Yes, exactly. Only when interactions come into it. Its not a comment but a question. How can I compute for a sample size when I have 2 groups to choose from? I mean, see I have data for the population size of both male and female households in a particular site however, they are unequal. I need respondents from each group because I am having a comparative analysis.
I was wondering where can I find the formulas for calculating 2-way Anova for non-balanced samples. Very educative discussions. I am working on a research, which entails 2way unequal sample size. I need your help regarding my project. I want to apply one-way anova but my data is not normally distributed and i need mean and standard deviation. Is it ok that if i will continue with one way anova. Please suggest me. For each population,the response variable that you want to measure is not normally distributed,then if the sample size is large enough then there is no need for normality because the 3 sample size and 3 sample standard deviation will be close to 3 population parameters which is required if null hypothesis is true.
I have a question related to unequal sample sizes. I investigate whether it is an advantage to have become blind as a child when it comes to second language acquisition. In total I have N 40 L1 speakers and 40 L2 speakers equal sizes , and each of these two groups have 11 early blind, 9 late blind and 20 sighted participants. What do you suggest? Please sir advice mee. Thanking u. However, for the duration factor is abit special which it has different duration. The setting is fried at C for 4, 5, 6 minutes while and C fried for 1,2 and 3mins.
I log transformed the parasite data and it has a normal distribution and equal variances, I was just wondering if I can use a One-way ANOVA to compare the mean abundance between sexes or would it be safer to just apply a non parametric Mann-Whitney U or Kruskal Wallis Test.
Hope to hear from you. My experiment model have two factors — temperature and different time points. However, it seems that there is no effect from the interaction of two factors and the temperature itself.
My question is that will the result of comparison between two temp at different time points be valid if I perform them using one-way GLM after the no significant finding in the initial 2-way GLM? I hope you can help me. The cell sizes and SDs are as follows:. I realize that the smallest group has the highest variance in both cases.
I hate to transform variables since it makes interpretation so complicated. What other options do I have? I have data of 2 years and and I realized the sample size is not equal for both of the year how can i do data cleaning in stata in this case.. Thanks for the information that you provided here. I have the same issue. I have a caregiver group of 96 and 42 control participants that I compare them on one variables.
I checked for the variance and there were no significant differences in the variance. However, do you know any published book that I can cite? Would a weighted mean account for these differences? The number of samples is also related to the number of interesting components for that group not due to poor sampling. I found the data not normal, so can I just use Kruskall-Wallis test? Is that ok? I have read the comment people asked and the replied you have given.
Hi I was wondering what the full reference is for Keppel I sampled 6 different land use types, replicated 4 land use types 5times and the other two, 4 and 2 due to their limited size for sampling. Now I want to see to significant difference using a parameter between different replications and their means using ANOVA. Please, how do I go about this analysis? It seems you have a lot going on there. Thanks so much Karen, that makes a bit more sense now! Will have a go at graphing them.
Thanks again! Hi Karen, I am in the process of collecting data and plan to use a 2 gender, between subjects x 3 condition, between subjects x 3 time of testing, within subjects ANOVA to analyse my data.
I want to run an a priori power analysis to check how many participants I should have in each cell. I am unsure if I am using Gpower correctly particularly if an effect size of. I wonder if this seems right and if having vastly mismatched cells will matter? Hi Karen, I am hoping you might be able to offer some suggestions regarding two questions I am struggling with for my data analysis. My supervisor has asked whether I can apply a correction factor to account for the difference in group size, however I was under the impression that the Mann-Whiteny already accounts for this?
Any ideas?? Any light at all you can shed on this would be greatly appreciated, I have been struggling for days and have exhausted the textbooks and web pages!!! Thanks in advance! M-W is fine for unequal samples. If you have an outlier or two, that would affect means possibly making them closer than say, the medians but would not affect the nonparametric test.
I say graph them. I am not able to carry this out, perhaps because the sample sizes are different? I am comparing 28 different categories between two groups at 3 different ages. How do I do this? Any help would be appreciated. Is it the 28 categories? This is tricky—unequal sample sizes are definitely a problem with two-way models, but at the same time 7 is a very, very small sample.
Is there any way to get more males instead? Is it okay do to that or is the samplesizes too unequal? The variances in score using two different scales are mostly twice as much for woman than for men, for instance std.
Hello, I m using a multiple regression for my research project. My sample sizes are unequal like students, parents and teachers I want to find the effect of parents and teachers on students.
I have used SPSS software to calculate it, but still want to confirm from you whether you can do muliple regression with unequal sample size. Pls help me as i am confused and stuck in this. I am a business student and i dont have a strong statistic background but im not afraid of learning if there are any articles that can help please let me know. I have three variables. Data will be collected from managers and employees. IV and DV data will be collected from managers and mediator data from employees.
Now the problem is if there are 20 managers and there are employees. I was following baren and kenny approach and Jud and kenny b recomendation to run regresson models to analyze data. Now im looking at other techniques due to unequal sample size. Can i analyze data in anova if there is any artice on this sourt of problem please let me know i appreciate any help i get. I am trying to figure out sample size of an article on socially conscious mutual funds.
The three independent sectors that are looked at are tobacco, alcohol, and gambling. Tobacco has 15 stocks in the industry, alcohol, and gambling Do you know what the number of the sample size would be for this? Would it be 3? Or 1, since they are all exclusive? It could either be the number of stocks or it could be, as you suggested, the number of industries.
So glad I found this site! I conducted three-choice experiments in which females are presented 3 different acoustic stimuli simultaneously. I record which stimulus they choose as well as the time it took them to make the choice latency. My issue is with the latency analysis. I assumed that a one-way ANOVA was a proper test because my independent factor is categorical choice and my dependent factor is continuous latency—time.
One issue I have is that the variance for the group with two individuals is HUGE, mainly because one female took her time to choose that stimulus, whereas another female chose that same stimulus rather quickly. I found no significance across the board, but is it because of that low sample size of group 1? Your choices are to run more subjects or drop that stimulus group. Since none of your groups is very large, running more subjects would be the best, if you can manage it.
I have done an analysis on 3 groups. Group 1 has 24 subjest, group 2 has and group 3 has subjects. To find out which group differed from each other i did pair wise comaprison between group1 and2, group 1 and 3 and group 2 and 3.
The pvalue for group 2 and 3 analysis was less than. My question is: the difference between group 2 with Is it because of very less number of subjects in group 1 the difference was not sigmificant or something else. I want to see the significant difference between these groups based on a parameter in common. Please let me know the best method or tool to analyse. Well it depends on which parameter you want to compare.
The different sample sizes are no problem. Is it compulsory to have no of patients equal in both group for data analysis?? If not then can i exclude a single patient to remove bias at the end of study for analysis to make equal sample in both groups?
My data conforms to normality and my model is significant 0. My factor sample period which is significant to the. I would investigate those variances more. What would be the way to go when downsizing the larger sample groups in terms of randomization? So we have:. The Female subjects are more because in the same study but a different analysis we will do exactly the same comparison, but with an added factor, eg. It sounds logical to randomly select 20 Female Athletes and 20 female Non-Athletes, but should we care if they are In-Pregnancy or not?
Or should we account for that as well? Figure out what percentage of the female athlete population is usually pregnant at any given time, then sample your two samples at the same rate. Decide that the population of interest is non-pregnant female athletes and just use that sample.
Than ks for the information. I would like to ask, what is recommended to use as post hoc when runnin on-way ANOVA with different size samples. I get confused with my data analysis. Im about to study motivation towards grade achievement. The motivation is divided into 2 categories: intrinsic interest and attitude and also extrinsic family, social, teaching style, learning style.
But when I try to run the post hoc test, its comes out like this: Warnings Post hoc tests are not performed for Gred because at least one group has fewer than two cases. I would start with some frequency tables. So in my hypothetical, this might mean picking A ratings out of 25, Your sampling seems fine.
The one thing I would change, though, is eliminate steps Those are still based on the very large pop size. As long as your sampling is truly random, there should theoretically be no difference between the mean of the population and the sample.
I have a question, when running a one way anova with three levels 60, 62, 63 participants in each group and one group not having met the normality assumption although the histogram looks like it satisfies normality but equal variance was met, what kind of post hoc test should I be using? I have 3 subgroups from the main group.
The no. Thank you. I would need a lot more information, and probably to actually see the analysis to figure this one out. Hi, i m doing a studt with six groups , so i have to do anova.
Could you please suggest me what type of post hoc test i can use in my study, because my sample is large. Hi, I was wondering if you can help me to find an answer for my question? I have collected data on smoking status. I want to conduct a t-test to compare these two groups regarding their difference in mean of another variables. Is is doable? I just ignored testing this variable due to very unbalanced sample size. Just be very careful to check the equal variance assumption.
The bigger issue is that 11 is very small, and you may not want to make inferences on the responses from 11 people. Thank you for sharing your knowledge with us. I am trying to compare a treatment and a control group, across 8 different segments of people. My sample sizes for treatment and control groups for each of the 8 segments are not even. So, I am wondering whether these results are reliable at all?
If I want to increase the power, is there any way other than increasing the sample size because I can not?
For instance, is there any other test? Yes, if a test is insignificant and the true effect size is the effect size you measured, then you have insufficient power to detect that effect. Here are pretty much the only -ways to increase power. However, i have a study in which i intend to KruskWallis and i would want to have my results in a table from. Is it order to put the medians or i use p values only?
Hi Keneth, although technically a Kruskal Wallis is not testing medians, it is pretty common to report medians as a descriptive stat, along with the K-W test statistic and p-value. Namste Mam I have some problem in my statistics, I have two sample size one 18 and other 17 when i test normality, from Shapiro test R presenting p values of 17 sample size 0. With the p-Values it is observed that one has normal distribution but next does not present normal distribution.
In this situation which test is suitable, Can i use Wilcox. I have drawn this sample from one community Forest which is divided into two blocks one is unmanaged and other is managed block of CFs. I would first investigate what distributions you do have.
An Ourlier? Hi In my paper, males and females compared through Manova Test. The number of males is 37 and females are Is this difference of numbers affect the results? How can I justify this difference? As you probably assumed: when depression and anxiety increases the n for level of the respective group gets smaller there are few subjects with higher levels of anxiety or depression in the sample. What would you do? It is certainly reasonable to combine those groups, as long as it makes theoretical and logical sense.
Anova sig. How come? The F test always trumps the post-hoc. Please help me with my assignment. The animals are tested individually on a delayed-response task. A raisin is hidden in one of three containers while the animal watcher from its cage window. A shade is then pulled over the window for 1 minute to block the view. After this delay period, the monkey is allowed to respond by tipping over one container.
If its response is correct, the monkey is rewarded with the raisin. The number of trials it takes before the animal makes five 5 consecutive correct responses is recorded.
The researcher used all the available animals from each species which resulted in unequal sample size n. The data is summarized below.
Gravetter, Frederick J. Guide Questions: 1. Formulate the steps in hypothesis testing 10 pts 2. Identify if the problem uses one-tail or two-tail of alpha level? Explain why? I was hoping to use ANCOVA to compare a battery of neuropsychological tests in carriers vs non-carriers, controlling for age, gender and education level and I have three questions about that which I was hoping you could help me with. Firstly, do I have to demean the covariates, before feeding them into the SPSS multivariate general linear model?
Is there a non-parametric alternative that I could use instead? Does it mean that I could interpret the results as if the data were balanced? Thank you very much. I am doing an analysis on the influence of teacher characteristics ex. You do this with a multilevel or mixed model. Do you have any suggestions or comments on whether this is going to provide useful information?
This could be useful, but pay very close attention to those assumptions. A non-parametric test, like Kruskall-Wallis, may be a safer approach. The distributions are normal. I read somewhere that if there is less than a 5-fold difference in standard deviations, the ANOVA should still be robust, even with heterogeneity of variance, but the site did not list any references.
In my case, there is a 1. In what journal was it published? Keppel is a textbook, not a journal article. I am running both t-tests and logistic regression analyses looking at income differences between two groups.
One group has subjects; the other In another comparison, one group has and the other group has , I have run t-tests using the lincom function in stata with unequal variances. While my means change slightly with the smaller samples, the overall patterns persist and statistical significance does not change. I have a reviewer who has asked whether I have applied any corrections to take sample size differences into account. Would you suggest any additional corrections, other than what I have already done?
The reviewer in particular questioned whether I could trust my results that indicated statistical significance, given the very different sizes between the two groups.
Would you agree with this concern? It sounds like you already tried the subset of the larger group, and got the same answer. I have 3 sample groups I wish to compare. If so, how do I calculate the weighted mean? The samples come from 3 different stakeholder groups i. Does make a difference when calculating the weighted mean?
I have completed an independent samples t-test and because equal variances are not assumed, I go with the statistics which SPSS provides for that correction. One solution I have been told is to select a random sample of the bigger group so I would select 71 cases randomly out of the and then run the test so that you have equal groups 71 to 71 to run your t test.
Have you ever heard of this? Is this the most robust way of dealing with the issue of having both unequal variances and unequal n size? I have heard of that just read it in a book again yesterday. Also, past research have said females would generally do better, so with it at 59 and males at 29, should i report a possible confound? It could. This is exactly the situation where the bigger sample of females could cause problems.
Are the results the same within each gender? I did not think this was a problem, above all with this small difference. Do you have any advice? What should I do? Hi Marco, there is no need to do anything, particularly if at least two of those IVs are manipulated. I have to test whether there is a difference between the two groups at baseline before the start of the treatment but also after 3 and 9 months T2, T3. A second study design is to recruit a group of individuals and then split them into groups based on some independent variable.
Again, each individual will be assigned to one group only. This independent variable is sometimes called an attribute independent variable because you are splitting the group based on some attribute that they possess e. Each group is then measured on the same dependent variable having undergone the same task or condition or none at all.
For example, a researcher is interested in determining whether there are differences in leg strength between amateur, semi-professional and professional rugby players. This type of study design is illustrated schematically in the Figure below:.
Every time you conduct a t-test there is a chance that you will make a Type I error. In some repeated measures studies, each repeat occurs under a different experimental condition. There is a qualitative difference among the repeats. No problem here. In others, the amount of time that has passed between repeats is important. Or equivalently, the amount of space if the repeats are say, along a transect. In other words, you want to treat the within-subjects effect of time as a continuous, quantitative variable.
This is theoretically valid and reasonable, but repeated measures ANOVA can only account for categorical repeats. What to use instead : A marginal or mixed model can treat time as a truly continuous effect.
In some studies, the important predictor variables are measured on each repeat, right along with the response. What to use instead : A marginal or mixed model can incorporate time-varying covariates. For example, you may have students measured over time, but students are also clustered within classrooms. Streams may be measured over time, but are also clustered into watersheds.
In all these cases, the repeated measures ANOVA can account for the repeats over time, but not the clustering. What to use instead: A mixed model can incorporate multiple levels. There is a repeated measures design that occurs in specific experimental studies. These are studies in which each subject is repeated measured across many trials. Each trial contains one item, and there are multiple items for each condition. An example may be to measure reaction time of 50 participants to each of 20 high-frequency and 20 low frequency words.
After all, some participants will always be faster than others. But each word also has 50 repeated measurements one per participant and those are also likely to be correlated to each other. Some words will elicit faster times than others, even within the same condition. What to use instead : A mixed model with crossed random effects. And if the outcome variable is continuous, unbounded, and measured on an interval or ratio scale, you may be able to solve non-normality with a transformation.
Luckily, there are other options. Does this test check for random effects in your data set? Thank you, Sophia. Could someone help me, because I do not know what I did wrong?
I have a design where participants view images repeatedly, and the images have 3 levels. I have a continuous predictor i.
Can a mixed model LME be appropriate for this type of design? Thank you. It sounds like it, but I would need to know a lot more detail before I could give you accurate advice about the analysis to take for any given study. I would be thankful if you could provide me a quick feedback regarding the best analysis for my situation.
I have one sample which went through physical activity program. We measured participants at baseline, 10 and week follow up no control condition.
Our outcomes are a few tests on a continuos scale time, repetitions. It really depends on why people are dropping out and how much you can assume randomness of dropout.
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