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Measurement invariance allows for the comparison and interpretation of scores obtained from the same scales at different times. In this respect, the purpose of this research is to examine the measurement invariance of the satisfaction with life scale applied to different age groups. The study employs the survey model, a type of quantitative research. The purposive sampling method was used in the research. This method is one of the non-random sampling types. This method collected data from 120 individuals under the age of 25 and 120 individuals over the age of 60 who resided in Ankara. The collected data were divided into two groups according to age: young and old. Measurement invariance was examined in four separate stages by performing Multiple Group Confirmatory Factor Analysis. These are configural, metric, scalar, and strict invariance. The data were examined to determine the configural, metric, and scalar invariance across different age groups by using multiple group confirmatory factor analysis. As a result, configural ([chi-squared]/df=0.84;p>0.05), metric ([chi-squared]/df=1.26; p>0.01), scalar ([chi-squared]/df=1.38;p>0.05), and strict ([chi-squared]/df=1.43;p>0.05) invariance are provided that the satisfaction with life scale has invariance properties for two different age levels. This research's application of the satisfaction with life scale scores to various age groups demonstrates measurement invariance and yields results with a similar structure.

An increasing number of K-12 students are being offered distance and remote teaching alternatives. As digital technology enables education to reach greater physical distances, to more students, and evolve into various instructional forms, it raises questions about the consistency between different distance and remote teaching modalities in terms of student learning experiences and the role of instructors. In this study, students' perceptions of the psychosocial environment are analyzed using transactional distance (TD) as a theoretical lens to understand their perceptions of closeness and/or distance in a remote teaching and learning environment. Qualitative and quantitative (descriptive) data were collected using a survey based on What Is Happening In this Class (WIHIC) and the Learning Climate Questionnaire (LCQ). The data represent 271 students (73% response rate) from 25 schools and 44 classrooms. Results show that aspects of TD vary between the learning environments, leading to various design issues for distance and remote teachers to consider. A prominent contribution is that challenges faced in the field of distance education are not solely technical in nature. While students' evaluations of technology may be quite similar, it is the interaction and perceived accessibility in the learning environment that significantly influence the learning experience. Based on the results, it is also concluded that survey could be used to strategically evaluate TD with the potential to develop remote teaching and learning practices in schools.

The purpose of this study was to quantify and compare interprofessional skills among first year pharmacy students who participated in healthcare and non-healthcare setting service-learning experiences. This was a survey-based pre/post comparison of interprofessional attitudes among student pharmacists during a first professional year service-learning course comparing two cohorts at healthcare-related sites versus non-healthcare sites. The Interprofessional Attitudes Scale (IPAS) was administered to students in fall 2021 and spring 2022. Healthcare and non-healthcare sites were compared using unpaired t-testing comparing the mean sums of Likert scores. This longitudinal cohort survey encompassed a total of 110 of 117 eligible first-year pharmacy students completed the pre-survey, (96% response rate), and 78 of 112 eligible students completed the post-survey (71% response rate). From pre to post survey, there was a positive increase of the mean Likert scores in all five IPAS subsections. There was no significant difference of mean Likert scores displayed between the sample of healthcare and non-healthcare students from the pre-survey to the post-survey in each of the five IPAS subsections. There was no difference in pre/post mean Likert scores of the five subsections of the IPAS in first-year pharmacy students, regardless of placement at healthcare or non-healthcare related sites.

This study reports on two key aspects relating to the use of the Online Learning Readiness Self-Check (OLRSC) survey, which has been proposed as identifying non-traditional students' readiness for online learning, and their strengths and weaknesses in six key areas. The first aspect validates the use of the instrument based on data from 199 students engaged in an online tertiary enabling course at a regional university in Australia. Factor analysis verified the scale structure of the instrument; however, two items were removed prior to the final analysis due to low communality and/or high cross loading with other items. This is followed by an examination of whether the instrument might be useful for the early identification of students who are at risk of disengagement from the enabling program. While it was hypothesised that the instrument, which measured factors such as the quality of interaction with peers and instructors, their capacity to manage technology and how well they managed learning, should have been a useful tool to identify early disengagement, the hypothesis was not supported. No significant associations were identified between any of the instrument's scales and early withdrawal from the course or completion of the first unit of study. Future recommendations for educators are made with a view to improving student engagement.

Research indicates that deceitful videos tend to spread rapidly online and influence people's opinions and ideas. Because of this, video misinformation via deepfake video manipulation poses a significant online threat. This study aims to discover what factors can influence viewers' capability to distinguish deepfake videos from genuine video footage. This work focuses on exploring deepfake videos' potential use for deception and misinformation by exploring people's ability to determine whether videos are deepfakes in a survey consisting of deepfake videos and original unedited videos. The participants viewed a set of four videos and were asked to judge whether the videos shown were deepfakes or originals. The survey varied the familiarity that the viewers had with the subjects of the videos. Also, the number of videos shown at one time was manipulated. This survey showed that familiarity with subjects has a statistically significant impact on how well people can determine a deepfake. Notably, however, almost two-thirds of study participants (102 out of 154, or 66.23%) were unable to correctly identify a sequence of just four videos as either genuine or deepfake. This study provides insights into possible considerations for countering disinformation and deception resulting from the misuse of deepfakes.

Purpose: There is little consensus in the literature regarding a) what it means for a school leader to lead with data, and b) how to measure data-informed leadership in a reliable and valid way. This study examines the psychometric properties of an operational measure intended to assess the extent to which a school leader is a data-informed school leader. The measurement invariance, reliabilities and construct and predictive validities of the "Data-Informed School Leadership Survey" (DISL Survey) are assessed using various psychometric statistical techniques. Methods: Using data collected from 155 teachers from 7 public schools in a southern state, the following psychometric statistics used to address our purpose: the Many-Facet Rasch (MFR) Model, Bayesian second-order Confirmatory Factor Analysis (CFA), Bayesian Structural Equation Modeling--Multiple Indicators, Multiple Causes analysis (Bayesian SEM-MIMIC), and reliability analysis. Findings: Results show an adequate fit from all MFR, Bayesian CFA, and MIMIC models and a high reliability (Cronbach [alpha] = 0.98). The DISL Survey instrument exhibits sound psychometric properties. Results likewise confirm the value of using MFR modeling and Bayesian methods to examine the psychometric properties of DISL Survey as a means of improving educational leadership measures. Implications for Research and Practice: Data from this study confirm the validity and reliability of the "Data-Informed School Leadership Survey" (DISL Survey) as an instrument to assess the strengths and weaknesses of Data-Informed School Leadership (DISL) and as a means for providing feedback for improving such leadership. Heretofore a measure for assessing this leadership was non-existent.

Objectives: Assessing functional impairment is one of the essential components in the clinical evaluation of ADHD in adulthood, serving both diagnostic and outcome evaluation purposes. However, clinicians and researchers may face challenges in selecting suitable instruments due to variations in accessibility and quality of instruments. Methods: We conducted an online survey involving an international group of 92 respondents engaged in clinical practice and/or research on ADHD. The survey aimed to evaluate current practices in assessing impairment in adult ADHD and related disorders, while also identifying areas requiring adaptation or potential new developments. Results: Our findings revealed that clinicians and researchers utilize a diverse range of instruments for assessing impairment in adults with ADHD, including some that may lack adequate properties for this purpose. Notably, dissatisfaction with current practice standards was expressed, underscoring the need for novel assessment approaches and improved psychometric properties. Conclusion: It is evident that research endeavors are warranted to either refine existing measures or devise new ones for assessing functional impairment in adult ADHD. Emphasis should be placed on disseminating instruments that enhance accessibility in both research and clinical settings, and facilitate streamlined administration and interpretation.

The high cost of health care in the United States creates complex decisions where suboptimal choices may negatively affect an individual's physical and financial health. The challenge for patients is that the complex nature of health-related financial decisions requires specialized knowledge to avoid these suboptimal choices. While the benefits of improved health literacy are well documented, the connection between health care and household finances may mean that there is a role for education and financial knowledge in improving outcomes. This study uses data from the Financial Industry Regulatory Authority's Financial Capability Survey in a binomial logistic regression to examine the role of education and financial knowledge in health-related financial decisions. The results show that both higher levels of education and financial knowledge are related to a higher likelihood of obtaining health insurance coverage and a lower likelihood of having medical debt and engaging in cost avoidance. Income disparities remain, however, especially for those in the middle-income brackets. These results raise the possibility that increases in general education and financial knowledge may improve health outcomes, but those facing the most complex health care decisions may need more specialized knowledge. This implies that given the connection between health care and personal finances, increasing objective and subjective knowledge may lead to better outcomes for patients' financial and physical health.

This mixed-methods study compares how secondary teachers implemented Education Reform in China in 2012 and 2022. The survey asked how often a teaching or evaluation strategy was used. The conclusions indicate that even though teacher-directed lessons (teacher talk, individual work, and questioning) still dominated, subject teachers were using a variety of student-centered learning (SCL) methods (activities and group work) and integration of technology (ICT) in accordance with reform initiatives. Surveys also revealed that classroom management needs decreased as a result of the reforms. The problem lies with the examination system, as there is little room for introducing reform initiatives.

Preparing students to enter the workforce and attracting and retaining good teachers are Americans' top educational priorities for the next administration in Washington, regardless of who wins the upcoming presidential election, the 2024 PDK Poll finds. While eight in 10 or more Americans pick those two items as priorities, other policy priorities also win broad support. Seven in 10--or more--favor an increased focus on addressing student mental health, helping students who have fallen behind academically, and college affordability. The survey was produced by Langer Research Associates for PDK International, with data collected June 21 to July 1, 2024, in English and Spanish, among a representative, random national sample of 1,009 adults.

Negative public perception of schools nationally impacts the teaching profession in direct and indirect ways. This perception has depressed the number of students in teacher preparation programs at universities. Indirectly, public perception affects teacher working conditions, compensation, and morale. Education advocates and researchers talk about the historical roots of perception of teachers, how and why negative perceptions persist, and how teachers can change the narrative.

PDK polls consistently show that parents give their children's schools high marks but are dissatisfied with the performance of public schools nationally. This paradox may be explained by the persistent narrative of failing public schools that has dominated media coverage in the past 50 years. Authors Mark Hlavacik and Jack Schneider tracked the prevalence of the failing-schools narrative in print publications, books, and other media. They found connections between national coverage of education and local education reporting. This national narrative of failing schools uses public schools as a scapegoat, which absolves us of our collective responsibility to each other. A more nuanced and multifaceted narrative of public schools is needed.

Lecture capture (LC) has been implemented globally to enhance accessible resources supporting academic success. Little is known about the effects of teaching students how to effectively use LC to augment learning. This correlational pilot study used a 14-item Likert-like survey to examine relationships between undergraduate LC viewing practices and final course grade. No statistically significant findings were identified between LC use and course grades. However, the mean course grade was 3.29% higher among students (n = 32) who implemented and maintained effective LC strategies (M = 90.61, SD = 5.68) compared with those who did not (n = 18). Six of the 14 LC survey items revealed significant positive correlations and two of the 14 LC survey items revealed significant inverse correlations. These findings could help educators prioritize teaching students how to effectively and efficiently use LC resources to enhance academic outcomes.

Teachers are crucial to bridging the theory-praxis divide in science education by utilising evidence-based teaching practices to improve outcomes for their learners. However, the perspectives of primary teachers have seldom been considered beyond the confines of specific professional development programs. This paper aims to explore Australian primary teachers' beliefs about how primary science education could be improved. A sample of 165 primary educators responded to an open-ended digital survey prompt. The results showed that teachers viewed themselves and their colleagues as central to the improvement of primary science education as evidenced by the most prominent themes of Professional Development (47.27%), Funding-Resources (37.58%), Classroom Practice (21.82%) and Personal-Teacher Improvement (21.21%). Curiously, university did not feature strongly, suggesting the participants may hold neutral views regarding the impact of universities on primary science education. The findings should serve as a catalyst for future research and engagement with primary teachers. Universities could expand their roles in building relationships with and providing accessible professional development to a group of primary teachers who, quite rightly, view themselves as key to improving primary science education.

There has been widespread media commentary about the potential impact of generative Artificial Intelligence (AI) such as ChatGPT on the Education field, but little examination at scale of how educators believe teaching and assessment should change as a result of generative AI. This mixed methods study examines the views of educators (n = 318) from a diverse range of teaching levels, experience levels, discipline areas, and regions about the impact of AI on teaching and assessment, the ways that they believe teaching and assessment should change, and the key motivations for changing their practices. The majority of teachers felt that generative AI would have a major or profound impact on teaching and assessment, though a sizeable minority felt it would have a little or no impact. Teaching level, experience, discipline area, region, and gender all significantly influenced perceived impact of generative AI on teaching and assessment. Higher levels of awareness of generative AI predicted higher perceived impact, pointing to the possibility of an 'ignorance effect'. Thematic analysis revealed the specific curriculum, pedagogy, and assessment changes that teachers feel are needed as a result of generative AI, which centre around learning with AI, higher-order thinking, ethical values, a focus on learning processes and face-to-face relational learning. Teachers were most motivated to change their teaching and assessment practices to increase the performance expectancy of their students and themselves. We conclude by discussing the implications of these findings in a world with increasingly prevalent AI.

The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individuals report how many others they know in the subpopulation of interest (e.g. "How many sex workers do you know?") and how many others they know in subpopulations of the general population (e.g. "How many bus drivers do you know?"). NSUM is widely used to estimate the size of important sociological and epidemiological risk groups, including men who have sex with men, sex workers, HIV+ individuals, and drug users. Unlike several other methods for population size estimation, NSUM requires only a single random sample and the estimator has a conveniently simple form. Despite its popularity, there are no published guidelines for the minimum sample size calculation to achieve a desired statistical precision. Here, we provide a sample size formula that can be employed in any NSUM survey. We show analytically and by simulation that the sample size controls error at the nominal rate and is robust to some forms of network model mis-specification. We apply this methodology to study the minimum sample size and relative error properties of several published NSUM surveys.

Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for the optimum design, we propose a new two-stage algorithm, the Fisher Information Maximizer (FIM), which utilizes estimates from generalized linear models to find a global optimal grouping scheme among all possible N-group schemes. After we define, decompose, and calculate different types of regressor-specific design errors, our analyses from both simulation and empirical examples suggest that: 1) the optimum design of GRC counts is able to reduce the grouping error to zero, 2) the performance of modified Poisson estimators using GRC counts can be comparable to that of Poisson regression, and 3) the optimum design is usually able to achieve the same estimation efficiency with a smaller sample size.

Adaptive survey design refers to using targeted procedures to recruit different sampled cases. This technique strives to reduce bias and variance of survey estimates by trying to recruit a larger and more balanced set of respondents. However, it is not well understood how adaptive design can improve data and survey estimates beyond the well-established post-survey adjustment. This paper reports the results of an experiment that evaluated the additional effect of adaptive design to post-survey adjustments. The experiment was conducted in the Detroit Metro Area Communities Study in 2021. We evaluated the adaptive design in five outcomes: 1) response rates, 2) demographic composition of respondents, 3) bias and variance of key survey estimates, 4) changes in significant results of regression models, and 5) costs. The most significant benefit of the adaptive design was its ability to generate more efficient survey estimates with smaller variances and smaller design effects.

Despite the widespread promotion of inclusive environments within higher education, social barriers and a lack of understanding among university staff continue to hinder students with disabilities in their goals of graduating and finding employment. Using ableism as a theoretical framework, this study aimed to explore attitudes among higher education institution employees towards students with disabilities and their feasibility in different types of education and employment positions. "Employees" in this study included teaching staff, student support, and administration. We conducted a factorial survey experiment in which respondents were invited to evaluate vignettes describing fictional students with and without disabilities. Other factors such as age, gender, ethnicity, and motivation were also included in the vignettes. A total of 2157 higher education employees across Norway participated in the survey and answered four questions regarding the likelihood of the student described in the vignette graduating, finding employment, and being suitable for relevant work tasks. Additionally, respondents were asked to rate how likely it was that they could make arrangements for the student during their studies. Results from multilevel regression analyses suggest that higher education employees are generally positive towards making arrangements for students with disabilities. However, despite this, students with disabilities were considered less likely to graduate, find employment, and less suitable in performing relevant work tasks in comparison to students without disabilities. We argue that ableist expectations continue to dominate perceptions of students within academia, and until ableism is addressed at an institutional level, higher education will remain far from inclusive.

This study analyzes the consequences of item nonresponse to the question about a household's total net income in the European Social Survey (2008-2018). We recognize two mechanisms in avoiding answering the income question: task complexity and question sensitivity, and apply multilevel logistic regressions to predict the probability of refusals or "Do not know" across respondents of different income levels. We find that the refusal to answer the income question is the highest for respondents with lower incomes, while the probability of selecting "Do not know" answers or refusal to answer is the same among respondents with higher incomes. The bias resulting from the correlation between response propensities and household income affects the accuracy of estimates for several attitudinal measures when income is included as an explanatory variable. We recommend reducing the risk of bias by limiting the complexity and sensitivity of the income question and accounting for nonresponse bias.

This article analyzes whether response patterns in surveys differ between the general population, regular immigrants, and recent refugees. Analyses show that the address quality of refugees contacted in the first wave of a panel study is worse than that of the general population, but of a similar quality to that of other recent immigrants. Once contacted, people in refugee households are more willing than others to participate in the first wave. In subsequent waves, this pattern changes. Address quality remains relatively low, and the motivation to participate deteriorates and is worse in comparison with other populations. However, Cox regression models of individual response behaviour reveal that this is mostly a composition effect. When socio-demographic and interviewer characteristics are taken into account, refugees have a lower risk of attrition than other immigrants, but they have a similar risk as the general population. This article provides important insights for the implementation of research about recent immigrants and refugees into ongoing panel studies.

The presentation of survey requests represents an easily modifiable feature of survey communications that can in some contexts affect response propensities. Here, we examine how two features: the framing of the participation request (informed by prospect theory) and the inclusion of targeted appeals based on demographic background (age or electoral district), affect participation rates in the 2021 Icelandic National Election Study, a mixed-mode cross-sectional survey. We find that these features generally fail to affect response propensities and sample composition. However, framed requests produced marginally higher response rates for a subset of the sample that was invited to complete the survey online. This suggests that modifying the presentation of the survey request may be less effective in cross-sectional surveys than in longitudinal ones, where more information is available to inform the content of targeted messages.

To estimate the distribution of the number of acquaintances of the members of a society, the network scale-up method asks survey respondents about the number of people they know with features for which national statistics are available. While many features have been used for this purpose, first names have been suggested to produce particularly low levels of transmission error and recall bias. For this method to be precise, a set of names needs to be selected for the survey that jointly represents the population in relevant variables such as gender or age. This article provides a solution approach to finding the optimal set of names. This can be applied to any population for which a joint distribution of first names and relevant variables is available. We show that our approach successfully provides sets of names closely mirroring the population distributions for six countries with different name statistics.

This survey investigates artificial intelligence (AI) literacy among academic library employees, predominantly in the United States, with a total of 760 respondents. The findings reveal a modest self-rated understanding of AI concepts, limited hands-on experience with AI tools, and notable gaps in discussing ethical implications and collaborating on AI projects. Despite recognizing the benefits, readiness for implementation appears low among participants. Respondents emphasize the need for comprehensive training and the establishment of ethical guidelines. The study proposes a framework defining core components of AI literacy tailored for libraries. The results offer insights to guide professional development and policy formulation as libraries increasingly integrate AI into their services and operations.

At the 2020 American String Teachers Association (ASTA) Conference held in Orlando Florida, an attendee of the Wellness Committee Listening Session recommended that a study be conducted to determine the prevalence and awareness of mental health concerns among students and teachers within the organization. Members agreed that there is more needed to be understood so that ASTA could provide professional development webinars, resources, as well as local, state, and national conference sessions. In response to this request and the clear need for more information, a survey was developed and deployed to the ASTA membership in the fall of 2020. Follow-up interviews with members who volunteered in response to an invitation on the survey were conducted in the spring and early summer of 2021. Teachers reported a high prevalence of student mental health issues prior to and during the pandemic and reported concerning levels of work overload, compassion fatigue/burnout, and mental health issues. Generational differences in mental health perceptions were noted, as were opportunities to provide targeted professional development and community-level services.

A growing body of literature suggests manipulating some graphing conventions can affect visual analysis and adherence to graphing conventions varies widely in single case design publications. Yet use of graphing conventions by the wider population of behavior analysts is largely unexplored. The current study examined relations among types of graphing conventions (established versus unestablished functions), demographic variables (e.g., education level, primary job setting), and likelihood of revision ratings reported by 631 board certified behavior analysts (BCBAs, BCaBAs, and BCBA-Ds). Statistically significant differences in ratings were detected across types of conventions and demographic categories. Master's level BCBAs in clinical roles were more likely to revise conventions that affect visual analysis than conventions whose functions are unknown, whereas there were no differences between types of conventions for doctoral level BCBAs in academic roles. We discuss these findings in the context of audience control of graphing conventions.

Here we present the development of the Mentoring in Undergraduate Research Survey (MURS) as a measure of a range of mentoring experienced by undergraduate science researchers. We drafted items based on qualitative research and refined the items through cognitive interviews and expert sorting. We used one national dataset to evaluate the internal structure of the measure and a second national dataset to examine how responses on the MURS related to theoretically relevant constructs and student characteristics. Our factor analytic results indicate seven lower order forms of mentoring experiences: abusive supervision, accessibility, technical support, psychosocial support, interpersonal mismatch, sexual harassment, and unfair treatment. These forms of mentoring mapped onto two higher-order factors: supportive and destructive mentoring experiences. Although most undergraduates reported experiencing supportive mentoring, some reported experiencing absence of supportive as well as destructive experiences. Undergraduates who experienced less supportive and more destructive mentoring also experienced lower scientific integration and a dampening of their beliefs about the value of research. The MURS should be useful for investigating the effects of mentoring experienced by undergraduate researchers and for testing interventions aimed at fostering supportive experiences and reducing or preventing destructive experiences and their impacts.

The Stranger Face Trust scale (SFT) and Imaginary Stranger Trust scale (IST) are two new self-report measures of generalized trust that assess trust in strangers--both real and imaginary--across four trust domains. Prior research has established the reliability and validity of SFT and IST, but a number of measurement validation tests remain. Across three separate studies, I assess the test-retest reliability, measurement invariance, predictive validity, and replicability of SFT and IST, with the misanthropy scale (MST) and generalized social trust scale (GST) serving as benchmarks. First, tests of internal consistency, test-retest reliability, and longitudinal measurement invariance established that all four generalized trust scales were acceptably reliable, with SFT and IST yielding greater overall reliability than MST and GST. Second, tests of multiple group measurement invariance revealed that SFT and IST were equivalent across gender, race, education, and age groups, while MST and GST were non-equivalent across the same sociodemographic groups. Third, an investment game established the predictive validity of SFT and MST, with IST and GST yielding poor predictive validity. Fourth, tests of factor structure and measurement invariance indicated that all four generalized trust scales replicated across samples. The present findings bolster the validity, reliability, and measurement equivalence of SFT and IST, while illustrating the compromised validity and measurement non-equivalence of MST and GST. Implications for the measurement of generalized trust are discussed.

Individual religiosity measures are used by researchers to describe and compare individuals and societies. However, the cross-cultural comparability of the measures has often been questioned but rarely empirically tested. In the current study, we examined the cross-national measurement invariance properties of generalized individual religiosity in the sixth wave of the World Values Survey. For the analysis, we used multiple group confirmatory factor analysis and alignment. Our results demonstrated that a theoretically driven measurement model was not invariant across all countries. We suggested four unidimensional measurement models and four overlapping groups of countries in which these measurement models demonstrated approximate invariance. The indicators that covered praying practices, importance of religion, and confidence in its institutions were more cross-nationally invariant than other indicators.

The relation between reading and mathematics achievement is an active area of research, and evidence in the extant literature suggests they are closely related. However, there is a lack of understanding about how their relation develops over time. Indeed, traditional longitudinal analyses have focused on investigating each domain independently, which misses the opportunity to examine important relations between the two simultaneously as they change over time. Thus, in this study we investigate the joint development of reading and mathematics achievement and the association between their longitudinal trajectories modeled as piecewise linear phenomena using a Bayesian inference approach. We used a random sample (n = 1000) from the Early Childhood Longitudinal Study, Kindergarten class, which followed a cohort of children from kindergarten through middle school, where 49% were male and 76% were in public schools. The findings showed that both educational domains were positively associated along their piecewise linear trajectories, however, the magnitude of association decreased over time. Moreover, a focus on distinct developmental phases highlighted the reciprocal relation between reading and mathematics.

Retrospective alcohol use data are prone to recall bias, a limitation that could be addressed with real-time ecological momentary assessment (EMA) tools. We aimed to (1) introduce a simple (single-click) EMA methodology for collecting real-time alcohol use data, and (2) investigate the EMA methodology's performance relative to established alcohol use data collection tools. In March-April 2021, we sampled undergraduate students (n = 84) and collected a week of alcohol use data. Participants entered their real-time drinking start times using our EMA methodology, self-reported their drinking details in daily surveys, and a subsample recorded their breath alcohol concentration (BrAC) using smart breathalyzers. We estimated the accuracy of our EMA methodology in collecting alcohol use data relative to data collected by daily surveys and breathalyzers. Overall, 199 drinking events were recorded with the EMA methodology. Numbers of drinks recorded with the EMA methodology were correlated with self-reported daily surveys (r = 0.82, p < 0.001) and BrAC readings (r = 0.69, p < 0.001). Sensitivity and specificity of the EMA methodology in detecting heavy drinking relative to daily surveys were 82% (95% CI [67%, 92%]) and 97% (95% CI [85%, 100%]), respectively. These were 74% (95% CI: [64%, 83%]) and 92% (95% CI: [85%, 96%]) for binge drinking. Similar results were found when we used breathalyzers as the reference standard test. We developed an EMA methodology for collecting real-time alcohol use data (alcohol drinking start-time, frequency, magnitude, patterns, and pace). Our findings support the utility of our EMA methodology in collecting alcohol use data among college students.

Although questionnaire surveys are widely applied in second-language research, there is still little research on the application of questionnaire among early career researchers. Problems such as the lack of details concerning why the questionnaire survey is adopted, how the questionnaire is constructed, and how the survey is administered and reported are prevalent in student teachers' research proposals or theses, which greatly impairs the quality of their academic work. However, few studies have been conducted to address these problems. This study aims to explore the real situation of the questionnaire survey application by examining student teachers' perceptions of a questionnaire survey and their application of this research method to their theses. Using a mixed-method approach, the study collected quantitative and qualitative data from 138 student teachers, including 103 fresh undergraduates and 35 post-graduates. Quantitative data analysis used descriptive statistics, while qualitative data analysis used semi-structured interviews and content analysis. The study found that: (1) student teachers had some basic but unsystematic perceptions of the questionnaire survey. They had insufficient awareness of the rules of writing good questions, piloting questionnaires, sampling strategies, doing reliability, and validity analysis, etc. (2) Most student teachers developed questionnaires based on existing questionnaires without explaining reasons and adjustments. They introduced questionnaire design in their theses in an incomplete way, with piloting, selecting sample strategies, and statistical analysis methods often missing. Thus, some pedagogical implications can be drawn to improve student teachers' awareness and competencies of the questionnaire survey.