Introduction
SPSS is a windows based tool & to deal with it SPSS help is required. It is a versatile and responsive programme which specifically works for statistical data. We can find other statistical tools as well but due to its easy-to-navigate feature and other positives, it is famous and in demand amongst other tools and software. SPSS deals with different categories of analyses such as correlation analysis, regression analysis, ANOVA & MANOVA etc. This blog is all about correlation analysis using SPSS and how can you perform it.
How to perform correlation analysis using SPSS?
Correlation is mainly the statistical association, which is commonly referred to as the degree to which a pair of variables is linearly related to each other. The correlation coefficient (r) is a measurement of the degree to which the movement of two different variables can be analysed. Hence, through the value of r, it is possible for the researchers to analyse the interlink between the two variables. In statistical analysis, correlation or dependence is considered as a statistical relationship, whether causal or not, between two random variables or bivariate data in the whole data set. Hence, the value of r mainly indicates the relation between the two variables. Through this correlation coefficient analysis, the data analysts or the researchers can explore the value of r which lies between -1 and +1. A positive correlation indicates that the variables are correlated positively with each other, and on the other hand, the negative value of r refers that there negative correlation between the variables in the data set. Additionally, if -1 ≤ r ≤ +1, where r is equal to zero, it means that there is no such relationship between the two variables in the data set. The Pearson correlation coefficient is mainly used widely when researchers try to identify interlinks between the variables in a specific data set. In order to conduct the dissertation, it is necessary to use SPSS and perform correlation analysis. Through this analysis, it is possible for the researchers to identify the impacts of the independent variables on the dependent ones. There can be more than one independent variable, and in SPSS, it is easy to run the correlation analysis by choosing a systematic way.
In the Data set, there should be two or more continuous numeric variables, each defined as a scale, which will be utilised in the analysis. Each row in the dataset further represents one unique subject, person or unit. All the measurements taken on that person or unit should appear in that row. In order to run the bivariate Pearson correlation analysis, it is important for the researchers to sort the large volume of data with effective names and coding. After data sorting and management, the major steps to run the correlation, the functions are analysed, correlation and bivariate. The bivariate correlations window will be opened further, where the researchers must specify the variables used in the analysis. All of the variables in the data set are explained on the left side. In order to select variables for the analysis, choosing the variables in the list on the left and clicking the blue arrow button to move them to the right, in the Variables field are the major steps. The variables indicate the specific variables that are used in the bivariate Pearson correlation. The researchers need to choose at least two continuous variables from the data set, but they also can select more than two. The test will result in the correlation coefficients for each pair of variables in this list. There are different types of correlation models in SPSS, and Pearson correlation is widely used among them. The test of significance is necessary to be maintained, by clicking two-tailed or one-tailed, depending on the desired significance test. SPSS mainly uses a two-tailed best by default. Checking this option will include asterisks next to statistically significant correlations in the output; it is possible to flag significant correlations. By default, SPSS marks statistical significance at the alpha = 0.05 and alpha = 0.01 levels, but not at the alpha = 0.001 level. the researchers can specify which Statistics through clocking the options include which opens up different methods of Cross-product deviations, means and standard deviations as well as covariance, on the other hand, it also explains the ways to address Missing Values by excluding the cases pairwise or Exclude cases list wisely. This is the whole process of performing correlation in SPSS, where the researchers can explore the internal linkage between the dependent and independent variables in the specific data set.
Conclusion
In the SPSS data analysis, the large data set is also manageable through data sorting. Before performing correlation, and testing the hypothesis in the dissertation, the researchers must input the gathered data in a systematic way in SPSS, where it is possible to conduct data sorting and management. It is suitable for the researchers to progress in the study by putting the data variables, naming and appropriate coding, so that the dependent and independent variables are defined well. After performing the correlation and regression analysis in SPSS, the ultimate results will appear in tabular form, where the value of r is defined. If it is positive, there are positive impacts of the independent variable on the dependent one. A negative value of r refers to that there is a negative correlation and zero indicates no such correlation is there between the variables. Hence, in order to perform correlation and test the research hypothesis, the Pearson correlation is widely utilised where the researchers use SPSS to conduct in-depth critical analysis and evaluation.
