Get Help with Statistical Treatment of Data in Research

Statistical treatment of data is the application of mathematical and analytical techniques to a set of raw data to derive meaningful insights. It mainly involves descriptive statistics that summarize data by describing the relationship between variables in a sample/population and inferential statistics that illustrate whether differences exist among one or two groups of data. Examples of descriptive statistics include mean, standard deviation, median, and range, while inferential statistics include hypothesis testing and regression analysis.

If you are in search of experts to conduct statistical treatment, our professionals apply analytical techniques to raw data to derive meaningful insights used for decision-making. Additionally, this article is a guide to statistical treatment, illustrating its methods, how to write, an example of an application in research, and the types of data.

Methods of Statistical Treatment

(1). Descriptive statistics

Descriptive statistics are used to summarize data in an organized manner by illustrating the relationship between variables in a sample/population. They enable the researcher to make sense of the data set. Categories of descriptive statistics are mean and standard deviation.

The mean describes the central tendency of the data. Finding the mean allows the researcher to characterize observations in a sample. The standard deviation is used to measure the variability in a set of data. Below are the equations used to calculate both the mean, x , and standard deviation, s, assuming the number of measurements is n.

x=x₁ +x₂ +...x_{n⁄ n}

_{S= √∑(Xi -µ)²⁄n}

(2). Regression analysis

Regression analysis is a statistical analysis method where the researcher identifies the relationship between the dependent and independent variables in a data set. The researcher develops an equation that illustrates the changes in the dependent variable depending on the independent variable. A common form of this technique is linear regression which is further divided into simple and multiple linear regression. Below are the equations for each:

Simple-Υ= a + bΧ + u

Multiple- a+b₁X₁ + b₂X₂ +.....b_tX_t + u

In these equations:

Y is the dependent variable

X is the independent variable

a is the y-intercept

b is the slope of the explanatory variable

U is the error

(3). Testing hypothesis

Hypothesis testing is another method of statistical treatment that assesses assumptions of a population according to the statistics retrieved from a sample of a data set. Methods commonly used by researchers for this method are such as t-tests, Analysis of Variance [ANOVA], and the chi-square method.

It is important because it ensures quality assurance in a data set by eliminating errors such as type I and type II errors.

(4). Calculating sample sizes

When calculating sample sizes, the researcher finds the least number of observations that are needed to find the effects of a particular sample size. Calculating the sample size enables the researcher to get quality research results.

How to Write Statistical Treatment

When presenting the results of the statistical treatment of data, key areas to include are:

(1). Type of Data

Illustrate the type of data that was collected to justify the method of statistical treatment used. In statistics, data is categorized into two main classifications, qualitative and quantitative, which are further divided into: 1.) Nominal 2.) Ordinal 3.) Interval 4.) Ratio. Nominal and ordinal are qualitative categories of qualitative while the interval and ratio are quantitative data.

(a). Nominal Data

Nominal data is used for naming and labeling variables without any quantitative value. Researchers classify their observations into two or more categories that do not follow a specific order. Typical examples include education level (undergraduate, doctorate, postgraduate), disciplines of research (Biology, Psychology, Engineering), and research methods (qualitative, quantitative, mixed methods).

(b). Ordinal Data

Ordinal data comprises categories that can be arranged in a rank. The distance between each classification cannot be calculated but the classes can be ranked above and below the other. A typical example is the degree of agreement to disagreement. The responses are coded as numbers that symbolize a hierarchical order but do not reflect the real distance between the answers.

(c). Interval Data

Interval data is numerical, measured on a continuous scale, and has no true zero point. Arithmetic operations such as multiplication and division are not applicable in interval data. Typical examples include IQ scores, personality, and aptitude test results.

(d). Ratio Data

Ratio data has the presence of zero as a starting point, has the most information about the values, and all four arithmetic operations (addition, subtraction, division, multiplication) can be performed on it. Since ratio data has a starting point of zero, values less than zero are not possible. Typical examples include per capita income, return on investment, and the concentration of a solution in an experiment.

(2). Methods of Statistical Treatment

Specify the research design used and methods of data collection employed. In quantitative research, these include descriptive, correlational, experimental, and quasi-experimental, each categorized depending on the data collected. Describe the statistical treatment methods used and a justification for selecting the procedure. Illustrate how the assumptions for statistical tests were checked. An example is Levene’s test, which tests for the homogeneity of variance, Q-Q plots to test for normality or skewness, and Kurtosis to test for normal distribution.

(3). Statistical Analysis Software

Statistical analysis software allows the researcher to avoid mistakes and produce accurate figures in research if data is input correctly. Common statistical software packages include Statistical Package of Social Sciences (SPSS), Statistical Analysis System (SAS), MINITAB, STATA, R, MATLAB, and Microsoft Excel. List any statistical analysis software packages used for analysis in the study.

Statistical Treatment of Data in Research Example

A researcher wanted to do a statistical analysis of two marketing research methods to test their effectiveness. They collected data based on two methods: interviews and focus groups. The data was collected from two groups of people in different countries using stratified sampling, measuring for variables like customer turnover rate and sales. They described the data using descriptive statistics and compared the means using t-tests. The researcher got a difference of p<0.5, showing that advertising made an impact on company sales. They then did statistical analysis using SPSS and analyzed the results.

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Summary

Statistical treatment of data is the use of statistical methods to turn a set of raw data into meaningful conclusions. The purpose of doing the statistical treatment is to come up with accurate, evidence-based results that are great for decision-making. Various methods can be used to do statistical treatment of data such as descriptive statistics, inferential statistics, and hypothesis testing. These processes require you, as the researcher, to have technical knowledge of the procedures. Hiring an expert to do statistical treatment of data assures you of quality results because they customize the help they offer according to your research needs. So, are you looking for someone to do statistical analysis for your company? Reach out to us today to get started!