For chi-square post-hoc, use a simple comparison of two independent proportions with a z test.

You wouldn't necessarily report means with a chi-square analysis, since your interest is in comparing frequencies, but that's not to say you wouldn't do some sort of basic descriptive statistics comparison (means, medians, std dev, etc.).Get your data ready for stromanbieter vergleich ohne bonus analysis by coding the responses.Each position is one survey question, and the scale uses the following responses: Strongly agree, agree, neutral, disagree, strongly disagree.Sample question presented using a five-point Likert item.Begin analyzing your Likert scale data with descriptive statistics.In terms of the other data characteristics, there was very little difference among the scale formats in terms of variation about the mean, skewness or kurtosis.Hence, Likert scales are often called summative scales.These can be applied only when more than 5 Likert questions are tation needed.The chi square test is one approach for analyzing the data in this way.

Data from Likert scales are sometimes reduced to the nominal level by combining all agree and disagree responses into two categories of "accept" and "reject".For example, in a set of items A,B,C rated with a Likert scale circular relations like A B, B C and C A can appear.I would analyze the data both ways - with chi-square and with anova, and see how it turns out - if the outcomes are the same, you're all set.The Likert scale is commonly used in survey research.Strongly agree, likert scaling is a bipolar scaling method, measuring either positive or negative response to a statement.This is the source of much confusion; it is better, therefore, to reserve the term Likert scale to apply to the summated scale, and Likert item to refer to an individual item.Overall, you can treat the scales as interval and run methods that compare means, such as anova.Simplify your survey data further by combining the four response categories (e.g., strongly agree, agree, disagree, strongly disagree) into two nominal categories, such as agree/disagree, accept/reject, etc.).When treated as ordinal data, Likert responses can be collated into bar charts, central tendency summarised by the median or the mode (but tipps und tricks für novoline automaten some would say not the mean dispersion summarised by the range across quartiles (but some would say not the standard deviation.But this can only be the case if the intervals between the scale points correspond to empirical observations in a metric sense.If the data are ordinal, we can say that one score is higher than another.There are many approaches available, and the best one depends on the nature of your study and the questions you are trying to answer.