My sample size is small (n= 28). I have 2 cohorts who have carried out the same interventions at different times. I have collected pre and post intervention scales for both groups. What I have not done is pair the data, as it was anonymous. This is part of a bigger qualitative study. Any suggestions which test I should use to see if there are any differences in the scale scores pre and post?
19341
The study I am analysing is a pre-and-post intervention questionnaire of students' views before and after studying a module.  The questionnaire was distributed in three different geographical locations, where the same instructor was delivering the same module at different times within one year.  The scale used is a 5-point Likert scale (which I can also convert into a binary agree/disagree variable if needed).   The surveys are anonymous, so I can't match the sample.  However, in some samples, all the students answered both the pre-and-post questionnaires (same $N$) and in others this was different (different $N$).   Overall I have 343 respondents for the pre-intervention questionnaire, and 378 respondents for the post-intervention questionnaire.  However in two of the three locations, I have equal pre and post intervention responses, but they are anonymous: so they are not independent, but not matched. (This is a weakness of the data, and will be reported in the results).  My questions are: a) Given the sample size, would running an unpaired t-test on the full sample be appropriate? Or should I be going for a non-parametric test?  b) Can the subsamples where there are equal respondents (i.e. all students answered both pre-and-post questionnaires) be analysed as non-independent non-matched samples?
20330
What is the difference between a repeated measures ANOVA over some factor (say experimental condition) and a MANOVA? In particular one website I stumbled across suggested that MANOVA does not make the same assumption of sphericity that repeated measures ANOVA does, is that true?     If so, why would one not just always use MANOVA?   I am trying to conduct a repeated measures ANOVA with multiple DVs, what is the appropriate approach?
25340
I am trying to test the impact of a sale initiative on store revenue. I have created 10 matched pairs of stores and then randomly distributed one store in each pair into a test or control group.  I then ran the sale for 21 days within the test group (keeping control constant) and measured relative lift in revenue compared to baseline.  My questions  1.) Should I be using a paired t-test based on the previous store matching  2.) Should I be measuring across each day of the test such as:          Control Test 1/1/2017    4   3 1/2/2017    3   4 1/3/2017    1   3 1/4/2017    2   3 1/5/2017    2   4 1/6/2017    1   7   Or measuring across my paired markets such as:                 Test Control Market Pair 1   10  5 Market Pair 2   8   3 Market Pair 3   5   1
73210
I need help on determining what tests to use in SPSS. Let me provide some background on my research:   I am doing a research on system automation of leave management in a company (which is still using a manual paper form to apply for leave). My hypothesis validates the automation of the different processes (independent variables) on the overall improvement of the leave management process (dependent). My control variables are age, gender and dept. I have developed a prototype of a system and provided a questionnaire in the form of a 5-point Likert scale to the participant. (Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree). The questions measures their response on the statement provided on the test evaluation of the system. These questions are categorised based on the hypothesis.   Where do I start?
48674
Suppose that I have three populations with four, mutually exclusive characteristics.  I take random samples from each population and construct a crosstab or frequency table for the characteristics that I am measuring.  Am I correct in saying that:   If I wanted to test whether there is any relationship between the populations and the characteristics (e.g. whether one population has a higher frequency of one of the characteristics), I should run a chi-squared test and see whether the result is significant. If the chi-squared test is significant, it only shows me that there is some relationship between the populations and characteristics, but not how they are related. Furthermore, not all of the characteristics need to be related to the population.  For example, if the different populations have significantly different distributions of characteristics A and B, but not of C and D, then the chi-squared test may still come back as significant. If I wanted to measure whether or not a specific characteristic is affected by the population, then I can run a test for equal proportions (I have seen this called a z-test, or as prop.test() in R) on just that characteristic.   In other words, is it appropriate to use the prop.test() to more accurately determine the nature of a relationship between two sets of categories when the chi-squared test says that there is a significant relationship?
62920
What statistical test can I use to compare two ratios from two independent samples. The ratios are after to before results. I need to compare the after/before ratios for two independent models and show whether they are have significant difference or not. Please help!
102491
Can the group recommend a good introduction text/resource to applied resampling techniques? Specifically, I am interested in alternatives to classical parametric tests (e.g. t tests, ANOVA, ANCOVA) for comparing groups when assumptions such as normality are clearly violated.   An example problem type I would like to educate myself as to a better way to solve may involve something such as:  I) 2 Groups: Treatment and Control    Dependent Var: Change in account balance dollars after intervention  Covariate: Pre intervention account balance dollars.  Issue with applying ANCOVA: Many subjects will not have any change (many zeros).   II) 2 Groups: Treatment and Control  Dependent Var: new accounts added  Covariate: Pre intervention number of accounts.  *Many subjects will not have any added account (many zeros).   Can I use a bootstrap? A permutation test? This is the type of analysis I would like to apply nonparametric resampling methods to.
61796
I have two independent groups: from one I have drawn a sample of size less than 30 due to its small population, while the second group has sample size 100.  Is it appropriate to use an independent samples t-test in this case?
84118
I have following problem:   Within an independent groups 1-factor design I have two independent groups, with a sample size of 20 each. The data of the treatment group is not normally distributed, whereas the data for the control group is (checked with Shapiro-Wilk Normality Test). Now I want to check if the differences of the means of both groups are significant.  What is the appropriate test for this? I think it should be the Wilcoxon Rank Sum and Signed Rank Test, but I am not sure...  Could please anybody help me?
53680
I have a fundamental question: is it reasonable to use a statistical test to find out if 2 datasets are similar or not? Some comments have called out the T-test as being unable to answer that question. Why is the T-test insufficient? Furthermore, what other tests are there to determine differences in distributions for the two sample problem.
65782
If you want to test whether two variables follow the same distribution, would it be a good test to simply sort both variables, and then check their correlation? If it is high (at least 0.9?), then the variables most likely come from the same distribution.  With distribution here I mean "normal", "chi-square", "gamma" etc.
23095
I have a data set from a customer survey, I want to deploy a statistical test to see whether there is significance difference between product 1 and product 2.  Here is a data set of customers' reviews.   The rate is from very bad,bad,okay,good,to very good.  customer    product1    product2 1           very good   very bad 2           good        bad 3           okay        bad 4           very good   okay 5           bad         very good 6           okay        good 7           bad         okay 8           very good   very bad 9           good        good 10          good        very good 11          okay        okay 12          very good   good 13          good        good 14          very good   okay 15          very good   okay   What methods should I use to see if there is any difference betw these two products?
35279
I'm analyzing reaction time data from a repeated measures ANOVA with the following design: Factor 1 (between-group): GROUP (controls, clinical) Factor 2 (within-group): TASK TYPE (social, non-social) Factor 3 (within-group): DECISION DIFFICULTY (easy, hard)  ANOVA results indicate a significant 2-way interaction (GROUP&times;DIFFICULTY). No other main effects, 2-way interactions, nor the 3-way interaction are significant (all $p &gt; .4)$.  Since there was no effect of task, I pooled data across the 2 task types. I'm not sure what is the right way to go from here in terms of reporting and interpreting the results. My specific questions are:   Should I always perform follow-up tests with a significant 2-way interaction, or should I just stop there and interpret based on the figures (and without further stats)? Between- or within-group post-hoc: Should post-hoc tests be restricted to an independent t-test between groups (i.e. comparing "pooled easy" data between groups, and "pooled hard" data between groups)? Can I also perform and report a within-group paired t-test comparing "pooled easy" to "pooled hard" within each group? Interpretation: If neither independent t-test from (2) is significant, can I still interpret the 2-way interaction based on a significant paired t-test from (2)? (The clinical group's reaction times appear to increase with task difficulty, whereas reaction times don't change with task difficulty in controls.)   In the absence of significant between-group t-tests, can I still report these results based on the 2-way interaction and a within-group t-test as "task difficulty affects reaction times in the clinical group and not the control group"?  Thank you. I appreciate any help you can give!
104861
I have an experiment where I measure the effect of a manipulation on two different groups of participants, and the variable that I manipulate also has two levels. Thus, I can essentially use a 2x2 ANOVA to analyze the results. But it is also possible for me to measure the effect of the different levels of the variable first on Group 1 and then on Group 2, by using paired-samples t-tests.   Is there a rule of thumb to decide what needs to be done in this case? I think one advantage of using an ANOVA could be the option to investigate the interaction between the different groups and the different levels of the variable. Perhaps one could also argue that an omnibus analysis is also a better choice from a stylistic point of view.   But is there a very compelling conceptual reason to prefer a 2x2 ANOVA over two separate pairwise comparisons in my case? And if so, what would it be?
35342
I have question on the methods of performing meta analysis. I have data that consists of multiple Randomized Control Trials (RCT) with different dose levels and observational studies with different dose level as well.  The responses are brain pressure measured as continuous random variable.  So my understanding is that I have to perform meta analysis on the studies that share same level of treatment (same dose) and I can include both RCT and observational studies in that treatment level analysis.  Is there any thing I can do as a whole study? What kind of answer should I provide to my client?
95534
I have a question on selecting the appropriate statistical test as below:  I have a group of people attending a training course, in order to measure whether the training course is effective in improving attendees' knowledge. We did the following:   attendees will complete a pre-test (TRUE or FALSE only) before the training attendees attending the training attendees will complete a post-test (TRUE or FALSE only) after training   Both pre and post tests are having exactly the same questions, the test is tagged with barcode so that we are able to trace which pre and post tests are a pair.  By further breaking down the research question, it can be divided into two parts:  1. whether the training is effective in general  2. whether the training can improve answering of a particular question (item)  For question 1, I think paired t-test will be appropriate: we mark all the test, each attendee will have a pre-test score and a post-test score, we take mean of the pre-test and post-test score respectively, and then go for paired t-test.  For question 2, at first I am thinking of doing a McNemar’s test: as the answer for each question in pre-test and post-test can be matched. I planned to do something like . But then I have received some alternative method which I'm not sure whether it is appropriate or not:   count the number of attendees answered question X correctly in pre-test and divide it with total number of attendees answer question X, so it becomes a percentage do the same thing in post-test, so we have two percentages here perform "paired" t-test on it   When I first saw it, I wonder how I can "pair" each question, as the categorical data is already converted into continuous data. Perhaps the proposer accidentally add the word "pair" and he actually means "t-test" only. But I still wonder why discard the information based on pairing.  A few questions here:   for question 1, is using paired t-test appropriate? for question 2, is McNemar's test appropriate? for question 2, is "t-test" still appropriate? is there any reason why "t-test" is preferred?
59934
Say I fit a multiple regression of p explanatory variables. The t-test will allow me to check if any single one of those is significant ($H_0: \beta_i = 0$). I can do a partial F-test to check if some subset of them is significant ($H_0: \beta_i=\beta_j=...=\beta_k=0$).  What I often see though is that someone gets 5 p-values from 5 t-tests (assuming they had 5 covariates) and only keeps the ones with a p-value &lt; 0.05. That seems a bit incorrect as there really should be a multiple comparison check no? Is it really fair to say something like $\beta_1$ and $\beta_2$ are significant but $\beta_3$, $\beta_4$ and $\beta_5$ are not?   On a related note, say I run 2 regressions on 2 separate models (different outcome). Does there need to be a multiple comparison check for significant parameters between the two outcomes?  Edit: To differentiate from the similar question, is there any other interpretation to the p-values besides: "B_i is (in)significant, when adjusting for all the other covariates"? It doesn't seem that this interpretation allows me to look at every B_i and drop those less than 0.5 (which is similar to the other post).   It seems to me that a sure fire way to test whether B_i and Y have an a relationship would be to get a correlation coefficient p-value  for each covariate and then do a multcomp (although that would definitely lose signal).  Finally, say I computed the correlation between B1/Y1, B2/Y1 and B3/Y1  (thus three p-values). Unrelatedly, I also did a correlation between T1/Y2, T2/Y2, T3/Y2. I'm assuming the correct Bonferroni adjustment would be 6 for all 6 tests together (rather than 3 for the first group and 3 for the second group - and thus getting 2 "semi"-adjusted p-values).