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Models of population-wide cultural change tend to invoke one of two broad models of individual change. One approach theorizes people actively updating their beliefs and behaviors in the face of new information. The other argues that, following early socialization experiences, dispositions are stable. We formalize these two models, elaborate empirical implications of each, and derive a simple combined model for comparing them using panel data. We test this model on 183 attitude and behavior items from the 2006 to 2014 rotating panels of the General Social Survey. The pattern of results is complex but more consistent with the settled dispositions model than with the active updating model. Most of the observed change in the GSS appears to be short-term attitude change or measurement error rather than persisting changes. When persistent change occurs, it is somewhat more likely to occur in younger people and for public behaviors and beliefs about high-profile issues than for private attitudes. We argue that we need both models in our theory of cultural evolution but that we need more research on the circumstances under which each is more likely to apply.

Since most social science research relies on multiple data sources, merging data sets is an essential part of researchers’ workflow. Unfortunately, a unique identifier that unambiguously links records is often unavailable, and data may contain missing and inaccurate information. These problems are severe especially when merging large-scale administrative records. We develop a fast and scalable algorithm to implement a canonical model of probabilistic record linkage that has many advantages over deterministic methods frequently used by social scientists. The proposed methodology efficiently handles millions of observations while accounting for missing data and measurement error, incorporating auxiliary information, and adjusting for uncertainty about merging in post-merge analyses. We conduct comprehensive simulation studies to evaluate the performance of our algorithm in realistic scenarios. We also apply our methodology to merging campaign contribution records, survey data, and nationwide voter files. An open-source software package is available for implementing the proposed methodology.

The reliability of transcranial magnetic stimulation (TMS) measures in healthy older adults and stroke patients has been insufficiently characterized. We determined whether common TMS measures could reliably evaluate change in individuals and in groups using the smallest detectable change (SDC), or could tell subjects apart using the intraclass correlation coefficient (ICC). We used a single-rater test-retest design in older healthy, subacute stroke, and chronic stroke subjects. At twice daily sessions on two consecutive days, we recorded resting motor threshold, test stimulus intensity, recruitment curves, short-interval intracortical inhibition, and facilitation, and long-interval intracortical inhibition. Using variances estimated from a random effects model, we calculated the SDC and ICC for each TMS measure. For all TMS measures in all groups, SDCs for single subjects were large; only with modest group sizes did the SDCs become low. Thus, while these TMS measures cannot be reliably used as a biomarker to detect individual change, they can reliably detect change exceeding measurement noise in moderate-sized groups. For several of the TMS measures, ICCs were universally high, suggesting that they can reliably discriminate between subjects. TMS measures should be used based on their reliability in particular contexts. More work establishing their validity, responsiveness, and clinical relevance is still needed.

In any design science such as public management, the importance of measurement is central to both scholarship and practice. Research built on measures that are not valid or reliable can generate misleading results and produces little value in the world of practice. This article applies measurement theory to administrators' self-perceptions of organizational performance, measures commonly used in the literature. Such measures can be prone to common source bias whereby spurious results are highly likely. This article uses measurement theory to show why common source bias can be a problem and then introduces an empirical test for bias based on a data set that includes both perceptual measures of performance and archival measures. The empirical test shows that spurious results are not only common but that they might include as many as 50% of all statistical tests. The article further examines the specific types of questions and measures most likely to generate measurement bias and provides guidelines for scholars conducting research on public organization performance.

The present study uses doubly latent models to estimate the effect of average mathematics achievement at the class level on students’ subsequent mathematics achievement (the “Peer Spillover Effect”) and mathematics self-concept (the “Big-Fish-Little-Pond-Effect; BFLPE”), controlling for individual differences in prior mathematics achievement. Our data, consisting of 13-year-old students from Canada, the USA, and New Zealand, come from a unique cross-national database with a longitudinal design at the student level: the Second International Mathematics Study (SIMS80). This historical survey was administered by IEA in the 1980s and highly influenced the development of educational policies in the following decades. We replicate a widely cited study based on SIMS80, interrogating the validity of its findings of a positive peer spillover effect. When we adjust for measurement error, using doubly latent models, we observe that originally positive peer spillover effects become less positive or disappear altogether. On the contrary, negative BFLPEs become more negative and remain statistically significant throughout. Our study is the only cross-national study to have evaluated both the BFLPE and the peer spillover effect with controls for a true measure of prior achievement — and the only study to test the peer spillover effect cross-nationally using doubly latent models. Our findings question the empirical results of past and current research evaluating school- and class-level compositional effects based on sub-optimal models that fail to control for measurement error.

Reliability and measurement error are measurement properties that quantify the influence of specific sources of variation, such as raters, type of machine, or time, on the score of the individual measurement. Several designs can be chosen to assess reliability and measurement error of a measurement. Differences in design are due to specific choices about which sources of variation are varied over the repeated measurements in stable patients, which potential sources of variation are kept stable (ie, restricted), and about whether or not the entire measurement instrument (or measurement protocol) was repeated or only part of it. We explain how these choices determine how intraclass correlation coefficients and standard errors of measurement formulas are built for different designs by using Venn diagrams. Strategies for improving the measurement are explained, and recommendations for reporting the essentials of these studies are described. We hope that this paper will facilitate the understanding and improve the design, analysis, and reporting of future studies on reliability and measurement error of measurements.

Forensic pathologists commonly use computed tomography (CT) images to assist in determining the cause and manner of death as well as for mass disaster operations. Even though the design of the CT machine does not inherently produce distortion, most techniques within anthropology rely on metric variables, thus concern exists regarding the accuracy of CT images reflecting an object's true dimensions. Numerous researchers have attempted to validate the use of CT images, however the comparisons have only been conducted on limited elements and/or comparisons were between measurements taken from a dry element and measurements taken from the 3D-CT image of the same dry element. A full-body CT scan was performed prior to autopsy at the Office of the Chief Medical Examiner for the State of Maryland. Following autopsy, the remains were processed to remove all soft tissues and the skeletal elements were subject to an additional CT scan. Percent differences and Bland–Altman plots were used to assess the accuracy between osteometric variables obtained from the dry skeletal elements and from CT images with and without soft tissues. An additional seven crania were scanned, measured by three observers, and the reliability was evaluated by technical error of measurement (TEM) and relative technical error of measurement (%TEM). Average percent differences between the measurements obtained from the three data sources ranged from 1.4% to 2.9%. Bland–Altman plots illustrated the two sets of measurements were generally within 2mm for each comparison between data sources. Intra-observer TEM and %TEM for three observers and all craniometric variables ranged between 0.46mm and 0.77mm and 0.56% and 1.06%, respectively. The three-way inter-observer TEM and %TEM for craniometric variables was 2.6mm and 2.26%, respectively. Variables that yielded high error rates were orbital height, orbital breadth, inter-orbital breadth and parietal chord. Overall, minimal differences were found among the data sources and high accuracy was noted between the observers, which prove CT images are an acceptable source to collect osteometric variables.

Life satisfaction is often assessed using single-item measures. However, estimating the reliability of these measures can be difficult because internal consistency coefficients cannot be calculated. Existing approaches use longitudinal data to isolate occasion-specific variance from variance that is either completely stable or variance that changes systematically over time. In these approaches, reliable occasion-specific variance is typically treated as measurement error, which would negatively bias reliability estimates. In the current studies, panel data and multivariate latent state-trait models are used to isolate reliable occasion-specific variance from random error and to estimate reliability for scores from single-item life satisfaction measures. Across four nationally representative panel studies with a combined sample size of over 68,000, reliability estimates increased by an average of 16% when the multivariate model was used instead of the more standard univaríate longitudinal model.

Survey research is the most frequently used data collection method in many disciplines. Nearly, everybody agrees that such data contain serious measurement errors. However, only few researchers try to correct for them. If the measurement errors in the variables vary, the comparison of the sizes of effects of these variables on each other will be wrong. If the sizes of the measurement errors are different across countries, crossnational comparisons of relationships between variables cannot be made. There is ample evidence for these differences in measurement errors across variables, methods and countries (Saris and Gallhofer in Design, evaluation and analysis of questionnaires for survey. Wiley, Hoboken, 2007; Oberski in Measurement errors in comparative surveys. PhD thesis, University of Tilburg, 2011). Therefore, correction for measurement errors is essential for the social sciences. The correction for measurement errors can be made in a simple way, but it requires that the sizes of the error variances are known for all observed variables. Many experiments are carried out to determine the quality of questions. The relationship between the quality and the characteristics of the questions has been studied. Because this relationship is rather strong, one can also predict the quality of new questions. A program SQP has been developed to predict the quality of questions. Using this program, the quality of the questions (complement of error variance) can be obtained for nearly all questions measuring subjective concepts. For objective variables, other research needs to be used (e.g., Alwin in Margins of error: a study of reliability in survey measurement. Wiley, Hoboken, 2007). Using these two sources of information, making correction for measurement error in survey research is possible. We illustrate here that correction for measurement errors can and should be performed.

ContextThe Open Landing Error Scoring System (OpenLESS) is a novel tool for automating the LESS to assess lower extremity movement quality during a jump-landing task. With the growing use of clinical measures to monitor outcomes and limited time during clinical visits, there is a need for automated systems. The OpenLESS is an open-source tool that uses a markerless motion-capture system to automate the LESS using 3-dimensional kinematics.ObjectiveTo describe the development of the OpenLESS, examine its validity against expert-rater LESS scores in healthy and clinical cohorts, and assess its intersession reliability in an athlete cohort.DesignCross-sectional study.Patients or Other ParticipantsNinety-two adult participants from 3 distinct cohorts: a healthy university student cohort (12 men, 14 women, age = 23.0 ± 3.8 years, height = 171.9 ± 8.3 cm, mass = 75.4 ± 18.9 kg), a post–anterior cruciate ligament reconstruction (ACLR) cohort (8 men, 19 women, age = 21.4 ± 5.7 years, height = 173.5 ± 12.5 cm, mass = 73.9 ± 13.1 kg, median 33 months postsurgery), and a field-based athlete cohort (39 women, age = 25.0 ± 4.7 years, height = 165.0 ± 7.1 cm, mass = 63.5 ± 8.6 kg).Main Outcome Measure(s)The OpenLESS software interprets movement quality from kinematics captured by markerless motion capture. Validity and reliability were assessed using intraclass correlation coefficients (ICCs), standard error of measurement, and minimal detectable change.ResultsThe OpenLESS agreed well with expert-rater LESS scores for healthy (ICC2,k = 0.79) and clinically relevant post-ACLR cohorts (ICC2,k = 0.88). The automated OpenLESS system reduced scoring time, processing all 353 trials in under 25 minutes compared with the 35 hours (approximately 6 minutes per trial) required for expert-rater scoring. When tested outside laboratory conditions, the OpenLESS showed excellent reliability across repeated sessions (ICC2,k > 0.89), with a standard error of measurement of 0.98 errors and minimal detectable change of 2.72 errors.ConclusionsThe OpenLESS is a promising, efficient tool for automated jump-landing assessment, demonstrating good validity in healthy and post-ACLR populations and excellent field reliability, addressing the need for objective movement analysis.