Video Database for Emotional Psychological Experiments

1) Because I am a novice when it comes to reporting the results of a linear mixed models analysis, how do I report the fixed effect, including including the estimate, confidence interval, and p-value in addition to the size of the random effects. I am not sure how to report these in writing. For example, how do I report the confidence interval in APA format and how do I report the size of the random effects?

2) How do you determine the significance of the size of the random effects (i.e. how do you determine if the size of the random effects is too large and how do you determine the implications of that size)?

3) Our study consisted of 16 participants, 8 of which were assigned a technology with a privacy setting and 8 of which were not assigned a technology with a privacy setting. Survey data was collected weekly. Our fixed effect was whether or not participants were assigned the technology. Our random effects were week (for the 8-week study) and participant. How do I justify using a linear mixed model for this study design? Is it accurate to say that we used a linear mixed model to account for missing data (i.e. non-response; technology issues) and participant-level effects (i.e. how frequently each participant used the technology; differences in technology experience; high variability in each individual participant's responses to survey questions across the 8-week period). Is this a sufficient justification? 

I am very new to mixed models analyses, and I would appreciate some guidance. 

Hello,

I am currently writing my dissertation and have a few questions regarding reporting my results. I am not very confident in statistics, so please bear with me if this doesn't make any sense.

I am using a two-sample Wilcoxon test for my statistical analysis which I am doing through R. I am struggling with how to report the results as am only given a W and p-value. However, I have seen that most people report their results using an additional U and Z value. Is it ok to report using only the W and p value?

Additionally, I have read conflicting advice on whether to report the mean or median values! Could someone please help? My data doesn't follow a similar distribution pattern, so I am unsure if reporting medians would be viable. However, I have been told that I shouldn't report means and standard deviations on non-parametric tests. I am a little confused about what the best thing is to do.

Please can someone help!

Dear all,

Is there a way to convert partial eta^2 to cohen's d for repeated measures designs? I ask because I am conducting a meta-analysis and need to convert the studies' results into a common effect size.

I read that it can be calculated in 2 steps: first cohen's f and then cohen's d.

(1) cohen's f can be calculated from partial eta^2 as follows:

cohen's f = sqrt(partialeta^2/1-partialeta^2)

(2) cohen's f can be converted to cohen's d as follows:

cohen's d = f*2

When I try this with an example from a paper in which there was a partial eta^2 of .42, I get the following:

f=.85

d=1.7

However, I also have the pre and post means and SDs, and when I calculate the cohen's drm according to Lakens (2013), I get very different values. (Note that I do not have the r-value so I will assume one of .5). Mpre = .92, SDpre = .09, Mpost = .98, SDpost = .02.

Cohen's drm = (Mdiff/sqrt(SDpre^2 + SDpost^2 - 2 * r * SDpre * SDpost)) * sqrt(2(1-r))

drm = .73.

This is a huge difference. Even when changing the r-values, this effect size does not get close to the d-value of 1.7 estimated from the partial eta^2 value.

As such, I presume that the calculation I used from partial eta^2 to cohen's d is incorrect for a repeated measures design, and that a correction needs to be applied at step 1 or 2 (or both). I have not been able to find information about this so far. Does anyone know how to do this conversion for repeated measures designs properly?

Thank you in advance for your help.

I have a simple control group vs experimental group study, I want to report effect sizes of ANOVA (comparing levels of anxiety before a surgical intervention between the two groups), same sample size for the two groups. Is it better to use eta squared or cohen's d?

Thank you

I have some continuous variables from my experiment with 6 different treatments, and I would like to ANOVA to compare the means. But the data are not normally distributed even after data transformation. I have tried log, square root, and Box-Cox transformations, and they did not improve the homoscedasticity of variance. Are there any other data transformation to normalize the data? Can I still use ANOVA for non-normally distributed data, or I should use non-parametric analysis?

Dear Colleagues

It happens that while submitting a paper to any journal a preprint option is given. Preprint is not considered as published data but still, it can attract new citations. my question is:

Is this preprint a negative thing?

If your paper is published, will it be considered a self-plagiarized document?

If you submit this paper consequent to rejection by a few journals, will it count in your similarity index?

Dear research community, last year I published a conference paper. The proceedings are behind a paywall, on a private webpage and it appears that this means that it doesn't show up on Google.

So, I thought I'd log it on ResearchGate with the purpose of having the title and abstract show up when people search for it online. I even attached the PDF (but privately to respect copyright). I figured that services like Google Scholar would regularly do a webscrape of sites like ResearchGate and include all the research papers in their search.

This was several weeks ago but my paper still isn't Google-able.

Has anyone else had this problem? Does anyone have a solution for me? Thanks!

Say that your department has just hired a smart, inspired, assistant professor. This person has a great education, is thoroughly knowledgeable about theory and methodology, and is genuinely enthusiastic about working for the department. What is the most effective way to destroy this person's inspiration and long-term (post-tenure) research productivity?

I argue that the most effective way is to:

1. Try to manage the person through transactional means. That is, emphasize the carrots and sticks related to their job. An example would be making it clear that if they don't perform to some (often arbitrary) standard, they will be fired. (The Stick.) In contrast, if they do perform at or above the standard, they will have lifetime job security, almost regardless of future performance. (The Carrot.) Also, be sure to conduct annual performance appraisals emphasizing their short-term productivity.

2. Make their evaluations contingent on publishing in journals on some list. This will direct their focus from topics that they love and maximizes their reading auidience to topics that are likely to get published in journals on the list and addresses a narrower audience. It also reinforces the transactional nature of the management system.

3. Introduce a large dose of politics into evaluations. One way is to have people who are negligibly qualified evaluate their work. For instance, have someone outside their field conduct annual evaluation. Staff T&P committees with people who cannot read and understand their work, and weight the opinion of these committees more strongly than those of, say, the external letters of experts in the field who have a deep knowledge of the person's publication. Give substantial weight to variables like citizenship and collegiality which can cover up personal biases and other factors irrelevant to productivity.

That's my list. What's yours?

I am trying to conduct an SEM analysis using Mplus7 with planned missing data. But I got this error: one or more variables in the data set have no non-missing values. Check your data and format statement. How can I fix this?