answering to a survey by stating his living style, marital status, smoking habit, or drinking habit. A chi-square test is a common way for analyzing this data.
Steps In Conducting Data Analysis

After knowing the types of data that are collected by researchers, it is pertinent to ascertain how this data can be analysed. The following are the possible steps used in analysing data;
Step 1: Data Validation
Data validation must be performed to determine whether the gathered data sample conforms to the pre-set standards or is a biased data sample, which is separated into four steps. It is also done to ensure that each response to the survey or questionnaire is recorded by a real person. To ensure that each participant or respondent is chosen in accordance with the research criteria. To check that ethical standards were followed when collecting the data sample and that the respondent answered all of the questions in an online survey. Otherwise, the interviewer had asked all of the questionnaire's questions.

Step 2: Data Editing
More often than not, a large study data sample is riddled with flaws. Respondents may fill in some fields inaccurately or mistakenly skip others. Data editing is a method in which researchers ensure that the data provided is error-free. They must perform the appropriate checks and outlier checks to edit the raw edit and prepare it for analysis.

Step 3: Data Coding
This is the most important phase of data preparation since it involves categorizing and assigning values to survey replies. If a survey with an education research topics sample size of 500 is finished, the researcher will establish an age bracket to differentiate the respondents based on their age. As a result, analyzing tiny data buckets rather than dealing with a big data pile becomes easier.

Step 4: Choosing a Statistical Method
After validating, editing and coding the data, the last thing to do is to select a suitable statistical method. Researchers are open to adopting various research and data analysis approaches to derive useful insights once the data has been prepared for study. Statistical techniques, without a doubt, are the most popular for analyzing numerical data. The procedure is divided into two groups once more. To begin, the term 'Descriptive Statistics' is used to characterize data. Second, there is 'inferential statistics,' which aids in data comparison.

The use of descriptive analysis in quantitative research frequently yields absolute numbers, but the analysis is never sufficient to illustrate the rationale behind those numbers. Nonetheless, it is vital to consider the optimal research and data analysis strategy for your survey questionnaire and the story researchers wish to tell. In schools, for example, the mean is the greatest approach to represent students' average scores.
When the researchers wish to keep the research or conclusion local to the specified sample without generalizing it, descriptive statistics are preferable.

When comparing average votes in two cities, for example, differential statistics are sufficient. Some of the method in use in descriptive analysis are measures of frequencies (count, percentage and frequency),measures of central tendencies (Mode, mean and Median) and measures of positions (Range, Variance and Standard deviation).
Also, inferential analyses are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. It is used basically in estimating data and testing of hypotheses. Some of the methods used in inferential analysis include correlation, cross tabulation, regression analysis, frequency tables and analysis of variance.

Finally, for data analysis in research to be considered, the researcher should be note the following;
1. must have the necessary skills to analyze the data, Getting trained to demonstrate a high standard of research practice.

2. getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods, and choose samples.

3. find a way to deal with everyday challenges like outliers, missing data, data altering, data mining, or developing graphical representation in other to achieve accuracy in the result of the study.

4. Try to be neutral when interpreting the result of the study.