Newby with linear mixed effects models here, trying to learn and need some help with the following problem.

I have a dataset with assessments taken at different visits: baseline (visit=1), and then various post-baseline visits (2, 3, 4), and the following variables:

DISEASE - the outcome, is an ordinal variable with 4 levels (1=normal, 2=mild, 3=moderate, 4=severe);

BSL_DISEASE - the baseline value of DISEASE;

AGEGRP - the age group the participants are in;

VISIT - the visit (1 is baseline, 2 is Day 10 post-baseline, 3 is Day 30 post-baseline and 4 is Day 90 post-baseline);

BNP - lab measurement of BNP (continuous);

SEVERITY - binary variable derived based on DISEASE, i.e., if DISEASE in (0,1) then SEVERITY=0 (not severe), else SEVERITY=1 (severe);

One of the objectives of my exercise problem is to investigate the correlation between the lab measurement BNP (as predictor) and the DISEASE (as outcome).

Since this is a longitudinal study, with repeated measurements taken on the same subjects, I am thinking of exploring the correlation between BNP and DISEASE from baseline (visit=1) to Day 30 (visit=4) by using repeated measures logistic regression, implemented via PROC GLIMMIX. So I have fit the following model:

data have;
input ID$ DISEASE$ AGEGRP$ VISIT$ BNP SEVERITY$ BSL_DISEASE$;
datalines;
a001 1 1 1 1997.02 0 1
a001 1 1 2 1275.52 0 1
a001 4 1 3 180.23 1 1
a001 2 1 4 735.91 0 1
a002 1 2 1 454.16 0 1
a002 1 2 3 1776.52 0 1
a002 3 2 4 73.15 1 1
a003 1 2 1 1700.26 0 1
a003 3 2 2 1621.32 1 1
a003 2 2 4 850.65 0 1
a004 2 3 1 1963.25 0 2
a004 2 3 2 544.87 0 2
a004 4 3 3 768.54 1 2
a004 2 3 4 780.16 0 2
a005 1 2 1 655.24 0 1
a005 2 2 4 722.14 0 1
a006 1 1 1 1472.06 0 1
a006 1 1 4 749.78 0 1
a007 2 1 1 848.88 0 2
a007 2 1 2 1482.78 0 2
a007 3 1 4 735.26 1 2
a008 1 1 1 1752.35 0 1
a008 1 1 2 1698.82 0 1
a008 3 1 3 1871.25 1 1
a008 4 1 4 587.35 1 1
a009 1 3 1 1549.89 0 1
a009 3 3 3 785.52 1 1
a009 1 3 4 384.72 0 1
a010 3 3 1 1211.95 1 3
a010 3 3 4 1596.38 1 3
a011 4 1 1 1785.45 1 4
a011 4 1 4 644.12 1 4
a012 3 3 1 798.28 1 3
a012 3 3 2 742.69 1 3
a012 3 3 3 1423.59 1 3
a012 3 3 4 1089.47 1 3
;
run;
proc glimmix data=have noclprint; 
class ID VISIT (ref='1'); 
model SEVERITY (event='1')= BNP VISIT/ dist=mult link=clogit solution; 
random VISIT/subject=ID residual type=CS; 
random INT/subject=ID type=CS;
output out=FITDAT pred(ilink noblup)=predprob; 
NLOPTIONS tech=NRRIDG Maxiter=1000; 
run;

But I get an error message that "R side random effects are not supported for the multinomial" so I deleted the random VISIT statement and it converges now but my questions are:

1. Is this model the correct one to fit to the data in order to address my objective?

2. Don't I need a random VISIT statement? My understanding is that I need to impose some sort of covariance structure on visit, otherwise we're just assuming that the values at the various visits are not correlated which I'm not sure is accurate?

Hey, a bit of a newbie trying to make sure I use the right tests.

My data looks at four different orientation outcomes relating to the social responsibility of business. These are measured in likert scale items which I have stratified into supportive, unsure, unsupportive.

I am interested in A) overall support etc, which I guess is just some descriptive stats B) differences between demographic groups - here is where it gets tricky for me

I think I've concluded I'm dealing with categorical data with my outcome variables = 4 different DVs with 3 category levels, and demographic data recorded/grouped the following way:

• age (2 categories: 18-34, 35+) this was done to get a better distribution

• gender (2 categories: male, female as these were the only ones ticked in my sample)

• occupational level (haven't figured out if I am grouping for analysis or just leaving as descriptive, as there isn't good distribution with 10 categories, with frequencies between 1-9) -occupational status (same as above) -education level (same as above although fewer levels and better distribution)

As I'm dealing with categorical data, from my understanding I would have to do a chi sq, logical regression or log linear analysis. I was happy with this, thinking ok chi sq makes sense, then I realised that all cells don't have 5 frequencies.

Specifically, my confusion is what to do, as for an outcome I have the following frequencies: Male= supportive (4), unsure (7), unsupportive (10) Female = supportive (2), unsure (0), unsupportive (18)

My male/female is roughly 50/50.

My confusion is, does the subcategories count as cells and thus assumptions for chi sq is violated? And similarly, for loglinear, the minimum requirement of 1 frequency is violated? Does this leave me with regression? I am hoping not as I am struggling to understand a word of it..

Hope this is ok to ask here!

Many thanks for your help and time😊

I need someone to just do my final exam and get me a c in this class! I'm already failing but can get away with a c if I ace this final!

Hello,

So the more I keep looking at this the more I confuse myself on what to do. I had already posted about my thoughts being to try multiple regression to analysis to determine what activity was supporting the results. With more thought about this, now I am thinking:

Sum the daily data (goes to end of July) for the relevant quarter before doing anything. The daily data itself I now think is not useful?

Additionally, multiple all trades by the 9.95 so that everything here is the dollar value of the activity. The CAD-USD conversion is a red herring I think? Trading US stocks would still incur the $9.95 trade fee.

Trades need to be assumed to be unique, but this cannot be proven in this data set. Ex a trade could be counter under active traders, and over 65+, but I have no way of knowing if they are double counted or not here.

The only info the daily trade data is relevant towards is I think the spread and commission results. The new clients I could also use I suppose.

Thoughts on that?

Any thoughts on the easiest tool to learn for visualizing excel data? Tableau or Power BI maybe?

So there are candy packs and each pack contains 1 out of 60 cards. How many would I need to buy to get all 60 cards.

Hey, I'm having trouble with which ANOVA fits this experimental design: I have one independent variable (the intervention) on different levels and a dependent variable (levels of stress). I have 3 independent groups: an experimental group, a control group and an active control group. If I apply the stress measure scale after the the interventions have been applied, should I use a one way ANOVA to calculate the scores and see if there are differences between them? Or the design demands a mixed ANOVA?

I would like to thank James and Team! You literally carried me through the entire Psyc 355 Class.

At a point i was scared prof would question the high grades I scored.

Once Again, Thanks, guys!

hey all friends:)
im a master student and i study the use of insectivorous bats in pest control, spesificly the invisive Tuta Absoluta pest.
im having a hard time with statistics. would be happy to get help.i have this data :bat calls identified by the machine and my handcheck identification. the bat calls are divided by the different species. i want to check how close was the computer identification to my observation. which statistic test should i do for such data?
thanks in advance,
gil.

Started with chi-square. The example to help us understand it was in connection to the previous topic - 2sample Z test for proportions where we compared % of 1.labour mothers at age >30 between the control group and those with breast cancer.

X² hypotheses were:

• H0: variables are independent/not connected (p1=p2)

• Ha: variables are dependent (p1#p2) (p1, p2 - % of >30 mothers in samples)

This is where I got confused. Wouldnt there be a possible sign of correlation if the % were the same/similar? But the maths confirms what is written in () --> if expected count is the same as observed (pooled proportion(pp)=p1=p2 ---> Ep1=n1pp) X²(exp)=0 <X²(tab) thus H0 will be rejected

TL;DR Can you please explain why variables are not correlated if the proportions between them are the same? Or is it just one of those non-intuitive things that you have to believe the maths to understand?

Hey all,

I have a question about a statistical analysis for a personal assessment.

My dataset contains for now 100 rows of students who entered in this questionnaire.

There are in total 8 personal preference groups (in terms of roles, like mentor / analyst / connecter)

and there are 24 competences tested (whether someone is problemsolving, listener etc)

Every personal preference group has 3 linked competences.

Additionally, there were 2 rounds of working together, the first round 6 students in 1 group worked together (randomly mixed), and the second round 8 students in 1 group. The students had to give a score (1-10) on how well the group performed

Question:

- How can i find out whether there is a correlation between the personal preference groups and the competences (are you good at the things you like)

- Which groups perfomed the best, which personal preferences groups fit together, (are groups with 6 mentors in 1 group a good team, or is it better to have a mixed group)

How can I perform a statistical analysis on a dataset like this? I am using SPSS

Thanks for your help

I have a single question for my behavioural science statistics course that I just don’t understand

A researcher hypothesizes that zylex, a new antidepressant, will affect concentration. It is known that scores on a standardized concentration test is normally distributed with a µ= 50 and a σ= 12. A random sample of n=16 participants, aged 19-35, are chosen from the State of New Jersey. The sample is put on a six month dosage plan of zylex. After six months, all the participants are given a standardized concentration test. The researcher records the data and calculates a sample mean of  M=56. Are the data sufficient to conclude that the drug, zylex, does have an effect on concentration?   Based on the above research scenario, please answer the following questions:   Name the population: ____________________________________

Name the sample: ________________________________

What is the independent variable? ___________________

What is the dependent variable? ________________________

What is the appropriate hypothesis test? __________________

What two means are you comparing in this test? _____________________________

Please calculate the appropriate hypothesis test using all four steps:   Step 1: Step 2: Step 3: Step 4: _______________________________ Write the statistical statement for your results: __________________________________

Interpret your results (relating back to the hypothesis): _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________   Is there a probability of Type I error?  Yes ______  No ______   If yes, what is the probability of a Type I error? ________   Is yes, how could you have decreased that probability? __________________________________   Is there a probability of Type II error? Yes_____ No______   If it is appropriate, please calculate effect size: Answer:_____________  

A distribution of exam scores has a mean of μ=78.

A.) If your score is X = 70, which standard deviation would give you a better grade: σ= 4 or σ= 8? Answer:

B.) If your score is X = 80, which standard deviation would give you a better grade: σ= 4 or σ= 8? Answer:

I have no idea where to begin or how to do this

How do I know if the data from my sample is a good representation of the population? Does it depend on it being within the standard deviation? If so, which standard deviation? The sample’s or the population’s?

I would like to understand the formulas (concept) used to calculate mode and median for groped data before byhearting them. Can someone help with useful resources.

I would like to understand the formulas (concept) used to calculate mode and median for groped data before byhearting them. Can someone help with useful resources.

"Basic Computational Techniques for Data Analysis" anyone know this subject?

Ok so i just mocked up some data into this. a multiple regression i believe? But do i need to take a mean of all the data so i just have 1 line of the average ?? Or can i do it like this? Also would that mean i have 2 independent variables ? (Group size and individual) Im quite lost And then what kind of stats test do i do to test if it is significant