I am not sure what sort of KS ad Anderson-Darling post-hoc tests you are referring to. These tests are used to test normality and are not typically used as post-hoc tests. Again, which post-hoc tests to use depends on the null hypothese you want to test.
Null hypothesis: The variation between the control sample and the intervention sample are equal or very similar. The population statistics for the speech acoustics is unknown. No question that the next step requires a larger N especially since this will have to satisfy the medical community. Repeated Measures: At each exam the intervention subject speaks seven words that are repeated two additional times while each time the words are randomized.
If we limit the null hypothesis to the initial exam, the repeated measures analysis would not be necessary. Since the Anderson-Darling uses a weighted approach, I thought it would be of value. We are not seeking to conform the statistical data analysis to any particular type of distribution, rather to understand how it behaves, the magnitude of any differences and explain it accordingly… this thinking intersects directly with your statement about simplicity and alignment with hypothesis testing.
Lastly, we seek to compare the two samples control and intervention to determine if any variation occurs and the magnitude. We have the following additional data collected from intervention group subjects: date of injury; injury cause; injury bodily location; gender; age; and examination dates However, we believe that none of these affect the speech acoustic variation; therefore, we do not seek to analyze in the pilot.
We have all data in a database. We would rather program mathematical equation scripts as opposed to using a software package. Is the measurement some decimal number or a value such as 0, 1, 2, 3, 4, 5 where 0 is the worst and 5 the best? In this case, you probably can use a simple t test or Mann-Whitney test if the normality assumption is not met to compare the control and concussed groups.
Matthew, Yes, in this case you would need Hotelling T-squared test to make a comparison. If the data is not normally distributed and the variances are unequal, do I still use the Hotelling T Squared test? Thank you very much for your time and expertise. We are a two-person Indiana start-up now receiving some assistance from a Indiana healthcare foundation. There is a version of the test that does correct for violations of homogeneity of variances.
Additionally I do not see how the multiple independent variables, in my case 7, and the dependent variables, in my case 5, are inputted into the model.
Lastly, how do I create the covariance table for my 7 independent and 5 dependent variables? The seven independent variables, from a speech acoustic perspective, have no correlation nor interactions.
Each IV is considered from an individual perspective. Correspondingly, the dependent measurements have no correlation nor interactions either…same as IV. Also, both gender and age are aspects that need to be taken into consideration pilot study age range yrs.
Thus one needs to group them and compare to those of the same age range control group. Does the addition of gender and age preclude the Hotelling T2 test? This is done to have more data points thus not relying on one data point. One of the seven words IV is a made-up word, it is of some interest to determine if the made-up word performs based on the five measurement types differently than the other six actual words. I think this would be a separate statistical analysis, is this correct?
How should this be performed statistically? I realize this is outside the scope of the Ho but could lead to changing the made-up word to an actual word going forward. Matthew, What you are describing seems quite complicated, but it is not clear to me which are the essential things that you are trying to study.
Once you clearly state these hypotheses using precise terms based on measurable data , it will be easier to determine which tests are required. You are correct. It is easy to get lost in the forest.
Since this is an experimental study i. We are simply seeking to establish if there is variation and how much is the variation. Since the first examination answers this question, the subsequent examinations would only further establish that the variation diminishes and ultimately returns to a normal variation same as the control group.
We are seeking to establish that speech acoustics can reliably diagnose a concussion, thereby providing the basis for another study with a larger number of subjects.
Please suggest which other statistical test can i apply. I have collected data to understand saving habits of people. Each question contains several options. I want to understand if there is any significant difference between demographic groups like age, income etc regarding the choices of answers. Which statistical test do you suggest? Komal, It depends on the details. Will I get a notification if someone replied??? Hi all, Could you tell me how many dependant variables this tool can handle?
If you send me an Excel file with your data and the analysis that you have run, then I will try to figure out why you are getting these error values. You can find my email address at Contact Us.
A, B, C. For each vowel I have continuous values from about 5 different measurements e. In other words, do the words in context B look different from the words in context C in a statistically significant way , given the values of measurements 1,2,3,4,5?
I would need a lot more information about what sort of hypotheses you are trying to test before I would be able to answer your question. I am trying to figure out which analysis to use. I have 1 DV dichotomous ,3 IVs test scores, which are all ratio , and three covariates gender, actual age, and past offenses-categorical.
Please help. To use ANCOVA you would have 3 categorical independent variables which could be dichotomous and one dependent variable which would be numeric and not dichotomous. Please describe the situation better. Do I get it right or not?
Maria, Yes, assertion 1 is correct. Generally, you should use a two tailed test. Only when you are certain that one of the tails cannot occur, should you use a one tailed test. See Null and Alternative Hypothesis for more details. Your work with Real Statistics seems to me excellent, monumental and very useful. Could it be acceptable to procced this way or it does not have any meaning at all? Maria, If these are the values reported, then you need to make sure that a one-tailed test is appropriate and you only need to detect such a very large effect.
It is not likely that another test which is suitable will require a sample as small as This is not uncommon when working with real-world data. However, even when your data fails certain assumptions, there is often a solution to overcome this.
In practice, checking for these nine assumptions adds some more time to your analysis, requiring you to work through additional procedures in SPSS Statistics when performing your analysis, as well as thinking a little bit more about your data. These nine assumptions are presented below:. Before doing this, you should make sure that your data meets assumptions 1, 2, 3 and 4, although you don't need SPSS Statistics to do this.
Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running a one-way MANOVA might not be valid. You can find out about our enhanced content as a whole on our Features: Overview page, or more specifically, learn how we help with testing assumptions on our Features: Assumptions page.
The pupils at a high school come from three different primary schools. The head teacher wanted to know whether there were academic differences between the pupils from the three different primary schools. As such, she randomly selected 20 pupils from School A, 20 pupils from School B and 20 pupils from School C, and measured their academic performance as assessed by the marks they received for their end-of-year English and Maths exams. Therefore, the two dependent variables were "English score" and "Maths score", whilst the independent variable was "School", which consisted of three categories: "School A", "School B" and "School C".
This latter variable is required to test whether there are any multivariate outliers i. We do not include it in the test procedure in the next section because we do not show you how to test for the assumptions of the one-way MANOVA in this "quick start" guide.
You can learn about our enhanced data setup content on our Features: Data Setup. At the end of these steps, we show you how to interpret the results from this test. However, the procedure is identical. Note: You can select other post hoc tests depending on your data and study design. These nine assumptions are presented below: Assumption 1: Your two or more dependent variables should be measured at the interval or ratio level i. Examples of variables that meet this criterion include revision time measured in hours , intelligence measured using IQ score , exam performance measured from 0 to , weight measured in kg , and so forth.
At the end of treatment, each subject participates in a structured interview, during which the clinical psychologist makes three ratings: physiological, emotional and cognitive. The clinical psychologist wants to know which type of treatment most reduces the symptoms of the panic disorder as measured on the physiological, emotional and cognitive scales. This example was adapted from Grimm and Yarnold, , page We have a data file, manova. The response variables are ratings called useful , difficulty and importance.
Level 1 of the group variable is the treatment group, level 2 is control group 1 and level 3 is control group 2. Below is a list of some analysis methods you may have encountered. Some of the methods listed are quite reasonable, while others have either fallen out of favor or have limitations.
We will start by running the manova command. After the categorical predictor variable group , we need to specify the minimum and maximum values of that variable in parentheses.
We will begin by comparing the treatment group group 1 to an average of the control groups groups 2 and 3. This tests the hypothesis that the mean of the control groups equals the treatment group. We will also compare control group 1 group 2 to control group 2 group 3. The first hypothesis is given on the second line of the contrast subcommand, and the second hypothesis is given on the third line of the contrast subcommand.
We can use the pmeans subcommand to obtain adjusted predicted values for each of the groups. In the first table below, we get the predicted means for the dependent variable difficulty. In the next two tables, we get the predicted means for the dependent variables useful and importance. These values can be helpful in seeing where differences between levels of the predictor variable are and describing the model.
In each of the three tables above, we see that the predicted means for groups 2 and 3 are very similar; the predicted mean for group 1 is higher than those for groups 2 and 3. In the example below, we obtain the differences in the means for each of the dependent variables for each of the control groups groups 2 and 3 compared to the treatment group group 1.
With respect to the dependent variable difficulty , the difference between the means for control group 1 versus the treatment group is approximately The difference between the means for control group 2 versus the treatment group is approximately Click here to report an error on this page or leave a comment. Your Name required.
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