Which bivariate statistical analysis

London: Sage. Google Scholar. Glass, G. Statistical methods in education and psychology. Gravetter, F. Statistics for the behavioral sciences. Belmont, CA: Thomson Wadsworth. In this case, we say that the bivariate data has:. A classical example of dependent and independent variables are age and heights of the babies and toddlers. When age increases, the height also increases. Look at the following bivariate data table. It represents the age and average height of a group of babies and kids.

So, we use bivariate data to compare two sets of data and to discover any relationships between them. Bivariate Data Analysis. Bivariate analysis allows you to study the relationship between 2 variables and has many practical uses in the real life. It aims to find out whether there exists an association between the variables and what is its strength. Bivariate analysis also allows you to test a hypothesis of association and causality.

It also helps you to predict the values of a dependent variable based on the changes of an independent variable. You have a sample of 10 workers aged thirty to fifty-five years. The results are presented in the following bivariate data table. Bivariate data is most often displayed using a scatter plot. This is a plot on a grid paper of y y-axis against x x-axis and indicates the behavior of given data sets. Scatter plot is one of the popular types of graphs that give us a much more clear picture of a possible relationship between the variables.

The above scatter plot illustrates that the values seem to group around a straight line i. You can create scatter plots very easily with a variety of free graphing software available online. It is obvious that there is a relationship between age and blood pressure and moreover this relationship is positive i. The older the age, the higher the systolic blood pressure.

The line of best fit aims to answer the question whether these two variables correlate. It can be used to help you determine trends within the data sets. Furthermore, the line of best fit illustrates the strength of the correlation.

We have strong correlation when there is little space between the data points and the line. In our example above, we have a strong correlation. While descriptive statistics describes the characteristics of a single variable, inferential statistics examines the relationship between two or more variables. Bivariate statistics is a type of inferential statistics that deals with the relationship between two variables.

That is, bivariate statistics examines how one variable compares with another or how one variable influences another variable. This entry explains bivariate statistics by giving concrete examples from communication research. This entry also elaborates on two different types of bivariate statistics i. The significance level for Native Hawaiians and Pacific Islanders is also relatively high.

So what does this mean? In our hypothetical data set, since we only have race and income, this is a great analysis to conduct. Probably not. A two-way ANOVA no post is a statistical procedure to compare the means of a variable across groups using multiple independent variables to distinguish among groups. For instance, we might want to examine income by both race and gender, in which case, we would use a two-way ANOVA.

However, going far beyond a two-way ANOVA increases your likelihood of a type I error, for the reasons discussed in the previous section. There are entire courses and textbooks on the multiple different types of regression analysis, and we did not think we could adequately cover regression analysis at this level. The characteristics we assume about our data, like that it is normally distributed, that makes it suitable for certain types of statistical tests.

A relationship where it appears that two variables are related BUT they aren't. Another variable is actually influencing the relationship. Skip to content Chapter outline What is bivariate data analysis? Learners will be able to… Define bivariate analysis Explain when we might use bivariate analysis in social work research. Most of the assumptions for these tests are the same, and the video at this link goes through the assumptions for linear regression.

You may not understand these yet, but it will be a good resource for you as you move through your research classes. Bivariate analysis is a group of statistical techniques that examine the relationship between two variables. You need to conduct bivariate analyses before you can begin to draw conclusions from your data, including in future multivariate analyses. Statistical significance and p-values help us understand the extent to which the relationships we see in our analyses are real relationships, and not just random or spurious.

Find a study from your literature review that uses quantitative analyses. What kind of bivariate analyses did the authors use? What do the p -values of their analyses tell you? Learners will be able to… Explain the uses of Chi-square test for independence Explain what kind of variables are appropriate for a Chi-square test Interpret results of a Chi-square test and draw a conclusion about a hypothesis from the results.

The Chi-square test is designed to test the null hypothesis that our two variables are not related to each other. A statistically significant Chi-square statistic means we can reject the null hypothesis and assume our two variables are, in fact, related.

Think about the data you could collect or have collected for your research project. If you were to conduct a chi-square test, consider: Which two variables would you most like to use in the analysis?

What about the relationship between these two variables interests you in light of what your literature review has shown so far? Learners will be able to… Define correlation and understand how to use it in quantitative analysis Explain what kind of variables are appropriate for a correlation Interpret a correlation coefficient Define the different types of correlation — positive and negative Interpret results of a correlation and draw a conclusion about a hypothesis from the results.

A correlation between two variables does not mean one variable causes the other one to change. Correlations are a useful starting point for almost all data analysis projects. The magnitude of a correlation describes its strength and is indicated by the correlation coefficient, which can range from -1 to 1. A positive correlation, or direct relationship, occurs when the values of two variables move together in the same direction.

A negative correlation, or inverse relationship, occurs when the value of one variable moves one direction, while the value of the other variable moves the opposite direction.

If you were to conduct a correlation analysis, consider: Which two variables would you most like to use in the analysis? Learners will be able to… Describe the three different types of t-tests and when to use them. Explain what kind of variables are appropriate for t-tests. There are three types of t- tests that are each appropriate for different situations.

T-tests in general compare the means of one variable between either two points in time or conditions for one group, two different groups, or one group to an external benchmark variable..

In a paired-samples t-test , you are comparing the means of one variable in your data for the same group , either at two different times or under two different conditions, and testing whether the difference is statistically significant.

In an independent samples t-test , you are comparing the means of one variable in your data for two different groups to determine if any difference is statistically significant. In a one-sample t-test , you are comparing the mean of one variable in your data to an external benchmark, either observed or hypothetical.

If you were to conduct a t-test, consider: Which t-test makes the most sense for your data and research design? Which variable would be an appropriate dependent variable?