I think I would feel most comfortable with working with a new variable that would create the mode value of a set of Liker-scale responses for each respondent. Do you know of a way that this is possible? Thanks for your comment. In this case, you just report the decimal, and you should not round it up.
I think that you should be able to find the mode there. Thanks so much for this help and advice Achilleas : Any idea of a source I could reference to back-up this advice? Thanks very much indeed. A library near to me has those books, so I shall go fetch them. In the meantime…. Dear Achilleas, It is Jenny here again — we already exchanged some messages recently.
Are you with your books yet and able to confirm your source for this recommendation? Many thanks indeed and sorry to bother you again : Jenny :. Chi-Square Tests Value df Asymp. This is too high and it skews your statistics. I am sorry your advisor is out of reach, but you should really talk to him or her, if they are statistically competent — this looks like it needs lots of help.
I am doing a study with two independent groups — they are seeing different types of an advertisement. I want to know if there is difference between their cognitive responses. For that I have 3 lists for three different concepts of cognitive responses each with 10 questions answered on a 7pt likert scale. I have computed them together into three seperate new variables. Is it ok to do that and then use an independent samples T-test to compare the means of the two groups?
Or should I select the median like you show and then use Wilcoxon mann-whitney to compare the responses to the advertisements between groups? I also measured behavioral intentions with 12 questions also answered on a 7pt Likert scale.
I want to know if I can predict intentions from the advertisement people saw and if the relationship can be explained by the cognitive responses. What statistical test should I use? What statistical test should I use. Dear, i want a clarification from you. I have a 5 Independent variables which are sub-classified in to two,three and four. And i have around 34 dependent variables.
So, i want to analyse the differences among groups by using infrential statistics. So, which test is best to use? Note:My questionnaire were distributed randomly. That would depend on the type of variables, i.
Interesting advice thank you I have one question …during mean calculation for variables, let say it is 3. Ideally, you should have a verbal descriptor to go with each number. Ratio data: Ratio data is similar to interval data. Some of the significant points to keep in mind are: Statistical tests: Researchers sometimes treat ordinal data as interval data because they claim that parametric statistical tests are more powerful than nonparametric alternatives. Moreover, inferences from parametric tests are easy to interpret and provide more information than non-parametric options.
To analyze scalar data more appropriately, researchers prefer to consider ordinal data as interval data and concentrate on Likert scales. Median or range for inspecting data: A universal guideline suggests that the mean and the standard deviation are baseless parameters for detailed statistics when the data are on ordinal scales , just like any parametric analysis based on the normal distribution.
The non-parametric test is done based on the appropriate median or range for inspecting data. Best practices for analyzing the results of Likert scales Because the Likert element data is discrete, ordinal, and limited in scope, there has been a long dispute over the most logical way to analyze Likert data.
The advantages and disadvantages of each type of analysis are generally described as the following: Parametric tests assume a regular and uninterrupted division. Non-parametric tests do not assume a regular or uninterrupted division. However, there are concerns about a lesser ability to detect a difference when one exists.
Over the years, a series of studies that have tried to answer this question. However, they have been inclined to look at a limited number of potential distributions for Likert data, which causes the generalization of the results to suffer. Thanks to increases in computing power, simulation studies can now thoroughly evaluate a wide range of distributions. The researchers identified a diverse set of 14 distributions that are representative of the actual Likert data. The computer program extracted self-sufficient pairs of samples to test all possible combinations of the 14 distributions.
In total, 10, random samples were generated for each of the 98 distribution combinations. The study also evaluated different sample sizes. The results show that the Type I error rates false positive for all pairs of distributions are very close to the target quantities. If an organization uses any of the analysis and results are statistically significant, it does not need to be too worried about a false positive.
The results also show that for most pairs of distributions, the difference between the power of the two tests is trivial. If there is a difference at the population level, any of the analysis is equally likely to detect it. There are some pairs of specific distributions where there is a power difference between the two tests. If an organization performs both tests on the same data and does not agree one is significant, and the other is not , this difference in power affects only a small minority of cases.
In general, the choice between the two analyzes is a loop. If an organization needs to compare two groups of five-point Likert data, the analysis method usually does not matter. Both parametric and non-parametric tests, consistently provide the same security against false negatives and also offer the same protection against false positives.
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