• JASP version: 0.11.1
• OS name and version: Windows 10
• Analysis: Repeated measure (and ANCOVA)
• Bug description: Different results from R (base--ez package) and JASP
• Expected behavior: I imagine both results should be similar

Hello, I'm running two different analyses in JASP and R. The first analysis is an ANCOVA and the second one is a repeated-measures ANOVA. As always, people want to match the results across software and that drives my question. In the place where I work, R (ez package) is the default software and people will remain with its results.

When I compare the first analysis (ANCOVA), the results look great. They are similar between R (base) and JASP.

This makes me feel comfortable to ask the target-question.

I changed the format from my dataset to long and then I ran the RM ANOVA. Unfortunately, the output was not parallel. Just bolding some results, the main effects are different.
From ez package,
latino_group is 252
From JASP,
209
Condition from ez package is 144, but it is 172 from JASP.

Please check the image below:

Any comments are valuable.
Thank you,

The entire code is this one:
`#ancova
aov_outcome <- lm(M_4I2TOT ~ M_4I1TOT + factor(condition) * factor(latino_group), data = temp)
car::Anova(aov_outcome, type=3)
#results are ok

repeated measures (long format)

temp %>%
select(ID, latino_group,condition, M_4I1TOT,M_4I2TOT) %>%
pivot_longer(cols = M_4I1TOT:M_4I2TOT, #variables that have results
names_to = "time",
values_to = "results") -> temp_long

library(ez)
options(contrasts = c("contr.sum","contr.poly"))
ez_outcome <- ezANOVA(
data = temp_long,
dv = results,
wid = ID,
within = time,
between = .(latino_group, condition),
type = 3,
detailed = TRUE,
return_aov = TRUE)
summary(ez_outcome$aov)`

The dput of the dataset is below:
dput(temp) structure(list(ID = structure(c(10002, 10006, 10009, 10010, 10017, 10018, 10023, 10025, 10026, 10031, 10035, 10036, 10042, 10043, 10047, 10048, 10049, 10061, 10065, 10072, 10077, 10081, 10082, 10083, 10085, 10086, 10087, 10089, 10090, 10094, 10100, 10104, 10105, 10106, 10110, 10111, 10112, 10114, 10116, 10117, 10121, 10122, 10144, 10147, 10148, 10150, 10153, 10155, 10164, 10198, 10201, 10204, 10206, 10223, 10224, 10226, 10234, 10239, 10241, 10242, 10243, 10248, 20002, 20003, 20004, 20012, 20021, 20026, 20028, 20047, 20048, 20049, 20081, 20093, 20106, 20107, 20114, 20127, 20129, 20133, 20160, 20165, 20169, 20197, 20202, 20205, 20206, 20216, 20217, 20219, 20220, 20221, 20224, 20225, 20229, 20230, 20231, 20236, 20243, 20284, 20285, 20292, 20296), label = "ID", format.spss = "F5.0"), latino_group = c("white", "white", "white", "white", "white", "white", "latino", "latino", "white", "latino", "white", "latino", "white", "white", "latino", "white", "latino", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "latino", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "latino", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "latino", "white", "latino", "latino", "latino", "white", "white", "latino", "latino", "latino", "latino", "white", "latino", "latino", "latino", "latino", "latino", "latino", "latino", "latino", "latino", "latino", "latino", "latino", "white", "white", "latino", "latino", "white", "latino", "white"), condition = c("attention-control", "attention-control", "experimental", "attention-control", "experimental", "experimental", "attention-control", "attention-control", "attention-control", "attention-control", "experimental", "experimental", "experimental", "experimental", "attention-control", "attention-control", "experimental", "experimental", "attention-control", "experimental", "attention-control", "attention-control", "attention-control", "experimental", "attention-control", "attention-control", "experimental", "experimental", "experimental", "experimental", "attention-control", "experimental", "attention-control", "attention-control", "experimental", "attention-control", "experimental", "experimental", "experimental", "attention-control", "attention-control", "attention-control", "experimental", "experimental", "attention-control", "attention-control", "experimental", "attention-control", "experimental", "attention-control", "experimental", "attention-control", "experimental", "experimental", "attention-control", "attention-control", "experimental", "attention-control", "experimental", "experimental", "attention-control", "experimental", "experimental", "attention-control", "attention-control", "attention-control", "experimental", "experimental", "experimental", "experimental", "experimental", "attention-control", "experimental", "experimental", "attention-control", "experimental", "experimental", "attention-control", "experimental", "attention-control", "experimental", "attention-control", "attention-control", "experimental", "attention-control", "attention-control", "attention-control", "experimental", "attention-control", "experimental", "experimental", "attention-control", "experimental", "attention-control", "experimental", "attention-control", "experimental", "experimental", "experimental", "attention-control", "attention-control", "experimental", "experimental"), M_4I1TOT = c(7, 3, 8, 12, 7, 9, 3, 4, 8, 3, 5, 1, 12, 8, 2, 6, 10, 7, 7, 8, 9, 5, 12, 7, 9, 8, 8, 7, 13, 10, 7, 5, 4, 11, 10, 4, 4, 7, 7, 7, 3, 8, 10, 3, 7, 9, 8, 11, 7, 3, 4, 12, 8, 8, 7, 13, 10, 7, 13, 7, 8, 5, 10, 12, 5, 5, 2, 5, 9, 3, 6, 8, 7, 7, 1, 4, 7, 5, 8, 4, 3, 4, 4, 5, 6, 7, 2, 7, 6, 9, 2, 5, 5, 3, 6, 1, 10, 5, 10, 7, 3, 3, 5), M_4I2TOT = c(9, 2, 13, 8, 7, 12, 3, 1, 9, 6, 9, 7, 8, 8.2078195323884, 3, 5, 12, 10, 6, 6, 9, 7, 12, 8, 6, 5, 9, 6, 13, 13, 11, 13, 5, 9.17142301161172, 12, 8, 4, 13, 13, 6, 11, 9, 13, 3, 6, 10, 13, 8, 11, 5, 4, 10.2470110225081, 12, 10, 9, 10, 13, 13, 13, 12, 5, 9, 11, 11, 8, 4, 6.76864538709267, 13, 13, 11, 11, 5, 11, 8.06448852972179, 5, 9, 7, 6, 12, 4, 10, 5, 5, 2, 4, 8, 2, 10, 7, 11, 4, 8, 10, 2, 9, 2, 8, 7, 10, 7, 5, 12, 10)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -103L))

The attached file is the CSV file of my dataset.

temp.zip