Why use bivariate analysis

Statistical methods in education and psychology. Gravetter, F. Statistics for the behavioral sciences. Belmont, CA: Thomson Wadsworth. Michalos, A. Determinants of health and the quality of life in the Bella Coola Valley. Social Indicators Research, 72 1 , 1— Miles, J. Applying regression and correlation: A guide for students and researchers. Debra Dallie Sandilands 1 Email author 1. In case the dependent attribute is ordinal then the ordered probit or the ordered logit is used.

It is possible that the dependent attribute could be internal or a ratio like the scale of temperature. This is where regression is measured. Here is how we mention the kinds of bivariate data correlation. In this kind of variable both the variables of the bivariate data which includes the dependent and the independent variable have a numerical value.

When both the variables in the bivariate data are in the static form then the data is interpreted and statements and predictions are made about it. During the research, the analysis will help to determine the cause and impact to conclude that the given variable is categorical. Bivariate analysis is a kind of statistical analysis when two variables are observed against each other.

One of the variables will be dependent and the other is independent. The variables are denoted by X and Y. The changes are analyzed between the two variables to understand to what extent the change has occurred.

Bivariate analysis is the analysis of any concurrent relation between either two-variable or attributes. The study will explore the relationship that is there between the two variables as well as the depth of the relationship. It helps to find out if there are any discrepancies between the variable and what the causes of the differences are.

The bivariate analysis examples are used is to study the relationship between two variables. Let us understand the example of studying the relationship between systolic blood pressure and age. Here you take a sample of people in a particular age group.

Say you take the sample of 10 workers. The first column will have the age of the worker and the second column records their systolic blood pressure. The table then needs to be displayed in a graphical format to make some conclusion from it.

The bivariate data is usually displayed through a scatter plot. Here the plots are made on a grid paper y-axis against the x-axis and this helps to find out the relationship between the data sets that are given. A Scatter plot helps to form a relationship between the variables and tries to explain the relationship between the two.

Once you apply the age on the y-axis and the systolic blood pressure on the x-axis you will notice possibly a linear relationship between them. The graph will show that there is a strong relationship between age and blood pressure and that the relationship is positive. This is because the graph has a positive correlation.

The alternative hypothesis is that they are not the same. For example, say you are testing the effect of pet ownership on anxiety symptoms.

You have access to a group of people who have the same diagnosis involving anxiety who do not have pets, and you give them a standardized anxiety inventory questionnaire. Then, each of these participants gets some kind of pet and after 6 months, you give them the same standardized anxiety questionnaire.

To compare their scores on the questionnaire at the beginning of the study and after 6 months of pet ownership, you would use paired samples t-test. If the t-statistic is statistically significant, there is evidence that owning a pet has an effect on scores on your anxiety questionnaire. The two samples might have been tested under different conditions in a between-subjects experiment, or they could be pre-existing groups in a cross-sectional design e.

The null hypothesis is that the means of the two populations are the same. You have access to two groups of participants: pet owners and non-pet owners. These groups both fit your other study criteria. You give both groups the same questionnaire at one point in time. You are interested in two questions, one about self-worth and one about feelings of loneliness. If the t-statistic is statistically significant, then there is evidence of a difference in these scores that may be due to pet ownership.

This t-test is appropriate when there is an external benchmark to use for your comparison mean, either known or hypothesized. The null hypothesis for this kind of test is that the mean in your sample is different from the mean of the population. The alternative hypothesis is that the means are different. This kind of t-test is useful when a phenomenon or intervention has already been studied, or to see how your sample compares to your larger population.

Analysis of variance no post, generally abbreviated to ANOVA no post for short, is a statistical method to examine how a dependent variable changes as the value of a categorical independent variable changes. It serves the same purpose as the t-tests we learned in ANOVA is more flexible in that it can handle any number of groups, unlike t-tests, which are limited to two groups independent samples or two time points dependent samples.

The most common type of ANOVA that researchers use is the one-way ANOVA no post, which is a statistical procedure to compare the means of a variable across three or more groups of an independent variable. The data in Table Off the bat, of course, we can see a difference in the average income between these groups. Now, we want to know if the difference between average income of these racial and ethnic groups is statistically significant, which is the perfect situation to use one-way ANOVA.

To conduct this analysis, we need the person-level data that underlies this table, which I was able to download from IPUMS. With the basic analysis, the first table in the output was the following. The important thing to noticed here, however, is our significance level, which is.

Sounds great! But we actually get very little information here — all we know is that the between-group differences are statistically significant as a whole, but not anything about the individual groups. This is where post hoc tests come into the picture.

Because we are comparing each race to each other race, that adds up to a lot of comparisons, and statistically, this increases the likelihood of a type I error. However, there are other types of post hoc tests you may encounter. When I tell SPSS to run the ANOVA with a Bonferroni correction, in addition to the table above, I get a very large table that runs through every single comparison I asked it to make among the groups in my independent variable — in this case, the different races.

Figure Now we see some points of interest. Interestingly, for Asian people in the US, race appears to have no influence on their income compared to White people in the US.

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?