Translating SPSS Syntax into R with the BRAVE(R) Framework

Author

Laurence EFrank

Published

September 17, 2026

Before you start

Asking an AI tool to translate SPSS syntax can be tricky if you accept the output uncritically. The R code will usually look right and run without error, but might use a different default than SPSS (a different variance assumption, a different skewness formula, a different way of handling missing data). The numbers then disagree, and you have no idea why.

So in this exercise AI is a translator you supervise:

  1. Give the AI a precise, well-scoped prompt using the BRAVE(R) framework.
  2. Read the translation and identify where R’s defaults differ from SPSS.
  3. Run the code on data you control and verify the results make sense.

Data safety reminder. Never paste real, unpublished, or participant data into a GenAI tool. This exercise uses simulated data you generate yourself, so nothing sensitive ever leaves your machine.

Learning objectives

  • Write a BRAVE(R) prompt that asks AI to translate SPSS syntax into R.
  • Recognise common SPSS versus R default mismatches and check for them.
  • Run and verify an AI-produced translation against simulated data.

Set up your R environment

You only need to do one of the two options below.

Option A: You already have RStudio installed

  1. Open RStudio.
  2. Create a new R Markdown file: File ▸ New File ▸ R Markdown…, then keep the HTML output and click OK.
  3. To run a code chunk, click the small green ▶ (“Run Current Chunk”) at the top-right of the chunk, or place your cursor in it and press Ctrl+Shift+Enter (Windows/Linux) / Cmd+Shift+Enter (Mac).
  4. To produce the full HTML document, click Knit at the top of the editor.

Option B: You do not have RStudio: use Posit Cloud (in your browser)

Posit Cloud runs RStudio in the browser — nothing to install.

  1. Go to https://posit.cloud and click Sign Up (the free plan is enough).
  2. After logging in, click New Project ▸ New RStudio Project. Wait a few seconds for the workspace to load. You now have the full RStudio interface.
  3. Open an R Markwon file: in the Files pane (bottom-left) click File, choose New File, then New R Markdown File.
  4. Run chunks with the green ▶ button and Knit exactly as in Option A.

Tip. On the free Posit Cloud plan, projects have limited monthly hours. Close the project tab when you are done so you don’t burn through your time.

The SPSS syntax you will translate

This is the original analysis, written in SPSS syntax. Your job is not to run it in SPSS, but to obtain and then critically check an R translation of it.

* Descriptive statistics for key variables.
DESCRIPTIVES VARIABLES=score anxiety depression wellbeing
  /STATISTICS=MEAN STDDEV MIN MAX KURTOSIS SKEWNESS.

* Independent samples t-test (group: 1=control, 2=treatment).
T-TEST GROUPS=group(1 2)
  /MISSING=ANALYSIS
  /VARIABLES=score
  /CRITERIA=CI(.95).

* One-way ANOVA with post-hoc tests.
ONEWAY score BY group
  /STATISTICS DESCRIPTIVES HOMOGENEITY
  /MISSING ANALYSIS
  /POSTHOC=TUKEY ALPHA(0.05).

* Pearson correlations with pairwise deletion.
CORRELATIONS
  /VARIABLES=score anxiety depression wellbeing
  /PRINT=TWOTAIL SIG
  /MISSING=PAIRWISE.

Step 1 Create the simulated data

This gives you a dataset with exactly the variables the SPSS syntax expects: group (coded 1 = control, 2 = treatment, just like SPSS), plus score, anxiety, depression, and wellbeing. A few values are set to missing on purpose, so the /MISSING subcommands actually matter.

Copy past the entire R code chunck into your R Markdown file.

set.seed(2025)

n <- 60                          # respondents per group
group <- rep(c(1, 2), each = n)  # SPSS-style numeric codes: 1 = control, 2 = treatment
treat <- group - 1               # 0 = control, 1 = treatment (helper for the simulation)

# Correlated outcomes with a small treatment effect baked in.
anxiety    <- rnorm(2 * n, mean = 50 - 5 * treat, sd = 10)
depression <- 0.6 * anxiety + rnorm(2 * n, mean = 10, sd = 8)
wellbeing  <- 60 + 6 * treat - 0.3 * anxiety + rnorm(2 * n, mean = 0, sd = 8)
score      <- 100 + 8 * treat - 0.2 * depression + rnorm(2 * n, mean = 0, sd = 15)

dat <- data.frame(group, score, anxiety, depression, wellbeing)

# Inject a few missing values so /MISSING handling becomes visible.
set.seed(99)
dat$anxiety[sample(nrow(dat), 5)]   <- NA
dat$wellbeing[sample(nrow(dat), 4)] <- NA

# Quick look
str(dat)
'data.frame':   120 obs. of  5 variables:
 $ group     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ score     : num  113.2 83.1 88.3 83.8 84.3 ...
 $ anxiety   : num  56.2 50.4 57.7 62.7 53.7 ...
 $ depression: num  38.6 33.3 41.2 36.5 44.2 ...
 $ wellbeing : num  44 41.6 54.7 39.1 37.2 ...
colSums(is.na(dat))   # how many missing per variable?
     group      score    anxiety depression  wellbeing 
         0          0          5          0          4

Note on group coding. SPSS happily uses the numeric codes 1/2. In R you will almost always want group as a factor with readable labels — this is itself part of a faithful translation, and the AI may or may not do it for you.

Step 2 Ask AI to translate, using BRAVE(R)

Write a single, well-scoped prompt. The bracketed letters show the BRAVE(R) component each part covers. Please do not use the BRAVER code in your prompt and leave out (Expectations), (Role), etc.

Please translate the SPSS syntax below into equivalent R code (Expectations). Act as a statistician fluent in both SPSS and R (Role), writing for a social-science researcher who knows SPSS well but is new to R (Audience). My data frame is called dat and contains group (1 = control, 2 = treatment), score, anxiety, depression, and wellbeing (Variables). For each SPSS command, give the R equivalent, name the package you use, and add a one-line comment wherever R’s default differs from SPSS. For example variance assumptions in the t-test, the skewness/kurtosis formula, p-value adjustment in correlations, and missing-data handling (Variables). Only produce code I can run and check myself and do not fabricate any results (Boundaries). > > > [paste the entire SPSS syntax here] >

Refine

After you read the answer, push on the weak spots. This could be a Refine step:

The SPSS T-TEST output shows both an “equal variances assumed” and an “equal variances not assumed” row. Which one does your t.test() call give by default, and how do I get the other? (Refine)

For the correlations, does your code adjust the p-values for multiple comparisons? SPSS does not. Please make sure the R version matches SPSS. (Refine)

Step 3 Human oversight: the mismatches to check

Before you trust the translation, walk this checklist. These are the points where a SPSS to R translation most often goes wrong:

SPSS behaviour R default to watch for What to verify
Skewness / kurtosis use the G1/G2 (SAS/SPSS) formulas psych::describe() uses a different formula unless you set type = 2 Did the translation set type = 2?
T-TEST reports both Student and Welch, plus Levene’s test t.test() gives Welch only (var.equal = FALSE) Is the variance assumption stated? Is Levene’s test included?
ONEWAY HOMOGENEITY = Levene’s test, shown automatically R needs car::leveneTest() explicitly Is homogeneity actually tested, not skipped?
CORRELATIONS print raw two-tailed p-values (no correction) psych::corr.test() adjusts p-values (Holm) unless adjust = "none" Are the p-values uncorrected, like SPSS?
/MISSING=PAIRWISE vs ANALYSIS R functions differ: cor() needs use = "pairwise.complete.obs" Is the deletion method correct per command?
group is numeric 1/2 Many R model functions expect a factor Was group converted to a labelled factor?

The oversight habit. “It ran without error” is not “it matches SPSS.” For each command, ask: would this give the same numbers as SPSS, and if not, why?

Step 4 Run and verify

Paste the AI’s translation into the chunk below, run it, and check it against the checklist above and the model answer in the Appendix. Fix anything that doesn’t match SPSS’s behaviour.

# >>> YOUR TASK: paste the AI's R translation here, then run and verify it <<<

A couple of quick independent checks you can always do yourself:

# With only TWO groups, a one-way ANOVA F should equal the (Student) t squared.
# This is a verification that your t-test and ANOVA translations agree.
t_out <- t.test(score ~ group, data = dat, var.equal = TRUE)
aov_out <- summary(aov(score ~ factor(group), data = dat))
t_out$statistic^2        # t^2
aov_out[[1]]$`F value`   # ANOVA F  -> should match t^2

Step 5 Reflection

  1. Which checklist item, if missed, would most change your conclusions — and why?
  2. Where did the AI’s first translation differ from what SPSS actually does?
  3. How did the Refine step improve the translation compared with accepting the first answer?

BRAVE(R) framework: Fox & Boelen, GSLS GenAI Teacher Tutorial, Chapter 3, CC BY-SA-NC 4.0.