A number of methods have recently been developed to help psychological and cognitive scientists program RT experiments for the Internet. Currently available solutions include Tatool (von Bastian, Locher, & Ruflin, 2013), WebExp (Keller, Gunasekharan, Mayo, & Corley, 2009), and ScriptingRT (Schubert, Murteira, Collins, & Lopes, 2013), which have all been developed with the aim of providing precise timing Footnote 1. These libraries can be used to produce RT experiments that are cross-platform and cross-browser compatible. To run an experiment in Tatool or WebExp, participants need to have Java installed on their computer, whereas ScriptingRT is based on Adobe Flash. Importantly, in order to create RT experiments, these solutions typically still require substantial programming skill, and the researcher needs to host a Web server to publish the experiment. Furthermore, since these methods require specialized software or plugins, they do not meet the AMT constraint that the participant may not be required to install additional software.
Introducing the QRTEngine
Recommendations for online experimentation
Before using the QRTEngine, a researcher should carefully consider whether performing an online experiment is the right choice in the first place, and whether the QRTEngine is the optimal method for the planned study. The pros and cons of online research in general have already been discussed in some excellent previous publications (Behrend et al., 2011; Buhrmester et al., 2011; Mason & Suri, 2012; Paolacci et al., 2010). When focusing on RT tasks specifically, some additional limitations should be taken into consideration as well. The computer systems that participants use vary widely, and the error involved in measuring RT data and ensuring precise display durations on a given system is largely unknown. In addition, keyboards have different sampling rates (depending on both hardware and the operating system and browser combination), and monitor refresh rates also vary. Furthermore, continuously changing operating systems and Web browsers both contribute to the uncertainty in precision. When users run multiple applications on their computer, this can also affect timing randomly. Essentially, there are many sources of potential timing errors. These errors typically are expected to be random across subjects and conditions, and therefore, multitrial designs and collecting time audit information are necessary so that experimenters can filter out extreme timing errors in order to still obtain reliable results (Ulrich & Giray, 1989). Hence, we recommend conducting online RT experiments only when it is acceptable to test more participants in order to compensate for the noise introduced when using online acquisition methods.
Using the QRTEngine
A detailed introduction to using the QRTEngine is provided in the supplementary material as a step-by-step tutorial on how to create a simple Stroop task (MacLeod, 1991; Stroop, 1935). In order to build this task, one only needs a computer with an Internet connection and a Qualtrics account. In this article, we provide a concise overview of the development process of this same Stroop task.
The trial information, including the individual time audit information, is saved using a JSON representation within the standard Qualtrics wide data format. Saving the data in a format that can be directly imported into packages like SPSS or Excel is not possible, due to restrictions in the Qualtrics API. Therefore, we published the QRTEParser, a cross-platform Java program that is available on www.qrtengine.com and allows for converting the Qualtrics QRTEngine comma-separated value (CSV) file to a long format (where each row presents the data of a single trial). Standard packages such as SPSS and Excel can then be used to read the resulting CSV data file and perform data aggregation and statistics.
Timing features of the QRTEngine
The QRTEngine has been developed with the goal of providing timing capabilities as accurate as possible within the limits of modern Web browser technology. In this section, we discuss which actions we took to achieve this goal.
Logging of timing-related information
The QRTEngine uses an HTML5 feature called “requestAnimationFrame” (rAF) to synchronize the onset of stimuli with the refresh rate of the monitor. When using rAF, the QRTEngine is notified by the browser (through a high-priority function callback) no more than once every screen refresh that a screen refresh is taking place. Hence, every time that rAF notifies the QRTEngine, it can use a timer to determine whether the elapsed screen presentation time approaches the intended presentation time. In this way, the QRTEngine can estimate whether the last screen refresh of an intended screen duration is to be expected, and prepare the next screen accordingly. To correct for potential imprecision due to imperfect resolution of the timer, we recommend subtracting 5 ms from the intended duration. The rAF feature is currently supported in all modern browsers. In older browsers, rAF may not be available,Footnote 2 in which case the QRTEngine falls back to the less precise setTimeout() method (a low-priority function callback) to control the duration of presentation.
When the duration for a screen has been set, the QRTEngine logs the following time audit information during runtime: Duration, which is the intended duration set by the researcher; OnsetTime and OffsetTime, which provide time stamps in milliseconds relative to Unix epoch; and CalculatedDuration, which represents the estimated actual duration of screen presentation, based on the difference between the OnsetTime and OffsetTime. The CalculatedDuration can thus be used to estimate how much the actual duration of presentation deviated from the intended duration. Figure 1d provides an overview of all of the information that is saved during the example Stroop task.
Providing exact statements about the precision of the time stamps and the timing of screen presentation is not possible, because multiple factors can influence this precision. For example, when running many animations in multiple tabs or when a laptop’s battery power is low, the performance.now timer resolution is decreased in some browsers in order to save CPU power. Furthermore, it may sometimes happen that for unknown reasons, the rAF function call skips a frame. In general, very high CPU and especially RAM load can be expected to be associated with decreased precision of the timers. To estimate the precision of screen durations with the QRTEngine during an experiment, we therefore performed a validation study using external chronometry.
Validation using photosensitive diode
To investigate the accuracy of stimulus presentation timing using the QRTEngine, we conducted a timing validation study using a method similar to that reported by Simcox and Fiez (2014) to validate the timing accuracy of Adobe Flash. Accordingly, we presented stimuli under different CPU and RAM load conditions and compared the intended durations with the durations as measured by a photosensitive diode. We also compared the photodiode measurements to the durations logged by the QRTEngine in the CalculatedDuration attribute. The diode was placed on the computer screen on which the experiment was displayed and monitored via the line-in jack of a separate desktop computer running Windows Sound Recorder at a sampling rate of 44.1 kHz (this computer ran no other programs during recording). Offline analysis was performed in MATLAB and SPSS. Because of the variety of computer systems, operating systems, and Web browsers, testing every possible configuration is not possible. Therefore, we selected two configurations that reasonably represent typical configurations.
The experimental survey was run on two systems. System 1 was a BTO laptop running Windows 7 Ultimate on a 2.5-GHz Intel i5 quad core processor with 8 GB of RAM. The experiment was conducted running Chrome 27 on a Targa CRT monitor running at 60 Hz. System 2 was a MacBook Pro running OSX 10.5.8 on a 2.4-GHz Intel Core 2 Duo processor with 4 GB of RAM. The experiment was conducted running Firefox 16.0.2 with the same Targa CRT monitor running at 60 Hz. Similar to the experiment reported by Simcox and Fiez (2014), both systems used Prime95 version 27.9 to manipulate CPU and RAM load in a controllable, predictable way (Woltman, 2012). The four load conditions were (1) low, in which only the browser was running; (2) medium, in which Prime95 ran a torture test using 50 % of the CPU; (3) high, in which Prime95 ran a torture test using 100 % of the CPU; and (4) maximum, in which the torture test was run using up almost all RAM, as well.
The survey that we created for the experiment consisted of a white screen on which a large black square was presented approximately 40 times for durations of 1, 2, 3, 4, 5, 6, 12, 30, and 60 frames (in that order, with each duration condition fully completed before the next condition). The interval between the presentations of two squares was set to 1,500 ms. The total duration of the experiment was about 12 min.
Results and discussion
In order to estimate the accuracy of the presentation timing, we compared the actual stimulus duration measured using the photosensitive diode with (1) the duration set by the experimenter and (2) the calculated duration logged in the QRTE data file. The mean absolute differences between these measures are displayed in Table 1 (for details, see the supplementary material).
As Table 1 shows, the average deviation of both measures is around 6 ms in the low-load conditions. Only under conditions of maximum load was a substantial deterioration in performance observed in both systems, leading to an average deviation of around 10 ms. The results also indicate a difference in performance between the two systems. It may have been that this difference was due to the fact that the System 2 hardware and software were quite old (the operating system was released in 2007 and has lost support by current browsers). However, this difference in performance also illustrates a general caveat in online experimenting: The experimenter simply cannot know all hardware and other factors that will influence performance. Our findings show that the mean deviation between the intended duration of a stimulus and the actual deviation of a stimulus was small, and given the 60-Hz display rate used here, falls within the range of ± 1 display frame deviation (16.67 ms). A similar accuracy was observed for the calculated duration attribute that is logged in the QRTE data file. Researchers might use this attribute to get a reasonably reliable estimate of timing errors, which can be used to exclude trials or participants on an individual basis when accurate timing is critical. Apart from the mean deviation in milliseconds, we also analyzed the deviation between the photodiode measurement and the intended duration as expressed in the number of frames (16.67-ms units, in our test case). The results of these analyses are reported in Table 2. The results show that across the systems, including the low-, medium-, and high-load conditions, a timing accuracy of the intended duration within the range of ± 1 frame deviation was present in 97 % of the trials. In the supplementary material, we provide tables similar to Table 2 for the trials in which only one or two frames were presented.
Server communication delay
Another important timing-related aspect concerns the communication with the Qualtrics server. Because the implementation of a block of trials depends on the native Qualtrics Loop & Merge functionality, at the end of every trial the data that have been collected are automatically sent to the Qualtrics server. The speed of this server communication relies on factors such as the Internet connection of the participant and on the load of the Qualtrics server. The duration of the server communication time is variable and not under control of the researcher.
When setting up a QRTEngine survey, the researcher defines an intertrial delay (ITD), which is essentially the minimum delay between the end of the current and the beginning of the next trial (the intertrial interval), during which period server communication takes place. Please note that preloading (or caching) trials is not possible when using the Qualtrics environment. As is displayed in Fig. 1d, the ITD is the minimum time that will elapse between two consecutive trials set by the researcher. During the ITD, the last screen of trial n and the first screen of trial n + 1 are displayed, while in the meantime three processes take place. The first process, InitPre, takes care of the server communication during which the collected information is sent to the server and the data regarding the next trial are received from the server. When server communication is complete, the second process, called Init, starts, which initializes the next trial. The third process, InitPost, fills up the remaining ITD that was set by the researcher. If the combined duration of InitPre and Init exceeds the ITD, the duration of the InitPost is set to 0. The time stamps of all three processes are logged separately (see also Fig. 1d).
In the validation experiments, to be discussed in the next section, we measured the duration of the InitPre and Init processes. Because we anticipated that a slightly longer ITD for some participants would not negatively influence our results, we allowed MTurk workers to participate if their estimated server communication delay was lower than 2,000 ms.Footnote 4 Across the 158 participants in Experiments 1, 2, and 3, the average InitPre duration was 1,388 ms (SD = 630 ms), whereas the average Init duration was 110 ms (SD = 31 ms). Thus, whereas we aimed for an ITD of 1,000 ms, for most trials (75.7 %) the actual ITD was slightly longer than the intended duration. In the supplementary material, we have provided participant-specific information on the durations of InitPre and Init.
Online RT experiments using the QRTEngine
Experiment 1: Stroop task
The Stroop task is a classic paradigm that requires participants to identify the word color of congruent and incongruent color words. When word and word color are incongruent (e.g., the word “red” in green), RTs are slower and people make more errors than when the stimuli are congruent (MacLeod, 1991; Stroop, 1935).
The participants were recruited through AMT and were required to be located in the United States.Footnote 5 Fifty-two participants completed the task: 29 were female, 43 were right-handed, and the average age was 35 years (SD = 12.54). Participants received financial compensation to complete the task, which lasted for approximately 5 min. Informed consent was given prior to the experiment. The ethics committee of the Leiden University Psychology section approved the experiment, as well as the following experiments described in this article.
The experiment was based partly on the Stroop task used by Crump et al. (2013). Participants completed 96 trials, of which 48 were congruent. The background color of the page was white, and words were presented in 50-point font in the colors red, green, blue, and yellow. Participants were asked to respond by typing the first letter of the color of the stimulus. The fixation point, word, and feedback were presented at the center of the page.
The trial started with a fixation cross displayed for 1,000 ms, which was followed by a blank screen displayed for 500 ms. Then the screen showing the color word was displayed until a response was made. Accuracy feedback was given after the response, using the word “CORRECT” or “INCORRECT” displayed for 500 ms in a black 30-point font.
Participants found the task advertised as a HIT on AMT. They were informed that this HIT would require them to respond as accurately as possible and that it required full concentration. After participants had decided to take part, they were linked to our Qualtrics survey. This survey first collected some metadata, such as the browser version, operating system, and screen resolution used. Then, the survey estimated the speed of the participant’s Internet connection using the ITD estimation described earlier. When the connection speed was too low, the participant was kindly informed that he or she could not participate.
After participants had given informed consent, they were asked for their AMT worker ID in order that we could pay the participants later. This was followed by the instruction to maximize the browser window using F11 and reminding the participant that concentration was necessary for successful completion of the task. Following these general instructions, the specific instructions for the Stroop task were presented along with four examples. After this, participants started the task. When the task was completed, participants were asked for some demographic information before the survey ended.
Results and discussion
We excluded four participants because of an accuracy below 80 %. A one-way repeated measures analysis of variance (ANOVA) on correct RTs and error rates was conducted with Congruency as the factor. For this experiment, as well as for the following experiments, a Greenhouse–Geisser correction was applied when the assumption of sphericity was violated. In these cases, we report corrected p values and uncorrected degrees of freedom. All significant effects (p < .05) are reported. The MSE and partial eta-squared are reported as measures of effect size.
Figure 2 shows the mean RT and error rate for each condition. In line with predictions, RTs were slower for incongruent (1,064 ms) than for congruent (887 ms) trials, showing a large Stroop effect (177 ms), F(1, 44) = 144.28, MSE = 4903, p < .001, ηp 2 = .766. Error rates were very low, with higher error rates for incongruent (2 %) than for congruent (0.3 %) items, F(1, 44) = 17.92, MSE = .001, p < .001, ηp 2 = .289. The observed RTs are consistent with those in the study by Crump et al. (2013), who reported 859 ms for congruent and 1,152 ms for incongruent trials.
Experiment 2: Attentional blink
The attentional blink paradigm requires very precise control over the timing of stimulus presentation, and is therefore well suited to investigate the timing capabilities of the QRTEngine. Stimuli are presented in a rapid serial visual presentation (RSVP), and participants are asked to identify a first target (T1) and to decide whether a second target (T2) was present. The classic attentional blink effect shows that target identification of the T2 is impaired when it is presented 100–500 ms after T1 (Raymond, Shapiro, & Arnell, 1992). When T2 is presented directly after T1, there is less impairment, an effect called “lag 1 sparing.” When T2 is presented 2–8 places after T1, accuracy typically increases gradually with the distance from T1.
Participants were recruited through AMT and were required to be located in the United States. Among the 49 participants who completed the task, 20 were female, 37 were right-handed, and the average age was 34 years (SD = 12.54). Participants received financial compensation to complete the task, which lasted for approximately 5 min. Informed consent was taken prior to the experiment.
Attentional blink task
The experiment consisted of 80 trials and was partly based on the attentional blink task used by Crump et al. (2013). A gray 300-pixel square was displayed in the center of the page, the background was white, and stimuli were presented in the center of the square in a 50-point font. The letter sequences consisted of 7–14 pretarget letters and seven posttarget letters, all of which were uniquely selected from the whole alphabet and randomly ordered.Footnote 6 T1 always appeared at the end of the pretarget sequence, and T2 was presented on 50 % of the trials at each of the posttarget positions. The present attentional blink experiment used black letters as distractors, a white letter as T1, and a black capital X as T2.
In each trial, a fixation cross was presented for 1,000 ms, followed by the stream of letters, each of which was presented for 100 ms. After the stream completed, participants were asked to identify T1 by pressing the corresponding letter on the keyboard. Then they were asked to press “1” if T2 had been present, or “0” if T2 had not. Both questions were displayed in a 15-point black font in the center of the gray square.
The same procedure was used as described for Experiment 1.
Results and discussion
The analysis included only trials in which T1 was identified correctly. Figure 3 shows the mean proportions correct for detecting T2. A significant effect of lag is apparent, F(6, 252) = 31.89, MSE = .056, p < .001, ηp 2 = .432. The proportion correct is higher for lag 1 (.54) than for lag 2 (.41), t(42) = 2.82, p = .007, an effect demonstrating the typical lag 1 sparing. After lag 2, the proportion correct increases gradually to lag 7. In other words, the standard effects observed in attentional blink paradigms were reproduced in the present experiment. The proportions correct for the present experiment are quite similar to, although a little higher than, the results found by Crump et al. (2013), who reported a lag 1 proportion correct of .43 and a lag 2 proportion correct of .23. The differences between the lag 1 and lag 2 proportions correct in both studies are quite similar: .13 in our results, and .2 in the study by Crump et al. (2013).
Experiment 3: Masked priming
The minimum duration for which a stimulus can be displayed on an average monitor is 16 ms, corresponding to a refresh rate of 60 Hz. Therefore, if the QRTEngine were proved capable of accurately presenting stimuli for 16 ms, that would indicate very good control over the timing. Masked-priming tasks are among the few paradigms that depend on such short presentation times. A classic result pattern of the masked-priming task developed by Eimer and Schlaghecken (2002) is a negative compatibility effect: When primes are presented for very short durations, a congruent prime increases the RT. We expected that a negative compatibility effect could indeed be found if the QRTEngine allowed very short stimulus presentation times.
Participants were recruited through AMT and were all located in the United States. Among the 57 participants who completed the task, 33 were female, 57 were right-handed, and the average age was 42 years (SD = 13.04). Participants received financial compensation to complete the task, which lasted for approximately 30 min. Informed consent was taken prior to the experiment.
The experiment consisted of 576 trials. The stimuli were black and measured 70 pixels in width and 40 pixels in height, and were presented in the center of a white page. There were six blocks of 96 trials; each block had a prime duration of 16, 32, 48, 64, 80, or 96 ms. The prime stimulus consisted of two arrows pointing is the same direction (<< or >>). The mask stimuli were chosen from 24 images consisting of eight randomly rotated and placed lines. The probe stimulus was similar to the prime.
For this experiment, largely the same procedure was used as in Experiment 1. After the general instructions, participants received task-specific instructions and started the task. Each trial started with a fixation cross presented for 1,000 ms, followed by the prime with variable duration, which was followed by the mask presented for 96 ms. Next, a blank 48-ms interval was presented. Finally, the probe stimulus was presented for 96 ms and then removed immediately. Participants were instructed to respond as quickly and accurately as possible by pressing “S” when the probe consisted of left arrows, and “L” when the probe consisted of right arrows.
Results and discussion
Of the 57 participants, four outliers were omitted from analysis: three on the basis of low accuracy, one on the basis of RT (mean RT > 1,250 ms). Figure 4 shows the mean RTs and error rates for each of the prime durations. Importantly, the classic pattern of the masked-priming paradigm was reproduced: Relative to congruent primes, an incongruent prime increased RTs for longer prime durations (standard compatibility effect), whereas for shorter prime durations, the pattern reversed (negative compatibility effect). ANOVAs showed that the main effect of compatibility was not significant. The main effect of prime duration was significant, F(5, 260) = 5.49, MSE = 7875, p = .003, ηp 2 = .096, as was the interaction effect of compatibility and duration, F(5, 260) = 10.75, MSE = 549, p < .001, ηp 2 = .171. The compatibility effects were significant for all duration conditions except the 64-ms condition: for the 16-ms condition, compatibility effect = –10 ms, t(52) = 3.36, p = .001; for the 32-ms condition, compatibility effect = –14 ms, t(52) = 3.35, p = .002; for the 48-ms condition, compatibility effect = a marginally significant –10 ms, t(52) = 1.96, p = .056; for the 80-ms condition, compatibility effect = 11 ms, t(52) = –2.03, p = .047; for the 96-ms condition, compatibility effect = 16 ms, t(52) = –2.73, p = .009.
For the error rates, we found a significant main effect of prime duration, F(5, 260) = 3.4, MSE = .005, p = .031, ηp 2 = .061, a significant main effect of compatibility, F(1, 52) = 4.09, MSE = .007, p = .048, ηp 2 = .073, and a significant interaction effect, F(5, 260) = 7.57, MSE = .003, p < .001, ηp 2 = .127. The compatibility effects were significant for the 80-ms condition, compatibility effect = 3 %, t(52) = 2.14, p = .037, and for the 96-ms condition, compatibility effect = 5 %, t(52) = 4.21, p < .001.
The observed RTs are consistent with those from the study by Crump et al. (2013), who reported a compatibility effect of 34 ms in the 96-ms condition, whereas we found a compatibility effect of 16 ms in that condition. Since no negative compatibility effect was found in the study by Crump et al. (2013), we compared the negative compatibility effect with the one in the original masked-priming study by Eimer and Schlaghecken (2002). They reported a negative compatibility effect of about 12 ms in the 16-ms condition, very similar to the 10 ms from our results. Given that the pattern of our results is similar to the original findings by Eimer and Schlaghecken (2002), the results from the present experiment show that the QRTEngine can even be used to conduct behavioral experiments that rely on very short stimulus presentation timings.
Summary of behavioral validation studies
Performing behavioral research online is an interesting approach that is gaining increased popularity. The rapid development of Web-browser technology and the emergence of recruiting platforms such as AMT have facilitated this development. We think that the QRTEngine can help researchers conduct online behavioral research in an accessible and efficient way.
Although we believe that the QRTEngine indeed provides interesting benefits, potential users should be aware of some limitations. First of all, given that the observed accuracy of presentation timing is ± 1 frame deviation in 97 % of trials, the presentation timing capabilities of the QRTEngine are certainly not as good as those of software like E-Prime (Schneider et al., 2002) or comparable packages for offline research, since these solutions have considerably more control over the operating system. Furthermore, timing is partly dependent on the participant’s Web browser: For older browsers, the timing is less precise. Additionally, when using the time stamps logged during an experiment, it is important to keep in mind that these time audits are subject to measurement error. We expect that the limitations regarding timing will gradually be resolved in the future, since Web browsers develop very rapidly with a focus on speed. Although our validation studies suggest that the QRTEngine is often capable of providing very short presentation times of up to one frame (i.e., 16.7 ms when using a 60-Hz refresh rate), inaccuracy will always remain when running online experiments directly in the browser, especially with very short presentation times. Considering these validations, it should be noted that we did not perform any validation on mobile devices such as tablets or smartphones. Given these considerations, online studies will introduce considerable nonsystematic noise in terms of timing, which might affect the sensitivity to small effects in RTs. In extreme cases, measurement error could therefore be speculated to become even twice as high as in typical lab experiments. Increasing the sample size is the easiest way to reduce the impact of this problem. As a very conservative rule of thumb, researchers might therefore consider multiplying their sample size, based on conventional power analyses of lab studies, by a factor 4 in order to be equally sensitive to the similar effects observed in the lab.
Second, the ITD may occasionally exceed the desired duration between trials. This is because the method is dependent on Qualtrics servers and the Internet connection of the participant. We therefore recommend not using the QRTEngine when it is necessary for the intertrial interval to be exactly consistent or when the intertrial interval needs to be very short. Using a test that measures the Internet connection quality before the start of an experiment will partly help to reduce the impact of this limitation.
Third, at present the QRTEngine allows for presenting text and pictures only, so studies requiring auditory and movie stimuli are not supported. Furthermore, researchers should also be aware that designs in which the types of trials cannot be specified in advance in Loop & Merge lists might be difficult to implement in Qualtrics. Besides these QRTEngine-specific recommendations, we would remind potential users of the caveats associated with online RT experiments in general, which we discussed in the introduction.
On the basis of the StatCounter browser-use statistics for June 2014 (http://caniuse.com/usage_table.php, retrieved July 1, 2014), 85 % of currently used desktop browsers support rAF. For a complete overview of rAF support, including previous browser versions, see http://caniuse.com/requestanimationframe. Please note that these statistics are not absolute; as the site states, “a feature is considered ‘supported’ when its primary purpose is largely fulfilled.” Therefore, QRTEngine logs rAF and timer support for every participant.
For a complete overview of support, including previous browser versions, see http://caniuse.com/high-resolution-time.
Participation was only allowed and data were only acquired when the mean +2 standard deviations of an individual’s InitPre did not exceed 2,000 ms. These InitPre’s were measured in a separate question block, which looped ten trials while showing an initialization progress bar, before the experiment started.
Although these Mechanical Turk participants had a registered location within the United States, we cannot exclude the possibility that some participants were actually performing the experiment from another country.
We aimed to present eight posttarget positions, but due to a programming error, the eighth position was never presented. We therefore do not include the lag 8 condition in the analyses.
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The main programming of the QRTEngine was performed by Erwin Haasnoot. We thank those who have tested and provided feedback on earlier versions of the QRTEngine, especially Poppy Watson and Laura di Bella. This work was supported by a grant from the Netherlands Organization for Scientific Research (NWO) to Bernhard Hommel.
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Barnhoorn, J.S., Haasnoot, E., Bocanegra, B.R. et al. QRTEngine: An easy solution for running online reaction time experiments using Qualtrics. Behav Res 47, 918–929 (2015). https://doi.org/10.3758/s13428-014-0530-7
- Online experiments
- Amazon Mechanical Turk