This folder contains the analysis scripts and the results of the study "Should we stop thinking about inhibition? Searching for individual and age differences in inhibition as a psychometric construct" from Rey-Mermet, Gade, and Oberauer. The analysis scripts were programmed with R within RStudio. These files are given in the folder "R scripts". The data after merging and trimming are presented in the zip file called "datasets". This folder contains: - a subfolder called `aggregate` in which the data were aggregated across the different data trimming procedures and data transformations we used. The data consist of the difference scores. - a subfolder called `aggregate_acrosstrialtypes` in which the data were aggregated across the different data trimming procedures and data transformations we used. The data consist of the performance on the different trial types (e.g., incongruent, neutral and congruent). - a subfolder called `plots` in which the data distributions are plotted. We used 6 different data transformations (abbreviations used for the file or folder names are given in parentheses): - log / arcsin (`logarcsin`): This consisted of a logarithm transformation of the reaction times and an arcsine square root transformation of the error rates. - log / arcsin - shortened (`logarcsin_half`): Only half of the blocks were included in the analyses (because according to some accounts, measures of executive functions, such as inhibition, are most valid when they are novel; e.g., Friedman & Miyake, 2004; Rabbitt, 1997). - proportional (`prop`): These scores were computed as the RT differences between interference and baseline trials divided by the baseline trials (i.e., for the Interference effect I: (incongruent trials RTs - congruent trials RTs) / congruent trials RTs; for the Interference effect II: (incongruent trials RTs - neutral trials RTs) / neutral trials RTs; for the Facilitation effect: (neutral trials RTs - congruent trials RTs) / neutral trials RTs; see the negative compatibility task for an exception). - residuals (`res`): Interference trials were regressed on baseline trials, and residuals were used as measures of inhibition (i.e., for the Interference effect I: congruent trials RTs were regressed on incongruent trials RTs; for the Interference effect II: neutral trials RTs were regressed on incongruent trials RTs; for the Facilitation effect: neutral trials RTs were regressed on congruent trials RTs; see the negative compatibility task for an exception). - rate residuals (`rate`): These scores - a combination of error rates and reaction times - were computed following the procedure proposed by Hughes, Linck, Bowles, Koeth, and Bunting (2014). That is, the four experimental blocks were divided into eight subsets of trials. Within each subset, the rate of correct responses per second was calculated for the interference and baseline trials by dividing the number of correct responses per trial type (interference vs. baseline) by the time taken to make all of the responses, whether accurate or inaccurate (summing the RTs for that trial type). For each subset, the interference trial rates were regressed on baseline trials rates. Then, the residuals were averaged across subsets to find each participant’s final rate residual score - raw data (`raw`): No data transformation was applied; only the RTs differences were computed (i.e., for the Interference effect I: incongruent trials RTs - congruent trials RTs; for the Interference effect II: incongruent trials RTs - neutral trials RTs; for the Facilitation effect: neutral trials RTs - congruent trials RTs; see the negative compatibility task for an exception). We used 4 different data trimming procedures (abbreviations used for the file or folder names are given in parentheses): - removing data with 2.5 SD as criterion (`sd`): For each participant and each RT-based task, if the mean accuracy of a block was smaller than 2.5 Standard Deviations (SD) than the corresponding mean accuracy averaged across all participants, the block was removed from the dataset. If two blocks or more were removed, the task was removed from the dataset. If a task was missing, the participant was removed from the dataset. - removing data according to specific criteria (`val`): For each participant and each RT-based task, if the mean accuracy of a block was smaller than 75% for tasks with 2 response keys and 50% for tasks with 4 response keys, the block was removed from the dataset. If two blocks or more were removed, the task was removed from the dataset. If a task was missing, the participant was removed from the dataset. - removing data by visual screening (`vs`): For each participant and each RT-based task, if the mean accuracy of a block was considered as smaller than the mean accuracy of the other participants, the block was removed from the dataset. If two blocks or more were removed, the task was removed from the dataset. If a task was missing, the participant was removed from the dataset. - Missing data were kept and the structural equation modeling was run with case-wise maximum likelihood (`all`). Moreover, we applied the same data trimming procedure as Friedman & Miyake (2004) for the raw data (`fm_raw`) and for log-transformed data (`fm_logarcsin`). Results files are given in the folder "Results". In this folder, the folder `multiout` contains the analyses when multivariate outliers were excluded. The folder `all` contains the analyses when no participant was excluded because s/he was a mulivariate outlier. Each of these folders contains subfolders which stem from the combination of a specific data trimming procedure and data transformation. Each subfolder consists of: - a subfolder called `correlation` in which the correlations for the different measures and age groups are saved in text format. The following abbreviations are used in the file names: - both = correlations for both age groups together - young = correlations for the young age group only - old = correlations for the older age group only - measure1 = interference effect I (e.g., difference between incongruent and congruent trials) - measure2 = interference effect II (e.g., difference between incongruent and neutral trials) - measure3 = facilitation effect (e.g., difference between neutral and congruent trials) - a subfolder called `SEM` in which the structural equation modeling results are saved in text format. - SEM computations were performed separately for the Simon task including stimulus repetitions and for the Simon task excluding stimulus repetitions (`simonwithoutrep`). In all other tasks, stimulus repetitionws were not allowed. - SEM computations were also computed separately for each age group. These analyses are given in the files named with `withinagegroup`. - a file named `descriptives` in which the descriptive results (e.g., mean, SD, max, min) are saved in text format. - a file named `participantlist` consisting of the list of the participants used to compute the present results. This is saved in text format. - a file named `reliability` in which the reliability estimates are saved in text format. For the data transformations `logarcsin` and `raw`, a bifactor model approach was also used in which baseline trials of each task were forced to load on a baseline factor, and interference trials of each task were forced to load on an inhibition factor. These additional analyses are given in files named with `trial`. The models used to compute SEM are in the folders "models". They are prepared to be used with lavaan. - In the files named with `models_measure12`, the models for the interference effects I and II are presented; in the files named with `models_measure3`, the models for the facilitation effect are presented. - In the files named with `trial`, the models are presented for the SEM including the bifactor model approach mentioned above. - In the files named with `wag`(i.e., within age group), the models are presented for the SEM for each age group separately. Goodness-fit statists were also saved in overview files (i.e., files named with `overview_fitstatistics`) when multivariate outlers were excluded (i.e., in the folder `multiout`) and when they were not excluded (i.e., in the folder `all`). With these overview files, overall overviews were computed (named with `overview_SEM`), which are summarized in the excel file named "overview_SEM". The results reported in the article are presented in the folder called `all/logarcsin_sd`. In addition to the subfolders and files presented above, this subfolder contains the following subfolder: - `ANOVA&t_tests` in which the results from the ANOVA´s and t-tests are saved in text format.