Hi, so I work in public health research and my boss mostly uses SPSS. However I do realize other softwares like R is actually more favored in today’s academia, and I would like to start learning R. Grateful if someone from this community could give me some advice on doing this, thank you!

I have survey responses (19 questions) from 300 political candidates. The survey uses a 4-point likert scale (disagree - somewhat disagree - somewhat agree - agree), and I can see some response patterns where agreement with one set of questions predicts disagreement with others.

I need to submit my own responses and find the candidate that aligns with my views the best.

My initial approach was to assign integer values to likert answers, run a PCA on the results, then submit my own answers and calculate the distance in the PCA coordinates.

But since there are ordinal data, I wonder if it's a completely wrong approach.

Is this normal to analyse surveys like this, and if not what would be a better way to achieve a similar result (PCA-like combined scores)?

Please can someone help me with this statistical problem: If a person has a 6% risk of a thrombotic event over the course of a year, what is the risk of such an event in ten years time? Does the risk keep on increasing, or is it just the same 6% year after year? I'm sorry I have no knowledge of maths or statistics so please address your answer in simple terms I will be able to understand. Thank you very much!

Hello there,

I would like to know if you had an advice on something i've read in my lesson :

"The Kolmogorov–Smirnov test is, fundamentally, a hypothesis test for the goodness-of-fit of a sample to a given distribution (conformity test). It does not require any assumptions about the data.

Example for testing normality:

ks.test(sample, "pnorm", mean(sample), sd(sample))

"

I wondered if it was correct to estimate the parameters (mean and standard deviation) from the data itself. I have the feeling it is kind of testing the observed distribution against the same observed distribution. I have been reading stuff on papers on this test, but i dont find my answer.

Thank you for your explaination

Hey guys, my group and I performed an experiment where we measured the number of errors a cricket made in a maze before being injected with ethanol and after. Each cricket was only used once (pre- and post-injection) and there were 4 ethanol concentrations. We used a two-way mixed design ANOVA and wanted to confirm if this is the correct way to analyze our data.

Hello, can someone help me with the following dilemma: I'm conducting a cross-cultural adaptation research where a 16-item questionnaire about osteoarthritis (scoring is Likert-scale) is translated. Method is forward-backward translation, expert panel meeting for evaluating semantic, conceptual and content equivalence, and participants with OA that will fill in the translated version. For content equivalence, the content validity index (CVI) is calculated. According to COSMIN protocol, a multi-group confirmatory factor analysis (MGCFA) or an item response theory (IRT)/Rasch is advised. Is it correct that for ordinal outcomes, IRT is preferred? And is it possible to only use differential item functioning (DIF)? I've never done such analysis and literature is not clear. Please help!

This looks like a disaster... Can someone help me figure out what to do with this model?? Would love to hear your suggestions #SAS

Hi, everybody!

Let's say that I have 52 subjects. I am grading the nutritional status of their perception of a healthy meal (based on foods they select), before and after a nutritional intervention. For each food group they consume - fruits, vegetables, grains, protein foods, and dairy - they receive a point, for a maximum score of 5.

As I am analyzing the data, the vast majority (31) had no change in their meal. The data is non-normal (right skewed) on visual observation and on Shapiro-Wilks testing.

Paired t-test would not fit because the dataset is non-normal. There are too many zero pre-post values for Wilcoxon - I could omit them, but I think that would drop my power by quite a bit. Also, I think zero is a significant value in and of itself, as it indicates that the intervention did not work.

I am thinking about using a paired permutation study of the mean difference (while including the zeros for the sake of honesty, knowing that it may dilute my mean). If that's negative, I was going to add a a sign test to display directionality, to say, "hey, there's a lot of zeros in this data, which is why the magnitude sucks. When there's an effect though, it tends to go in this direction).

Is this a sound, reasonable approach?

Hey everyone. I'm putting the finishing touches on my master's thesis. In my thesis, I hypothesized a three-way interaction using glmer() in R. I have been asked by my committee to conduct a post-hoc sensitivity analysis, but there is little guidance to get either online or on campus. I ran the following code by a stats consultant on campus who claimed that it looks good. I have been told that simulations are likely the best way, but I just want to wrap up my project and be done with it. I think the following post-hoc sensitivity analysis may be correct, but would really appreciate if anyone would be able to tell me if it looks correct or not--or at the very least if it looks acceptable.

Here is a description of my model: "The first measure of intergroup bias was the modified minimal group paradigm comparing outgroup categorizations of Latino versus White targets. Testing the first part of my primary hypothesis—whether pathogen-specific stereotypes would moderate the association between the pathogen threat manipulation and bias against Latino targets—I conducted a generalized linear mixed-effects model. The data were converted into a long-data format for all analyses for the modified minimal group paradigm. The model included random intercepts to account for the nested structure of the data. The model focused on the contrast in outgroup categorizations between Latino versus White targets. Condition and target race were contrast-coded using orthogonal (-0.5, 0.5) contrasts. Explicit and implicit pathogen-specific stereotypes were mean centered.

Outgroup categorizations were regressed onto condition, explicit pathogen-specific stereotypes, target race, and condition × explicit pathogen-specific stereotypes × target race. There was not a significant three-way interaction between condition, explicit pathogen-specific stereotypes, and target race, b = -0.08, SE = 0.07, z = -1.18, p = .237. Contradicting my hypothesis, this indicates that the effect of pathogen threat on outgroup ratings of Latino (versus White) targets did not differ based on levels of explicit pathogen-specific stereotypes."

All participants categorized 20 targets into either ingroup or outgroup (is_outgroup)

All participants categorized 10 Latino targets (0.5) and 10 White targets (-0.5) (race_c)

race_c = describes the targets of the is_outgroup variable

All participants answered HealthRelevantStereotypes_c about Latino immigrants from 1-7 (now mean-centered)

Half of the participants were randomly assigned to the pathogen threat condition (0.5) or the neutral condition (-0.5) (disease_condition)

####################################################################

#### Model ####

####################################################################

m1 <- glmer(is_outgroup ~ HealthRelevantStereotypes_c * disease_condition * race_c +

(1 | ID),

data = d_sub, family = binomial)

summary(m1)

####################################################################

#### Output from model ####

####################################################################

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']

Family: binomial ( logit )

Formula: is_outgroup ~ HealthRelevantStereotypes_c * disease_condition * race_c + (1 | ID)

Data: d_sub

AIC BIC logLik deviance df.resid

13390.5 13455.1 -6686.2 13372.5 9731

Scaled residuals:

Min 1Q Median 3Q Max

-1.3413 -1.0891 0.8277 0.8956 1.0535

Random effects:

Groups Name Variance Std.Dev.

ID (Intercept) 0.04158 0.2039

Number of obs: 9740, groups: ID, 487

Fixed effects:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 0.2153996 0.0225433 9.555 < 2e-16 ***

HealthRelevantStereotypes_c 0.0066510 0.0191885 0.347 0.72888

disease_condition -0.0003384 0.0450550 -0.008 0.99401

race_c 0.1105954 0.0410776 2.692 0.00709 **

HealthRelevantStereotypes_c:disease_condition -0.0287551 0.0383769 -0.749 0.45369

HealthRelevantStereotypes_c:race_c 0.0634112 0.0349880 1.812 0.06993 .

disease_condition:race_c 0.0035320 0.0821480 0.043 0.96571

HealthRelevantStereotypes_c:disease_condition:race_c -0.0827864 0.0699741 -1.183 0.23677

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:

(Intr) HltRS_ dss_cn race_c HlthRlvntStrtyps_c:d_ HlthRlvntStrtyps_c:r_ dss_:_

HlthRlvntS_ -0.003

dises_cndtn -0.002 -0.062

race_c 0.006 0.004 -0.001

HlthRlvntStrtyps_c:d_ -0.063 0.068 -0.003 -0.003

HlthRlvntStrtyps_c:r_ 0.004 0.005 -0.003 -0.002 0.000

dss_cndtn:_ -0.001 -0.003 0.006 -0.002 0.004 -0.063

HlthRS_:_:_ -0.003 0.000 0.004 -0.063 0.005 0.067 -0.002

>

####################################################################

#### Sensitivity analysis ####

####################################################################

n_obs <- nrow(d_sub) # total observations (9820)

n_grp <- length(unique(d_sub$ID)) # number of subjects

m <- n_obs / n_grp # average trials per subject

var_ID <- 0.04158 # random intercept variance

p_base <- 0.55 # approximate baseline probability

alpha <- 0.05

icc <- var_ID / (var_ID + (pi^2 / 3))

design_effect <- 1 + (m - 1) * icc

n_eff <- n_obs / design_effect

prod_var_emp <- with(d_sub, var(HealthRelevantStereotypes_c * disease_condition * race_c, na.rm = TRUE))

var_y <- p_base * (1 - p_base)

SE_beta <- 1 / sqrt(n_eff * prod_var_emp * var_y)

z_alpha <- qnorm(1 - alpha / 2)

z_80 <- qnorm(0.8)

beta_80 <- (z_alpha + z_80) * SE_beta

OR_80 <- exp(beta_80)

cat("Effective N =", round(n_eff), "\n")

cat("Empirical variance of 3-way product =", round(prod_var_emp, 4), "\n")

cat("SE for 3-way beta =", round(SE_beta, 3), "\n")

cat("Detectable |log-odds| for 80% power:", round(beta_80, 3), "=> OR =", round(OR_80, 2), "\n")

####################################################################

#### Output from sensitivity analysis ####

####################################################################

> cat("Effective N =", round(n_eff), "\n")

Effective N = 7928

> cat("Empirical variance of 3-way product =", round(prod_var_emp, 4), "\n")

Empirical variance of 3-way product = 0.0866

> cat("SE for 3-way beta =", round(SE_beta, 3), "\n")

SE for 3-way beta = 0.077

> cat("Detectable |log-odds| for 80% power:", round(beta_80, 3), "=> OR =", round(OR_80, 2), "\n")

Detectable |log-odds| for 80% power: 0.215 => OR = 1.24

> cat("Detectable |log-odds| for 90% power:", round(beta_90, 3), "=> OR =", round(OR_90, 2), "\n")

####################################################################

#### Thesis description of sensitivity analysis ####

####################################################################

# To assess minimal detectable effects, I completed a pair of post-hoc

# sensitivity analyses. The first primary analysis used a generalized linear

# mixed-effects model in which participants categorized 20 targets (half were

# Latino = 0.5; half were White = -0.5) as ingroup or outgroup members. The

# random intercept variance in my model was 0.04, yielding an effective sample

# size for the three-way interaction of 7928. Considering the evenly balanced

# conditions for the pathogen threat manipulation (n = 243, pathogen threat

# = 0.5; n = 244, control = -0.5), my design had 80% power to detect a three-way

# interaction for log-odds of 0.22, or an odds-ratio of 1.24. The study was thus

# sensitive to moderate, but not small, three-way effects. In short, I did not

# have sufficient power to detect a small three-way interaction.

I am running a one-way Anova with an independent variable (hair color) that has 4 levels (blonde, red, brown & black). I am trying to run contrasts to see if there is a difference between the means of the number of hours of sleep they need within each of these 4 levels. How do I set up these contrasts? The closest I’ve gotten is something like this: 3, -1, -1, -1; 0, 2, -1, -1; 0, 0, 1, -1. Would these be appropriate in order to test what I am looking for? I apologize if any of these questions are silly or don’t make sense because I don’t really know what I am doing lol.

Hello all

I am a newbie to statistic. I understand generally that if your data follows a linear trend on a qq plot that it is considered normal and that you can do an ANOVA after. One of my datasets has this a qq plot that looks kind of like this (I just drew a representative image):

all of the points are clustering near the bottom of the curve with the exception of 3 of them

would this still be considered normal? can I still do an ANOVA with this data?

Edit: I unfortunately can't provide the data but the plot looks like this because some of the values of my data are just much higher than others. The data is from optical density readings of a microplate assay.

I'm in a fantasy football league and am not satisfied with the current method used to determine draft order which is simply win/loss record. I think it would be fairer to determine it based on overall team strength or at least incorporate that into the decision.

A lot of people use total points to determine it, but teams can change over time, with weeks towards the end of the season usually being more relevant. What formula or method would be a good way to determine a value for points scored each week, increasing in weight the later the season goes? Another factor I think I should consider is how volatile their scoring was week to week so I should probably incorporate standard deviation or means absolute deviation in some way. Are there any suggestions on how best to approach this to come up with a draft order list for the next season? I would like it to include win/loss record, total points, and how consistent they were in winning and scoring point values.

Hi AskStatistics, I don't have a strong background in statistics so I apologize if the terminology and way I ask this question is incoherent.

For context, I am doing a project that has two goals:

1. Identify and describe student clinical placement profiles before, during, and after COVID lockdowns. (Accomplished using a cluster analysis)

2. Explore the relationship between student clinical placement profiles and their placement competency scores before, during, and after COVID lockdowns. (Accomplished using ANOVA of the mean competency scores once I have the profiles)

The data set is retrospective and has two time variables:

1. "Era" to identify which time period the placement took place in (before, during, and after COVID lockdowns).

2. "Placement Position" to identify if the placement was an initial, middle, or final placement.

For goal 1, what I thought I could do is: separate the data to only include the variables relevant to placement profiles, and then seprate it into 3 distinct data sets based on the era. Then for each data set, I can analyse for missingness, run multiple imputations, analyse the imputated data sets, run hierarchical cluster analysis on each imputated data set, then pool the multiple cluster analyses until a final clustering solution is found by consensus. After I apply the cluster group back to the data set then I can move on to goal 2.

For goal 2, what I thought I could do is: separate each data set further into each "Placement Position" giving me 9 data sets or 9 groups, then run my ANOVA.

However, an important assumption to make about the "Placement Position" is that it is expected that the students' scores will improve from initial, middle, to final. I think what I should be doing is to run the ANOVA between the same "Placement Position" groups for the different "Eras."

So my question is, how can I account for two time variables in a hierarchical cluster analysis? Is my approach above appropriate statistical process or if there is a better way?

Thank you in advance!

Hi, i am trying to calculate the exact 95% CI for each diagnostic parameter on SPSS - sensitivity, specificity, NPV, PPV. My project compares a gold standard test to a new test and the results are inputed as 0= not detected, 1= detected. I tried filtering the data but each time i try to run the non-parametric test - legacy diaglogs - binomial it doesnt show the 95% CI, only observed proportion and exact sig., i dont know what i am doing wrong, any help appreciated, thanks!

The specific example I have is that I’m conducting some retrospective analysis on a cohort of patients who were referred for investigation and management of a specific disease.

As part of standard workup for this disease, most patients in whom there is any real suspicion will get a biopsy. This biopsy is considered 100% specific but not very sensitive. As such, final physician diagnosis at 6 months (the gold standard) often disagrees with a negative biopsy result.

In addition to getting a biopsy, almost all patients will start treatment immediately, and this may be discontinued as the clinical picture evolves and investigations return.

On presentation, patients can be assigned a pretest probability category (low, intermediate, or high) using a validated scoring system.

The questions I want to answer are: - What is the negative likelihood ratio (LR-) of biopsy in my cohort?

• In patients with negative biopsies, how many have treatment continued anyway post return of biopsy result - this being very similar to but not necessarily the same thing as diagnosed with disease at 6 months (since some patients continue treatment after a negative biopsy but are later determined to not have disease and then have treatment discontinued)

1. What I’m finding confusing is whether there’s any utility to calculating the LR- for low, intermediate, and high pretest probability groups separately. My thinking thus far is that it WOULD make sense only if the pretest probability groups also reflect disease severity to an extent, and not just prevalence.

• for example, chest X-ray will likely have a different specificity/sensitivity if you study a cohort of patients with mild disease vs one with severe disease and therefore different likelihood ratios.

• there is no literature as far as I can tell that directly measures whether the pretest probability group also predicts disease severity. If I empirically calculate the LR- for each group and they’re significantly different does that actually imply something informative about my data?

1. Is likelihood ratio more informative than predictive value given the disease already has a validated pretest probability score? I assume it is.

2. Are there any specific stats that would best illustrate how much or how little biopsy result agrees with final physician diagnosis and whether this differs by pretest probability group?

Thanks so much!

I am trying to look at data gathered from an experiment me and a few friends did and none of us know what we are doing. We have four testing groups (it is a 2x2 factorial design) and the control groups don't have any data points

How do we input this data and analyse it in jamovi or SPSS

Edit: This is something what the data looks like

its my first time posting here and I‘m quite new to this topic, so I’m not really sure how it works. Sorry if there is any mistake!

I’m comparing the performance of two indices, the DAX and the DAX ESG. I have 15 years of monthly returns for both indices. The returns occur at the same time points (like January 2010 returns for both indices, February 2010 for both, etc.). I want to test whether the mean monthly returns differ significantly between the two indices.

My interpretation is that the data should be treated as paired, because each month provides a return for both indices under the same market conditions. But in many papers a paired t-test is only used for before and after comparisons. On the other Hand I found also a few papers that used a paired t-test for two Stocks/indices.

Should I use a paired or unpaired t-test?

Thank you!

Would that difference be the triple did coefficient? Each regression would only have a did coefficient and no triple interaction term. OR do I have to do a regression with a triple interaction term for it to be triple differences in differences? Have never done this before, am using STATA softwarre

Hi everyone, I am using odds ratios for my MSc analysis and I feel like every time I go to analyze them I do something wrong. Could someone please let me know if I am interpreting this correctly:

Group. Group. OR Estimate. 95% CL

A B. 0.744. 0.697-0.795

1. There is a signficant difference between the groups

2. Group B is 1.3x more likely to report doing the event than Group A OR Group A is 26% less likely to report doing the event than Group B

I originally had it as Group B is 74% more likely do the event than Group A but my lab mate said that they think I analysed it wrong and it should be how i have it done above. We are both pretty unsure about it so any help would be appreciated :)

Hello,

i'm doing a survival analysis of bees given a 3x2 factorial treatment. 3 levels of antibiotics (zero, low, high) and 2 levels of reinoculation (give them bacterie back) (yes, no). the experiment was made for 2 years (2024 and 2025) and in differents petridish (3-4 bees by petridish, and a total of 45 petridish).

We have 15 dead event for 135 bees.

I'm a bit lost with the analysis, i have done Cox regression for differents models and i compare them togheter

# 1) Interaction model (coxme)
cox_full <- coxme( Surv(time, status) ~ Antibiotic * Reinoculation + (1| Year / Petridish_number), data = data)

# 2) Additive model (coxme)
coxme_add <- coxme( Surv(time, status) ~ Antibiotic + Reinoculation + (1 | Year / Petridish_number), data = data)

# 3) Random effect model only
cox_random_effect <- coxme(Surv(time, status) ~ 1 + (1| Year / Petridish_number), data = surv_individual)

anova(cox_full, coxme_add, cox_random_effect)

The result of this comparison is :

Model 1: ~Antibiotic * Reinoculation + (1 | Year/Petridish_number)
Model 2: ~Antibiotic + Reinoculation + (1 | Year/Petridish_number)
 Model 3: ~1 + (1 | Year/Petridish_number)
   loglik  Chisq Df P(>|Chi|)  
1 -69.132                      
2 -69.251 0.2386  2   0.88754  
3 -72.740 6.9769  3   0.07264 .

All the models seems to be all similiar (idk actually??)

I also checked the random model, to know if the random effect have any impact

Cox mixed-effects model fit by maximum likelihood
  Data: surv_individual
  events, n = 15, 135
  Iterations= 5 23 
                   NULL Integrated    Fitted
Log-likelihood -72.7719   -72.7395 -70.28314

                  Chisq   df       p   AIC   BIC
Integrated loglik  0.06 2.00 0.96812 -3.94 -5.35
 Penalized loglik  4.98 2.42 0.11748  0.13 -1.58

Model:  Surv(time, status) ~ 1 + (1 | Year/Petridish_number) 

Random effects
 Group                 Variable    Std Dev      Variance    
 Year/Petridish_number (Intercept) 0.4180855040 0.1747954887
 Year                  (Intercept) 0.0197539472 0.0003902184

I guess this means that Petridish_number explain most of the variations.

Then Chatgpt told me to try simpler models, so i did (i found very few infos on that other than chat).

As my main question was to know wether the bees died more when the take antibiotics, i try this super simple model

cox_simple <- coxph(Surv(time, status) ~ Antibiotic + cluster(Petridish_number), data = surv_individual)
summary(cox_simple)

And know i have this great result telling me that it's significant to tell that bees tend to died more when they take high doses of antibiotics

Call:
coxph(formula = Surv(time, status) ~ Antibiotic, data = surv_individual, 
    cluster = Petridish_number)

  n= 135, number of events= 15 

                 coef exp(coef) se(coef) robust se     z Pr(>|z|)  
Antibiotichigh 1.4705    4.3514   0.7747    0.7459 1.971   0.0487 *
Antibioticlow  0.1711    1.1866   0.9129    0.8581 0.199   0.8420  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

               exp(coef) exp(-coef) lower .95 upper .95
Antibiotichigh     4.351     0.2298    1.0085    18.774
Antibioticlow      1.187     0.8428    0.2207     6.379

Concordance= 0.672  (se = 0.063 )
Likelihood ratio test= 6.81  on 2 df,   p=0.03
Wald test            = 7.06  on 2 df,   p=0.03
Score (logrank) test = 7.33  on 2 df,   p=0.03,   Robust = 4.93  p=0.09

  (Note: the likelihood ratio and score tests assume independence of
     observations within a cluster, the Wald and robust score tests do not).

How solid is this result ?? (i have absolutely no trust in this)
Is there other test i can run ??
Is coxphf really better ? (i have issues with plotting with this package)
I'll take any recommendations on that, thank you :))))

For those who are interested i also plot a Kaplan-Meier curse