In this topic, we will go through some statistical analysis and tests that should be used when analysing a survey data. Let’s begin with normality test. Normality test is important because many of the statistical procedures such as correlation, regression, t-test and analysis of variance (ANOVA) are based on the assumption that the data follows a normal distribution. If our data fail to meet the normality assumption, it is impossible to draw accurate and reliability conclusion.

Watch the video below to learn how to perform a normality test with SPSS:


Many research studies focus on determining the relationship between two or more variables. Correlation coefficients are used to assess the strength and direction of the linear relationship between variables. Assume we are interested in the relationship between variable A and B. When the value close to +1 indicate a positive correlation, which means that as variable A increases, variable B also increases. When the value close to -1 indicate a negative correlation, which means that when variable A increases, variable B decreases. A value near to 0 indicates that there is little to no relationship between variables A and B. Let’s watch the video below to learn more, especially about how to perform using SPSS:


Many studies analyse the survey data using a regression model. This common statistical model is important because it can help to (1) determine which variables are significant and which can be ignored or deleted, (2) assess whether there is a linear relationship between the variables, and (3) understand the relative importance of each independent variable and describe the impact on the dependent variable. To get a better understanding on regression analysis, watch this video: 

 


Feel to learn more other statistical tests, let’s watch the video below:

  1. Video 1 (Independent t-test): 

  2. Video 2 (one-way ANOVA test): 

  3. Video 3 (Chi-square test of independence): 

Last modified: Saturday, 23 July 2022, 1:15 AM