Descriptive statistics are useful in showing characteristics of respondents and variables, but they cannot predict or explain behavior. For this, we needinformative statistics. These allow us to take relationships between variables, make business conclusions about the whole population based on our sample of respondents, and give business recommendations based on our results.
What is inferential statistics?Inferential statistics use advanced techniques and tests to analyze data, unlike descriptive statistics that mainly use single metrics like median, mean, or standard deviation.The history of inferential statisticsstarted with univariate analysis, which analyzes one variable at a time. This was followed by bivariate analysis methods that allow analysis of two variables simultaneously.
First generation multivariate analysis methodsinclude Analysis of Variants, Logistic Regression, Multiple Regression, Cluster Analysis, Exploratory Factor Analysis, and more. However, these methods do not account for the error connected with the analysis, a significant limitation.
Second generation multivariate analysis methods, developed as an answer to this weakness, include CBSEM and PLSSEM. In this article, we will focus on PLSSEM, which will be used for our mobile shopping case study.
How do popular inferential statistics work?*Analysis of Variants: This allows you to check whether there are any significant differences between groups. To understand analysis of variants, first you need to understand the variance measure. Variance shows how much values are different from the mean, how they are spread out, and it is very similar in concept to standard deviation.
*Regression Analysis: Regression is the process for estimating the relationships between variables. The most popular is linear regression, which assumes a linear relationship between variables. To illustrate how linear regression works, let's draw a graph with two variables: salary and experience. We want to find out how changes in experience affect salary.
*Structural Equation Modeling (SEM): SEM is used when you are not able to observe and measure every variable directly. For example, you can't observe and measure a quality of someone's life directly, so you measure variables that can be measured directly like wealth, health, education, etc., and use them to produce an assessment of the quality of someone's life.
Why PLSSEM for our mobile shopping case study?PLSSEM method assumes that there are unobserved constructs (variables) affecting the observed variables. By modeling these relationships, we can make better predictions and draw more accurate conclusions about the behavior of mobile shoppers.
ConclusionUnderstanding inferential statistics is crucial for conducting a successful mobile shopping analysis. By using methods like Analysis of Variants, Regression Analysis, and Structural Equation Modeling, we can gain insights into relationships between variables, make business conclusions based on our sample of respondents, and give business recommendations based on our results.
FAQ1.What is inferential statistics?Inferential statistics are statistical methods used to analyze data and make predictions or draw conclusions about a population based on a sample.
2.Why use PLSSEM for mobile shopping analysis?PLSSEM allows us to model relationships between observed variables and unobserved constructs, giving us a more accurate understanding of the behavior of mobile shoppers.
3.What is the difference between descriptive statistics and inferential statistics?Descriptive statistics provide information about characteristics of a dataset, while inferential statistics allow us to make predictions or draw conclusions about a population based on a sample.
4.What are some common methods of multivariate analysis?Some common methods of multivariate analysis include Analysis of Variants, Logistic Regression, Multiple Regression, Cluster Analysis, Exploratory Factor Analysis, and Structural Equation Modeling.
5.What is PLSSEM in structural equation modeling?Partial Least Squares Structural Equation Modeling (PLSSEM) is a method of SEM that allows us to estimate relationships between observed variables and unobserved constructs using multivariate regression techniques.
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