If they do, you will be able to create three separate scales, by summing the items on each dimension.ДЇactor analysis is based on a correlation table. You perform a factor analysis to see if there are really these three factors. Before using the questionnaire on your sample, you pretest it on a group of people similar to those who will be completing your survey. You develop a new questionnaire about customer satisfaction with 30 questions: 10 concerning product knowledge, 10 concerning communication skills and 10 concerning people skills. This involves finding a way of condensing the information contained in some of the original variables into a smaller set of implicit variables (called factors) with a minimum loss of information.ДЇor example, suppose you would like to test the observation that customer satisfaction is based on product knowledge, communications skills and people skills. The procedure works by comparing the variance between group means versus the variance within groups as a way of determining whether the groups are all part of one larger population or separate populations with different characteristics.Exploratory factor analysis is a statistical approach that can be used to analyze interrelationships among a large number of variables and to explain these variables in terms of a smaller number of common underlying dimensions. The name 'analysis of variance' is based on the approach in which the procedure uses variances to determine whether the means are different. For more detailed information about the differences between specific means, use a multiple comparison method such as Tukey's. If the p-value is less than your alpha, then you conclude that at least one durability mean is different.

Because you are examining one factor (carpet type) you use a one-way ANOVA. You put a sample of each carpet type in ten homes and you measure durability after 60 days.

Transformations of the original dataset may correct these violations.ДЇor example, you design an experiment to assess the durability of four experimental carpet products. However, ANOVA procedures work quite well even if the normality assumption has been violated, unless one or more of the distributions are highly skewed or if the variances are quite different. ANOVAs require data from approximately normally distributed populations with equal variances between factor levels. To perform an ANOVA, you must have a continuous response variable and at least one categorical factor with two or more levels. The null hypothesis states that all population means (factor level means) are equal while the alternative hypothesis states that at least one is different. ANOVAs assess the importance of one or more factors by comparing the response variable means at the different factor levels. Analysis of variance (ANOVA) tests the hypothesis that the means of two or more populations are equal.

Ancova minitab 18