Over the past quarter-century, the literature on gender, peace, and security has evolved into a substantial interdisciplinary field. In this line of work, researchers have investigated the interplay between state security and women’s security, or how gender equality at the state level affects the occurrence of international and intranational conflict. The conclusion is that more gender-equal countries are less prone to engage in warfare, pointing toward a link between women’s security and national security. Various indicators have been used to capture gender equality in this literature, such as the representation of women in parliamentary roles, the proportion of women participating in the labor force, and school enrollment among girls relative to boys.

The standard measure of authoritarianism asks respondents about desirable qualities in children. Although these questions are gender-neutral, respondents may differ in the gender of the child in their heads when answering. The items also may tap into gendered expectations about boys' and girls' behavior. We conducted three experiments that randomly assigned respondents to be asked about a child, a boy, or a girl in the items. We compare the means, measurement properties, and correlation between authoritarianism and other important variables across the conditions. Asking respondents about a girl creates significant differences in the level and measurement of authoritarianism, which is partially driven by the respondents' sexism. There are, fortunately, few other significant differences in the correlates of authoritarianism.

Building on the availability of geospatial data, improvements in mapping software, and innovations in spatial statistics, political scientists are increasingly taking geography seriously. As we adopt the tools of geographers, we must also consider the methodological challenges they have identified. We focus on the modifiable areal unit problem (MAUP)—the idea that the size of aggregate spatial units and the location of their borders affect the empirical results we obtain. We first describe the logic of the MAUP, and then demonstrate the MAUP through simulations, showing MAUP-related inconsistency in regression results in randomly generated and real-world data. We identify MAUP concerns, and best practices, in top journals in political science. We conclude by suggesting how scholars may respond in theoretical and empirical terms to concerns about validity and reliability that arise from the MAUP.

This paper presents a comprehensive analysis of interference events in automotive scenarios based on radar systems equipped with communication-assisted chirp sequence (CaCS). First, it examines the impact of interference on radar and communication functionalities in CaCS systems according to the orientation of the investigated nodes. For this purpose, a graph-based approach is employed with MATLAB simulations to illustrate the potential occurrence of interference on the graph for communication functionality compared with their counterparts on radar. Second, the paper delves into the impact of interference on the synchronization between two communicating CaCS nodes. It extends a previous study to match the frequency of current radar sensors, where chirp estimation, an adjusted version of the Schmidl & Cox algorithm, and correlation are adopted to synchronize the transmitter and receiver of two CaCS communicating nodes in the time-frequency plane. The proposed synchronization method is finally verified by measurements at ${79}\,\mathrm{GHz}$ with a system-on-chip, where the resulting correlation metric and mean square error are illustrated as validation factors.

Additional language speakers (ALSs) often experience anxiety due to challenges posed by their nonstandard pronunciation. Building on these insights, this paper introduces an instrument, the Accent Anxiety Scale (AAS), specifically designed to assess three sources of anxiety that are experienced by ALSs, including (a) apprehension about negative evaluations from other individuals due to their distinctive speech style, (b) concerns about rejection from the target language community because of their “foreign” pronunciation, and (c) anxieties over potential communication hurdles attributed to the intelligibility of their pronunciation. We evaluated the psychometric robustness of the AAS by analyzing data from a total of 474 immigrant and international student ALSs at a predominantly English-speaking Canadian university. Study 1 focused on immigrants (N = 203) and employed exploratory factor and correlational analyses to isolate a concise number of internally consistent and valid items for each subscale. Study 2 extended these analyses to international students (N = 153) and employed confirmatory factor and correlation analyses to further validate the AAS in this population. Study 3 examined international students (N = 118) at two time points to establish the AAS’s temporal stability. These studies yielded robust psychometric evidence for the factor structure, reliability, and validity of the AAS. The findings not only support the use of the AAS as a research instrument but also offer implications for pedagogical strategies aimed at alleviating ALSs’ accent anxiety.

Our understanding of politics often relies on the ideological placement of political actors—ranging from scaling legislative roll-call voting in the United States to text-based classifications of political parties in Europe. A particularly thorny problem remains estimating individual positions in legislatures with strong partisan discipline. We improve upon recently developed measurement strategies and propose a novel approach for estimating legislators’ ideological positions: an expert survey in which respondents compare pairs of representatives on a left-right dimension. The innovation of our approach lies in the combination of four particular features. First, we rely on political youth leaders who are insightful and easy to recruit. Second, the rating task does not involve numeric scaling and consists of simple pairwise comparisons. Third, we efficiently and automatically detect informative comparisons to reduce the cost and length of the survey without compromising our estimates. Fourth, we use a Bayesian Davidson model with random effects to generate an ideological position for each legislator. As an empirical illustration, we estimate the placement of the 709 members of the 19th German Bundestag. Several validity tests show that our model captures variation within and across political parties. Our estimates offer a thorough benchmark to validate alternative measurement strategies. The presented measurement strategy is flexible and easily extendable to diverse political settings because it can capture comparisons among political actors across time and space.

A crucial first step in helping consumers improve their financial lives is understanding their financial circumstances and well-being. The Financial Well-Being (FWB) scale measures a consumer’s subjective well-being related to aspects of their financial circumstances. It is available in standard-length (10-item) and abbreviated (5-item) versions, but no research has compared how completing either version may alter consumers’ responses. Notably, the 5-item scale includes a higher share of reverse-coded (i.e., negatively framed) items. We hypothesize that the difference in item framing between scale versions influences participants’ feelings about their financial situation, predicting that completing the 5-item FWB scale will result in more negative responses compared to completing the 10-item FWB scale. To test this hypothesis, we implement a randomized survey experiment using the Understanding America Study. In our experiment with nearly 6,000 participants, we find that completing the 5-item versus the 10-item FWB scale reduces FWB scores (average decline in the 5-item FWB score of 0.9 points, 95% CI [–1.552, –0.249]), and increases the share with a “low” 5-item FWB score by 5.0 percentage points, 95% CI [0.028, 0.071]), responses to individual scale items, and self-rated FWB. This pattern is strongest among lower-income respondents (average decline in FWB score of 2.3 points, 95% CI [–3.385, –1.171] and increases the share with a “low” 5-item FWB score by 8.1 percentage points, 95% CI [0.041, 0.121]). These findings highlight that FWB scale choice can have unexpected consequences. We discuss the implications for research on FWB and on the measurement of well-being more broadly.

Several methods used to examine differential item functioning (DIF) in Patient-Reported Outcomes Measurement Information System (PROMIS®) measures are presented, including effect size estimation. A summary of factors that may affect DIF detection and challenges encountered in PROMIS DIF analyses, e.g., anchor item selection, is provided. An issue in PROMIS was the potential for inadequately modeled multidimensionality to result in false DIF detection. Section 1 is a presentation of the unidimensional models used by most PROMIS investigators for DIF detection, as well as their multidimensional expansions. Section 2 is an illustration that builds on previous unidimensional analyses of depression and anxiety short-forms to examine DIF detection using a multidimensional item response theory (MIRT) model. The Item Response Theory-Log-likelihood Ratio Test (IRT-LRT) method was used for a real data illustration with gender as the grouping variable. The IRT-LRT DIF detection method is a flexible approach to handle group differences in trait distributions, known as impact in the DIF literature, and was studied with both real data and in simulations to compare the performance of the IRT-LRT method within the unidimensional IRT (UIRT) and MIRT contexts. Additionally, different effect size measures were compared for the data presented in Section 2. A finding from the real data illustration was that using the IRT-LRT method within a MIRT context resulted in more flagged items as compared to using the IRT-LRT method within a UIRT context. The simulations provided some evidence that while unidimensional and multidimensional approaches were similar in terms of Type I error rates, power for DIF detection was greater for the multidimensional approach. Effect size measures presented in Section 1 and applied in Section 2 varied in terms of estimation methods, choice of density function, methods of equating, and anchor item selection. Despite these differences, there was considerable consistency in results, especially for the items showing the largest values. Future work is needed to examine DIF detection in the context of polytomous, multidimensional data. PROMIS standards included incorporation of effect size measures in determining salient DIF. Integrated methods for examining effect size measures in the context of IRT-based DIF detection procedures are still in early stages of development.

A method is discussed which extends canonical regression analysis to the situation where the variables may be measured at a variety of levels (nominal, ordinal, or interval), and where they may be either continuous or discrete. There is no restriction on the mix of measurement characteristics (i.e., some variables may be discrete-ordinal, others continuous-nominal, and yet others discrete-interval). The method, which is purely descriptive, scales the observations on each variable, within the restriction imposed by the variable's measurement characteristics, so that the canonical correlation is maximal. The alternating least squares algorithm is discussed. Several examples are presented. It is concluded that the method is very robust. Inferential aspects of the method are not discussed.

It is common practice in IRT to consider items as fixed and persons as random. Both, continuous and categorical person parameters are most often random variables, whereas for items only continuous parameters are used and they are commonly of the fixed type, although exceptions occur. It is shown in the present article that random item parameters make sense theoretically, and that in practice the random item approach is promising to handle several issues, such as the measurement of persons, the explanation of item difficulties, and trouble shooting with respect to DIF. In correspondence with these issues, three parts are included. All three rely on the Rasch model as the simplest model to study, and the same data set is used for all applications. First, it is shown that the Rasch model with fixed persons and random items is an interesting measurement model, both, in theory, and for its goodness of fit. Second, the linear logistic test model with an error term is introduced, so that the explanation of the item difficulties based on the item properties does not need to be perfect. Finally, two more models are presented: the random item profile model (RIP) and the random item mixture model (RIM). In the RIP, DIF is not considered a discrete phenomenon, and when a robust regression approach based on the RIP difficulties is applied, quite good DIF identification results are obtained. In the RIM, no prior anchor sets are defined, but instead a latent DIF class of items is used, so that posterior anchoring is realized (anchoring based on the item mixture). It is shown that both approaches are promising for the identification of DIF.

Problem solving has been recognized as a central skill that today’s students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment. A computer log file records a student’s problem-solving process in details, including his/her actions and the time stamps of these actions. It thus provides rich information for the measurement of students’ problem-solving competency. On the other hand, extracting useful information from log files is a challenging task, due to its complex data structure. In this paper, we show how log file process data can be viewed as a marked point process, based on which we propose a continuous-time dynamic choice model. The proposed model can serve as a measurement model for scaling students along the latent traits of problem-solving competency and action speed, based on data from one or multiple tasks. A real data example is given based on data from Program for International Student Assessment 2012.

This article presents a joint modeling framework of ordinal responses and response times (RTs) for the measurement of latent traits. We integrate cognitive theories of decision-making and confidence judgments with psychometric theories to model individual-level measurement processes. The model development starts with the sequential sampling framework which assumes that when an item is presented, a respondent accumulates noisy evidence over time to respond to the item. Several cognitive and psychometric theories are reviewed and integrated, leading us to three psychometric process models with different representations of the cognitive processes underlying the measurement. We provide simulation studies that examine parameter recovery and show the relationships between latent variables and data distributions. We further test the proposed models with empirical data measuring three traits related to motivation. The results show that all three models provide reasonably good descriptions of observed response proportions and RT distributions. Also, different traits favor different process models, which implies that psychological measurement processes may have heterogeneous structures across traits. Our process of model building and examination illustrates how cognitive theories can be incorporated into psychometric model development to shed light on the measurement process, which has had little attention in traditional psychometric models.

The paper addresses three neglected questions from IRT. In section 1, the properties of the “measurement” of ability or trait parameters and item difficulty parameters in the Rasch model are discussed. It is shown that the solution to this problem is rather complex and depends both on general assumptions about properties of the item response functions and on assumptions about the available item universe. Section 2 deals with the measurement of individual change or “modifiability” based on a Rasch test. A conditional likelihood approach is presented that yields (a) an ML estimator of modifiability for given item parameters, (b) allows one to test hypotheses about change by means of a Clopper-Pearson confidence interval for the modifiability parameter, or (c) to estimate modifiability jointly with the item parameters. Uniqueness results for all three methods are also presented. In section 3, the Mantel-Haenszel method for detecting DIF is discussed under a novel perspective: What is the most general framework within which the Mantel-Haenszel method correctly detects DIF of a studied item? The answer is that this is a 2PL model where, however, all discrimination parameters are known and the studied item has the same discrimination in both populations. Since these requirements would hardly be satisfied in practical applications, the case of constant discrimination parameters, that is, the Rasch model, is the only realistic framework. A simple Pearson x2 test for DIF of one studied item is proposed as an alternative to the Mantel-Haenszel test; moreover, this test is generalized to the case of two items simultaneously studied for DIF.

A procedure for ordering object (stimulus) pairs based on individual preference ratings is described. The basic assumption is that individual responses are consistent with a nonmetric multidimensional unfolding model. The method requires data where a numerical response is independently generated for each individual-object pair. In conjunction with a nonmetric multidimensional scaling procedure, it provides a vehicle for recovering meaningful object configurations.