Univariate Analysis using SPSS -Statistical Aid

Three different types of procedures can be used to analyze data in statistics. These include univariate, bivariate, and multivariate analysis. The number of variables and the type of data determine which data analysis method is used. Additionally, we have to consider the objectives of the statistical analysis. We can easily conduct univariate analysis using SPSS, Stata, R, and Excel software. The univariate analysis details are described in this article.

What is univariate analysis?

Univariate analysis is a simple and fundamental type of statistical data analysis where only one variable is used. There is no relationship between a cause and an effect in this data set because there is just one variable. It is mostly used to describe data. After summarizing the data, the analysis will look for patterns.

Types

There are some essential and popular univariate analyses,

Summary Statistics: It is the most popular method for conducting univariate analysis. Measures of dispersion and measures of central tendency are common techniques of summary statistics. The range, standard deviation, variance, and interquartile range are some examples of measures of dispersion, and mean, median, and mode are basic measures of central tendency. We also used a frequency distribution table, which tells us the number of times for the occurrence of an event.
Bar chart: Rectangular bars are used to describe the bar chart. Various categories will be compared in the graph. These could either be plotted horizontally or vertically on the graph. The bar graph explains and compares the data set.
Pie Chart: The pie chart displays the data in a circular format. The graph is divided into slices, where each slice is proportional to the fraction of the complete category. So each slice of the pie chart is relative to the category’s size. The sum of all slices of the pie chart must be 100 percent.

We also use a frequency polygon and a histogram to describe univariate characteristics of a variable.

Univariate Analysis Using SPSS

We conduct Univariate analysis using SPSS to find the characteristics of the variable. SPSS is a simple statistical analytics tool that is very easy to use and maintain. It is the analysis of one random variable, such as descriptive statistics (mean, variance, etc.).
We have the following dataset with several highlighted variables.

Now we have to go through the following steps.

Let us have five variables named gender, age, GPA, social_media, and result. If we analyze each of the variables separately, then it’s called univariate analysis. We can find the mean, variance, and other descriptive statistics using univariate analysis. For univariate analysis, first we have to go to–

1.Analyze →→Descriptive statistics→→ Frequencies

Now we have to select all the variables and take them into the empty box named Variable(s).

2. We have to click on Statistics

3. Select all the required area,s such as mean, median, standard deviation, etc and click on Continue.

4. If we want the graph, such as a bar chart or, pie chart, then click on Charts and select the chart types, and click on continue.

5. click on Ok .

6. Now we can see the output window as follows.

Interpretation of the Result

This analysis summarizes data from 20 students. The sample exhibits a balanced gender distribution (mean 1.50) and an average age of roughly 22 years (mean 21.95), with a slight skew towards older students. Grade Point Averages (GPA) average around 3.26 (mean 3.2555), showing a moderate spread and a slight negative skew, indicating a tendency towards higher grades.

Regarding the perception of social media’s helpfulness, students lean slightly towards disagreeing with the statement that it’s not helpful (mean 2.90), though opinions vary considerably. Categorical GPA data centers around the “Medium” category (median 2.00, mode 2.00) with an average of 2.35, suggesting a distribution that skews somewhat towards higher categories.

In essence, the student group is characterized by a balanced gender mix, an average age in the early twenties, and a generally good academic standing. While opinions on social media’s utility vary, there’s a slight inclination to see it as helpful. Overall, the data paints a picture of a fairly typical student population with moderate variations in age, academic performance, and viewpoints.

Graphical Output

The data, gathered from 20 students, reveals a balanced gender distribution and an average age of around 22. Student GPAs average 3.26, with a slight negative skew suggesting a tendency towards higher grades. Opinions on social media’s helpfulness vary, leaning slightly towards agreement.

The categorical GPA distribution, visually represented in the bar chart, is bimodal. A significant portion of students (9 each) fall into the “1st class” and “2nd class” categories. Only a small fraction (2) are classified as “3rd class.” This distribution aligns with the descriptive statistics, where the mean categorical GPA was 2.35, indicating a skew towards higher categories.

The student group has a mix of genders. Their average age is in the early twenties. Also, they generally show good academic performance. This is evident in the high concentration of students in the top two GPA categories. While views on social media vary, the academic data suggests a strong performance overall.

Conclusion

Univariate analysis in SPSS provides a crucial first step in understanding your data. By examining each variable in isolation, we can uncover fundamental characteristics like central tendency, spread, and shape. Through descriptive statistics like means, medians, standard deviations, and visual aids like histograms and bar charts, we gain valuable insights into individual variable patterns and potential outliers.

This initial exploration is far from the complete story, but it lays a vital foundation. It helps us identify data entry errors, understand the distribution of our variables, and informs subsequent, more complex analyses. Mastering univariate analysis in SPSS equips you with the essential skills to effectively summarize and interpret the building blocks of your research, paving the way for deeper and more meaningful discoveries. So, dive in, explore your data one variable at a time, and unlock the initial insights waiting to be revealed!

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