Behavior Research Methods

Journal: Behavior Research Methods

Abbreviation

Behav Res Methods

Publisher

Springer

ISSN

1554-351X
1554-3528

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Publications1 - 10 of 14

Comparing Likert and visual analogue scales in ecological momentary assessment
Haslbeck, Jonas M.B.; Martínez, Alberto Jover; Roefs, Anne J.; et al. (2025)
Behavior Research Methods
Measuring subjective experiences in ecological momentary assessment (EMA) studies has become pervasive in psychological science. A design choice that has to be made in all of these studies is which response scale to use. However, to date there is little guidance on this choice in the context of EMA. As a first step towards understanding the effects of different response scales, we experimentally vary the response scale and assess whether the resulting time series of subjective experiences are systematically different. We conducted a between-person experiment comparing a seven-point Likert scale (n = 63) with a Visual Analogue Scale (VAS; n = 56) in an EMA study measuring affective states over 14 days. Using Bayesian multilevel models, we found that the VAS resulted in moderately higher within-person item means, lag-0 correlations, lag-1 autocorrelations, as well as lower within-person skewnesses and response frequencies of exact zeros. We found the largest difference in correlations with external criteria related to psychopathology, where correlations for the VAS were much higher. We did not observe reliable differences in within-person item variances, root mean squared successive differences, missing data, duration of measurements, and ratings about the experiences with the EMA survey. Apart from higher within-person means and higher correlations with external criteria in the VAS group, the differences were relatively small. While more research on response scales in EMA is needed, based on our results we conclude that the VAS should be preferred in studies aiming at capturing affective states relating to general psychopathology, as well as for items whose variation occurs close to scale limits. We conclude by discussing how our findings may contribute to a larger research agenda that addresses the fit of different response scales for different research aims.
nodeGame: Real-time, synchronous, online experiments in the browser
Balietti, Stefano (2017)
Behavior Research Methods
An algorithmic approach to determine expertise development using object-related gaze pattern sequences
Wang, Felix S.; Gianduzzo, Céline; Meboldt, Mirko; et al. (2022)
Behavior Research Methods
Eye tracking (ET) technology is increasingly utilized to quantify visual behavior in the study of the development of domainspecific expertise. However, the identification and measurement of distinct gaze patterns using traditional ET metrics has been challenging, and the insights gained shown to be inconclusive about the nature of expert gaze behavior. In this article, we introduce an algorithmic approach for the extraction of object-related gaze sequences and determine task-related expertise by investigating the development of gaze sequence patterns during a multi-trial study of a simplified airplane assembly task. We demonstrate the algorithm in a study where novice (n = 28) and expert (n = 2) eye movements were recorded in successive trials (n = 8), allowing us to verify whether similar patterns develop with increasing expertise. In the proposed approach, AOI sequences were transformed to string representation and processed using the k-mer method, a well-known method from the field of computational biology. Our results for expertise development suggest that basic tendencies are visible in traditional ET metrics, such as the fixation duration, but are much more evident for k-mers of k > 2. With increased on-task experience, the appearance of expert k-mer patterns in novice gaze sequences was shown to increase significantly (p < 0.001). The results illustrate that the multi-trial k-mer approach is suitable for revealing specific cognitive processes and can quantify learning progress using gaze patterns that include both spatial and temporal information, which could provide a valuable tool for novice training and expert assessment.
Accuracy in parameter estimation and simulation approaches for sample-size planning accounting for item effects
Buchanan, Erin M.; Elsherif, Mahmoud M.; Geller, Jason; et al. (2026)
Behavior Research Methods
The planning of sample size for research studies often focuses on obtaining a significant result given a specified level of power, significance, and an anticipated effect size. This planning requires prior knowledge of the study design and a statistical analysis to calculate the proposed sample size. However, there may not be one specific testable analysis from which to derive power (Silberzahn et al., Advances in Methods and Practices in Psychological Science, 1(3), 337356, 2018) or a hypothesis to test for the project (e.g., creation of a stimuli database). Modern power and sample size planning suggestions include accuracy in parameter estimation (AIPE, Kelley, Behavior Research Methods, 39(4), 755-766, 2007; Maxell et al., Annual Review of Psychology, 59, 537-563, 2008) and simulation of proposed analyses (Chalmers & Adkins, The Quantitative Methods for Psychology, 16(4), 248-280, 2020). These toolkits offer flexibility in traditional power analyses that focus on the if-this, then-that approach. However, both AIPE and simulation require either a specific parameter (e.g., mean, effect size, etc.) or a statistical test for planning sample size. In this tutorial, we explore how AIPE and simulation approaches can be combined to accommodate studies that may not have a specific hypothesis test or wish to account for the potential of a multiverse of analyses. Specifically, we focus on studies that use multiple items and suggest that sample sizes can be planned to measure those items adequately and precisely, regardless of the statistical test. This tutorial also provides multiple code vignettes and package functionality that researchers can adapt and apply to their own measures.
Contact-free measurement of heart rate, respiration rate, and body movements during sleep
Brink, Mark; Müller, Christopher H.; Schierz, Christoph (2006)
Behavior Research Methods
Value-based decision-making battery: A Bayesian adaptive approach to assess impulsive and risky behavior
Pooseh, Shakoor; Bernhardt, Nadine; Guevara, Alvaro; et al. (2018)
Behavior Research Methods
The validity of RFID badges measuring face-to-face interactions
Elmer, Timon; Chaitanya, Krishna; Purwar, Prateek; et al. (2019)
Behavior Research Methods
Face-to-face interactions are important for a variety of individual behaviors and outcomes. In recent years, a number of human sensor technologies have been proposed to incorporate direct observations in behavioral studies of face-to-face interactions. One of the most promising emerging technologies is the application of active Radio Frequency Identification (RFID) badges. They are increasingly applied in behavioral studies because of their low costs, straightforward applicability, and moderate ethical concerns. However, despite the attention that RFID badges have recently received, there is a lack of systematic tests on how valid RFID badges are in measuring face-to-face interactions. With two studies, we aim to fill this gap. Study 1 (N = 11) compares how data assessed with RFID badges correspond with video data of the same interactions (construct validity) and how this fit can be improved using straightforward data processing strategies. The analyses show that the RFID badges have a sensitivity of 50%, which can be enhanced to 65% when flickering signals with gaps of less than 75 s are interpolated. The specificity is relatively less affected by this interpolation process (before interpolation 97%, after interpolation 94.7%)—resulting in an improved accuracy of the measurement. In Study 2 (N = 73) we show that self-report data of social interactions correspond highly with data gathered with the RFID badges (criterion validity).
The effect of sampling rate and lowpass filters on saccades – A modeling approach
Mack, David J.; Belfanti, Sandro; Schwarz, Urs (2017)
Behavior Research Methods
The study of eye movements has become popular in many fields of science. However, using the preprocessed output of an eye tracker without scrutiny can lead to low-quality or even erroneous data. For example, the sampling rate of the eye tracker influences saccadic peak velocity, while inadequate filters fail to suppress noise or introduce artifacts. Despite previously published guiding values, most filter choices still seem motivated by a trial-and-error approach, and a thorough analysis of filter effects is missing. Therefore, we developed a simple and easy-to-use saccade model that incorporates measured amplitude-velocity main sequences and produces saccades with a similar frequency content to real saccades. We also derived a velocity divergence measure to rate deviations between velocity profiles. In total, we simulated 155 saccades ranging from 0.5° to 60° and subjected them to different sampling rates, noise compositions, and various filter settings. The final goal was to compile a list with the best filter settings for each of these conditions. Replicating previous findings, we observed reduced peak velocities at lower sampling rates. However, this effect was highly non-linear over amplitudes and increasingly stronger for smaller saccades. Interpolating the data to a higher sampling rate significantly reduced this effect. We hope that our model and the velocity divergence measure will be used to provide a quickly accessible ground truth without the need for recording and manually labeling saccades. The comprehensive list of filters allows one to choose the correct filter for analyzing saccade data without resorting to trial-and-error methods.
CAP: The creativity assessment platform for online testing and automated scoring
Patterson, John D.; Pronchick, Jimmy; Panchanadikar, Ruchi; et al. (2025)
Behavior Research Methods
Creativity is increasingly recognized as a core competency for the 21st century, making its development a priority in education, research, and industry. To effectively cultivate creativity, researchers and educators need reliable and accessible assessment tools. Recent software developments have significantly enhanced the administration and scoring of creativity measures; however, existing software often requires expertise in experiment design and computer programming, limiting its accessibility to many educators and researchers. In the current work, we introduce CAP—the Creativity Assessment Platform—a free web application for building creativity assessments, collecting data, and automatically scoring responses (cap.ist.psu.edu). CAP allows users to create custom creativity assessments in ten languages using a simple, point-and-click interface, selecting from tasks such as the Short Story Task, Drawing Task, and Scientific Creative Thinking Test. Users can automatically score task responses using machine learning models trained to match human creativity ratings—with multilingual capabilities, including the new Cross-Lingual Alternate Uses Scoring (CLAUS), a large language model achieving strong prediction of human creativity ratings in ten languages. CAP also provides a centralized dashboard to monitor data collection, score assessments, and automatically generate text for a Methods section based on the study’s tasks, metrics, and instructions—with a single click—promoting transparency and reproducibility in creativity assessment. Designed for ease of use, CAP aims to democratize creativity measurement for researchers, educators, and everyone in between.
Measuring naturalistic proximity as a window into caregiver–child interaction patterns
Salo, Virginia C.; Pannuto, Pat; Hedgecock, William; et al. (2022)
Behavior Research Methods
The interactions most supportive of positive child development take place in moments of close contact with others. In the earliest years of life, a child’s caregivers are the primary partners in these important interactions. Little is known about the patterns of real-life physical interactions between children and their caregivers, in part due to an inability to measure these interactions as they occur in real time. We have developed a wearable, infrastructure-free device (TotTag) used to dynamically and unobtrusively measure physical proximity between children and caregivers in real time. We present a case-study illustration of the TotTag with data collected over two (12-hour) days each from two families: a family of four (30-month-old son, 61-month-old daughter, 37-year-old father, 37-year-old mother), and a family of three (12-month-old daughter, 35-year-old-father, 33-year-old mother). We explored patterns of proximity within each parent–child dyad and whether close proximity would indicate periods in which increased opportunity for developmentally critical interactions occur. Each child also wore a widely used wearable audio recording device (LENA) to collect time-synced linguistic input. Descriptive analyses reveal wide variability in caregiver–child proximity both within and across dyads, and that the amount of time spent in close proximity with a caregiver is associated with the number of adult words and conversational turns to which a child was exposed. This suggests that variations in proximity are linked to—though, critically, not synonymous with—the quantity of a child’s exposure to adult language. Potential implications for deepening the understanding of early caregiver–child interactions are discussed.

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