Introduction Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Its aim is to reduce a larger set of variables into a smaller set of ‘artificial’ variables, called ‘principal components’, which account for most of the variance in the original variables. There are a number of […]

  Introduction Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable). The variable we are using to predict the

Introduction This series presents R codes that reproduce the results for The Corporate Reputation Model presented in Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R by Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, and Soumya Ray. The goal of The Corporate Reputation Model is to explain the effects of corporate reputation on customer satisfaction (CUSA)

Introduction Structural Equation Modelling (SEM) represents a powerful statistical technique used to test and estimate causal relationships using a combination of statistical data and qualitative causal assumptions. Researchers and analysts employ SEM to explore complex relationships among variables, making it essential in fields such as psychology, sociology, and business research. SEM allows for the simultaneous examination

  Procedure when there are two or more independent variables The Explore… command on its own cannot separate the dependent variable into groups based on not one but two or more independent variables. However, we can perform this feat by using the Split File… command. Click Data > Split File… on the top menu as shown below: You will be presented

We will do your research data analysis and statistical analysis using spss Hi, I’m Rigorous Statistics. With 10 years of experience, We deliver expert research data analysis and niche content creation for clients from all industries ranging from automotive to tech and health. Our analytical specialties include using SPSS, Python and Stata to analyze, interpret,

  Introduction The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups (although you tend to only see it used when there are a minimum of three, rather than two groups). For example, you could use a one-way

Introduction The chi-square test for independence, also called Pearson’s chi-square test or the chi-square test of association, is used to discover if there is a relationship between two categorical variables. SPSS Statistics Assumptions When you choose to analyse your data using a chi-square test for independence, you need to make sure that the data you

  Introduction Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Its aim is to reduce a larger set of variables into a smaller set of ‘artificial’ variables, called ‘principal components’, which account for most of the variance in the original variables. There are a number

Testing Assumptions of Linear Regression in SPSS Starting on the journey of regression analysis in SPSS after collecting your data is a pivotal moment in any research project. Once you have meticulously prepared your dataset, as outlined in our guide on data cleaning and management in SPSS, you are now ready to dive into the