Table of Contents

Implicit Association Test (IAT)

The implicit association test (IAT) according to Greenwald et al. (1998) is used to measure the cognitive association of two dimensions (e. g. Democrats/Republicans and good/bad). For this purpose, words and/or pictures are presented to the participant in 7 test blocks, which the participant should correctly assign to a category at the touch of a button. The reaction times are used to measure the strength and direction of the association.

Important: The IAT is not part of SoSci Survey's standard scope. The test must be booked separately and with costs as a module “Implicit methods”.

Note: Should the D-scores be missing after you downloded the data to SPSS, please set another decimal separator in the download (troubleshootingMissing Decimals in SPSS).


First create a New question of the type “Implicit association test (IAT)“within any category in the questionnaire.

The two dimensions are each divided into two categories. For each category, up to 8 words (left) or images (right, only the filename) can be entered.

The texts that are displayed as instruction at the beginning and between the test blocks can be adjusted as required. The “Explanations” tab contains a number of text input fields for this purpose. The respective standard text is visible under the input field (incl. HTML tags) and can easily be copied into the input field for customization.

After defining the categories and the corresponding stimuli (items), the test – like any other question – can be dragged on one page of the questionnaire when composing the questionnaire. It makes sense not to place any further questions on this page. If the corresponding page in the questionnaire is reached, the test runs automatically. The order of the displayed stimuli is randomly varied. After completion of the test, a characteristic value for association strength is automatically calculated (according to Greenwald et al., 2003).

Measurement of Reaction Times

The reaction times are measured with an accuracy in the range of milliseconds. So that this measurement is not distorted by the data transmission on the Internet, the question loads all necessary pictures and contents in advance.

The measurement of the reaction time starts with the presentation of a stimulus and ends with the correct keystroke of the participant. In the event of an incorrect keystroke, a negative feedback (red cross) will be displayed in accordance with the IAT concept and the time will continue to run until the correct keystroke.

After the correct keystroke, the stimulus is hidden. This is followed by a pause of 250 ms before the next stimulus is presented. This pause serves to clearly delimit the stimuli.


According to the IAT procedure, categories A to D are combined in the 7 blocks as follows (the BIAT and SC-IAT blocks differ from this scheme):

5BAExercise20 (optionally 40)

In the evaluation according to Greenwald et al. (2003) improved (recommended value, see below), the reaction times from exercise blocks 3 and 6 are also used for the calculation.

Balancing of the items

The sequence of the items (stimuli) within the test blocks is randomly varied. For the longer test blocks, the items are displayed several times. Randomization is performed according to the following ruleset:


The IAT question stores (in addition to the raw data) an index value for the association strength. This is calculated according to the literature below.

Greenwald et al. (2003) improved

The response times of blocks 3,4,6 and 7 are used for the evaluation. The cleanup takes place based on all response times of these blocks:

The evaluation is carried out in the following steps.

  1. After adjustment, the standard deviation of all response times in blocks 3 and 6 (collectively) as well as blocks 4 and 7 (collectively) is calculated.
  2. If the participant has answered incorrectly, the average response time within the block plus 600ms is used for this response. This value replaces the actual response time for further calculations (not for the standard deviation).
  3. The average response time for each of the test blocks is now calculated.
  4. The difference of the average response time is calculated from blocks 6 and 3 and blocks 7 and 4.
  5. These differences are divided (for individual standardization) by the standard deviations calculated above.
  6. The mean value of the two quotients is the index value (strength of the implicit association).

In principle, the range of values is not restricted. The results can be less than -1 and greater than +1 if the response times in blocks 3 and 6 or blocks 4 and 7 differ greatly.

The resulting value is positive if the association between categories A and C or B and D is stronger (less reaction times in block 3/4) than the association A-D or B-C (higher reaction times in block 6/7).

Utilization of the collected D-scores

The “improved” D-scores determined according to Greenwald et al. (2003) are interval scaled (metric) variables, which are predominantly (not exclusively) in the range between -2 and +2. They are usually treated in correlations and regressions in the same way as other metric variables. Before evaluating, check for extreme outliers that may need to be removed from the analysis.

The “improved” score is calculated based on the training blocks and/ of the actual test blocks. To estimate the reliability, the separate D-scores for the training blocks and the test blocks are correlated. In SoSci Survey these are the variables “Partial result based on training blocks 3 and 6 (D-Score)” and “Partial result based on test blocks 4 and 7 (D-Score)”. Reliability estimation follows the idea of the split-half method, as used for scale batteries.

Working with the Raw Data

The IAT module of SoSci Survey automatically evaluates the measured data and delivers a D-Score according to the current literature (see above). If the standard evaluation is not sufficient, SoSci Survey also provides the raw data of the measurement.

The raw data of the IAT includes, per block, which item was presented, whether the item was assigned to the correct category in the first attempt, and how long the participant needed for the assignment[ms]. This is not classic tabular data, because the items are presented in random order by default.

In the variables…b01i to….b07i, the information is stored in a format similar to that of the competition, e. g.

  ItemA 1 1183 ItemB 0 581 ItemC 1 774 ItemD 1 565 ItemE 1 565 

Here an identifier is always given for the item, the correct assignment (0=false, 1=correct) and the reaction time in milliseconds[ms].

As a rule, however, only the response times and the correct allocation are required for the evaluation. But in a handier format. The IAT module therefore saves the raw data as JSON, e. g.


The rows in the data set are not so nicely indented, but the data is the same: The outermost array (`[]`) contains 7 elements corresponding to the 7 blocks. Each block is represented by another array (`[]`), which contains as many arrays as trials. In the above data, e. g. 5 trials in blocks 1 and 2, the response time [ms] and the correct assignment (0=false, 1=correct) are coded within each of the trials.

These data can be read in with any software that supports JSON, e. g. GNU R. Since reading and processing the arrays of different lengths (i. e. no classical matrices) in R is not entirely trivial, we have provided an R-script which evaluates a SC-IAT on the basis of the JSON data: Import and analysis for the SC-IAT

Note: Handling the raw data is only necessary if you want to deviate from the standard evaluation. Normally, SoSci Survey does the evaluation automatically.


Some modifications to the IAT are possible with JavaScript. To do this, an HTML element with the corresponding JavaScript code is placed under the question. The identification `iatAB01` in the following examples must be adapted to the identification of the IAT.

Rotation of the Order

To ensure a high degree of comparability, the order of the blocks in the IAT is fixed, e. g. block 3 always shows categories A and C together on the left-hand side (Blocks).

If a random rotation e.g. of the pages is desired here…

This procedure ensures that the available variations are precisely defined. In addition, the sequence used in the data record can be traced exactly.

Number of Trials

The standard IAT comprises 7 blocks of 20,20,20,20,40,20,20 and 40 trials per block. The following JavaScript code increases the number of trials.

<script type="text/javascript">
SoSciTools.attachEvent(window, "load", function() {
// -->

Please note the square brackets within the round brackets. These define an array. This array must have exactly 7 elements, corresponding to the number of trials.


In addition to the classic IAT according to Greenwald et al. (1998), the module “Implicit Methods” also contains a single category IAT (SC-IAT according to Karpinski & Steinman, 2006) and the letter IAT (BIAT according to Sriram & Greenwald, 2009).

Single Category IAT

The SC-IAT does not compare two opposing concepts with an evaluative dimension, but only one. The participant should then categorize, for example, whether “monkey” belongs to the dimension “animal” or “positive” or not.

The implementation of the SC-IAT will be followed by Karpinski and Steinman (2006), a study on reliability presented by Blümke and Friese (2008).

In the German and English version of the question, terms (stimuli) are already entered for the evaluative dimension. These terms originate from Karpinski and Steinman (2006, p. 32) or are a literal translation of these terms into German.

Brief IAT

The BIAT reduces the number of blocks and trials to shorten the test duration. The process is implemented according to Sriram and Greenwald (2009).


Blümke, M. & Friese, M. (2008). Reliability and validity of the Single-Target IAT (ST-IAT): Assessing automatic affect towards multiple attitude objects. European Journal of Social Psychology, 38, 977–997.

Greenwald, A. G., McGhee, D. E. & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464-1480. Available online

Greenwald, A. G., Nosek, B. A. & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197–216. DOI: 10.1037/0022-3514.85.2.197

Karpinski, A. & Steinman, R. B. (2006). The Single Category Implicit Association Test as a Measure of Implicit Social Cognition. Journal of Personality and Social Psychology, 91(1), 16–32.

Lane, K. A., Banaji, M. R., Nosek, B. A. & Greenwald, A. G. (2007). Understanding and Using the Implicit Association Test: IV. In B. Wittenbrink & N. Schwarz. Implicit Measures of Attitudes, S. 59-102. Google Book Preview

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and Using the Implicit Association Test: II. Method Variables and Construct Validity. Personality and Social Psychology Bulletin, 31(2), 166-174. DOI: 10.1177/0146167204271418

Sriram, N. & Greenwald, A. G. (2009). The Brief Implicit Association Test. Experimental Psychology, 56(4), 283-94.