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disasters. These events, such as earthquakes, floods and fires, also affect people's consumption behaviors. In this study, it is aimed to examine the effects of earthquakes from natural disasters on certain consumption behaviors. With the qualitative data collection method, after the earthquake that occurred in Turkey on February 6, 2023 and was described as the “Disaster of the Century”, data were collected about the clothing purchases of earthquake victims and adult women who felt the earthquake and the data obtained were subjected to content analysis. In the research, it was tried to measure the consumer behaviors and attitudes of these women living in Kırıkkale in clothing shopping after the earthquake. In the research, it was tried to measure the consumer behaviors and attitudes of these women living in Kırıkkale in clothing shopping after the earthquake. As a result of the research, it was determined that the women who were exposed to the earthquake and felt the earthquake tried to meet their and their family's clothing needs cheaper by taking various vocational courses / training in order to produce them instead of buying them instead of buying them due to the increasing prices in the clothing sector as in many sectors after the earthquake, that they could increase or decrease their unplanned purchases, and that they shopped too much unnecessary or more clothing than they needed before the earthquake. It has been seen that people's interest in vocational courses / trainings has increased after disasters such as earthquakes, and suggestions have been given to the relevant institutions.

Copyright of Dynamics in Social Sciences & Humanities is the property of Ataturk University Coordinatorship of Scientific Journals and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article deals with the evaluation of the economic performance of domestic enterprises under foreign control, especially by subjects from Eastern European countries currently involved in the war conflict in Ukraine, through several key indicators monitored by Inward Foreign Affiliates Statistics (IFATS) and compiled by the Czech Statistical Office (CZSO). The war conflict in Ukraine raises, among other, questions about the effects of sanctions against aggressor countries. IFATS could serve as one possible source for evaluation of their impact with new data coming in the future. In the article, selected data from the IFATS database will be compared for the economy as a whole and according to the specific prevailing economic activities as well. Although the contribution of enterprises controlled by subjects from both aggressor countries and Ukraine to the Czech economy is quite marginal, the latter ones are among the most represented in terms of their number that is still growing. However, their economic importance grows much slower.

Recent discussions in education, industry, and government have focused on the need for growth and diversity in STEM fields. STEM education and practice directly contribute to the economic vitality of a nation and benefit its citizens. Yet, STEM education and employment growth seem lopsided concerning both gender and diversity. While researchers have studied various dimensions of this phenomenon, this paper seeks to add to the knowledge base by analyzing the effects of gender and college major on performance and attitudes in statistics-related courses. T-tests, one-way analysis of variance, and multiple regression were used to investigate the effects of gender, major, and attitude on performance in business statistics courses. Results indicate that, in the business statistics course, there were no significant differences between the average score of male students and female students in 2 of 3 semesters. In the marketing research course, where similar statistical concepts as taught in the business statistics course were adopted, results were similar. However, there were differences in the students’ scores when their academic majors were considered. Findings from this study can contribute to developing effective and innovative pedagogical methodologies to teach statistics and related subjects.

This quasi-experimental study examines the effect of short instruction videos on students' business statistics learning. Two hundred and thirty-one Dutch students attended 6-week online seminars on Business Statistics. One hundred and nineteen students were in an experimental group, and 112 in a control group. Students in the experimental group watched short instructional videos and studied online quizzes at their own pace. In the control group, students followed teachers' instructions throughout the seminars. It was found students watching short videos significantly outperformed those following teachers' virtual instruction. Short videos were especially useful for those who were good at math. The research sheds light on the design of hybrid learning, particularly for business statistics education at the university level. A quasi-experimental research to examine the effects of short instructional videos on students' statistics learning performance vs. the virtual lectures with teachers. Evidence of the benefits of short videos in statistics education for students who are good at math. Practical experiences sharing for designers of instructional videos. Recommendations for creating short instructional videos in higher education. [ABSTRACT FROM AUTHOR]

Analysis of business studies often involves the quantification of qualitative data to derive meaningful insights and making informed decisions. One such challenge is the inappropriate use of the arithmetic mean in economic and financial modeling. The arithmetic mean is a widely used statistical measure of central tendency that sums up a set of values and divides it by the total number of observations. While the arithmetic mean is simple and intuitive, its appropriateness in financial and economic modeling highly depends upon the nature of the data and the specific research question being addressed. This creates a dilemma. Despite the business community traditionally emphasizing quantitative research modeling, the growth of artificial intelligence and big data make qualitative research more desirable, particularly in areas such as ESG scorecards and financial literacy surveys. This paper discusses the challenges presented with analyzing studies after quantifying qualitative data and provides examples of how ordinal regression and other techniques could be used to analyze qualitative variables. This is especially applicable in undergraduate education. [ABSTRACT FROM AUTHOR]

The article deals with the evaluation of the economic performance of domestic enterprises under foreign control, especially by subjects from Eastern European countries currently involved in the war conflict in Ukraine, through several key indicators monitored by Inward Foreign Affiliates Statistics (IFATS) and compiled by the Czech Statistical Office (CZSO). The war conflict in Ukraine raises, among other, questions about the effects of sanctions against aggressor countries. IFATS could serve as one possible source for evaluation of their impact with new data coming in the future. In the article, selected data from the IFATS database will be compared for the economy as a whole and according to the specific prevailing economic activities as well. Although the contribution of enterprises controlled by subjects from both aggressor countries and Ukraine to the Czech economy is quite marginal, the latter ones are among the most represented in terms of their number that is still growing. However, their economic importance grows much slower. [ABSTRACT FROM AUTHOR]

An increasing number of employers are seeking college graduates with data analysis skills. However, research suggests that students have an aversion to statistics courses and avoid utilizing statistical tools in the workplace. Project-Based Learning has been proven to increase student engagement and achievement, particularly in courses with a heavy mathematical component. To bridge the gap between employer wants and student sentiment related to statistics, instructors of applied business statistics at a regional comprehensive public university implemented a group project that utilized real-word data to answer a research question. This paper examines if and through what channels this group project impacted student performance and knowledge using multiple linear regression models. Results indicate that inclusion of the group project is associated with higher final grades but lower average exam scores, while controlling for student ability using student cumulative grade point average as a proxy. Focusing on the students that completed the project, a higher project grade corresponds to both improved final grades and average exam scores, implying that the project solidifies learning of course concepts. [ABSTRACT FROM AUTHOR]

This study proposes the teaching-by-examples (TBE) approach where the learning objectives are achieved by creating lesson plans encompassing hands-on examples that take precedence in operationalizing the concepts and theories. It illustrates the TBE approach within the context of business statistics and proposes that there is a significant difference in the mean performance of students taught by the TBE approach and of those taught by the traditional lecture method. The findings suggest that the TBE approach results in better student performance than the traditional lecture method. This study also discusses the challenges that accompany the benefits of the TBE approach. [ABSTRACT FROM AUTHOR]

Due to the full-scale outbreak of COVID-19, many universities have adopted the way of online teaching to ensure the orderly development of teaching plans and teaching contents. However, whether online and offline teaching can develop homogeneously and how to ensure the teaching effect is a major challenge for colleges and universities. Therefore, it is urgent to construct a reasonable index system and evaluation approach for the quality of network teaching. Combined with the influencing factors and characteristics of online teaching, this study first puts forward a multi-index evaluation index system and then proposes a novel evaluation method for online teaching based on the analytical hierarchy process (AHP) and Dombi weighted partitioned Muirhead Mean (PMM) operator under Fermatean fuzzy (FF) environment. This presented method not only adapts to changeable evaluation information but also handles the elusive interrelationships among indexes, realizing the flexibility and comprehensiveness both in form and in the polyaddition process. The applicability and feasibility of this presented method are then discussed through the practical online teaching quality evaluation of a business statistics course case, and a group of tentative about the sensitivity analysis and comparative analysis further demonstrates the effectiveness and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

Effective programming of research and development (R&D) support, adjusted to the actual potential of beneficiaries, requires the use of modern analytical tools. An efficient R&D support system requires up-to-date data on technological trends, ongoing (and planning) research, market needs and developing innovation. The most popular programming methods were based on the analysis of data with a 4 to 5-year time delay until recently. Having described the method of refining information from unstructured data, we explore how to make it possible not only to solve the issue of up-to-date data but to identify of the latest trends in R&D activities. The analytical tools we describe were already fully functional in 2018 and are constantly being improved. The article presents the potential of one tool that can be applied in public support institutions. Methods of identifying and diagnosing technology trends are presented within the case study of the electric car technology trend. The presented case study shows the effectiveness of the method we developed for identifying and diagnosing areas requiring support from public funds. Public institutions, including public institutions supporting R&D and innovation processes, can apply tools that allow an increase in the quality of public support programmes offered, but also beneficial for the quality of strategic resources management within the institution itself. The comparison of the predictions made by the described tools with the classifications made by experts, the former are more accurate and precise. Moreover, the results of the analyses performed by the presented model are not influenced by distorting factors—fads, trends, political pressures, or processes with an unidentified, non-substantive background. It should be emphasized that the accuracy of the whole model is 0.84. The described tools and methods are already directly applicable in many areas related to the support of R&D activity worldwide. The article presents a solution that effectively enables the management of more precise programmes supporting innovative activities used for the first time in Poland. It is also one of the first uses of these methods by public administration in the world. Our approach not only strengthens improved adjustment of the support offered for R&D activity, but also makes it possible to apply and improve management methods in public institutions. [ABSTRACT FROM AUTHOR]

Currently, statistics on Research and Development (R&D) carried out in the business sector are computed in France on the sole basis of legal units: firstly, a survey is addressed to them to collect the data and then, statistics on R&D are disseminated at legal unit level. Considering the increasing importance of the enterprise group in the French economy, it seems difficult today to go on using only the legal units to calculate business statistics. Indeed, assimilating the legal unit to the enterprise is not relevant anymore for group's affiliates and subsidiaries. Taking into account the European definition of an enterprise will help to disseminate more consistent and relevant R&D statistics on the business sector. The French business statistical register established by the French National Statistical Institute (INSEE), called SIRUS, contains notably all the legal units and all the enterprises. The main contribution of this register is to make possible the calculation and dissemination of statistics at another level than the legal unit one: the enterprise level. This article first describes why the data should go on being collected at the legal unit level and not at the enterprise one. Indeed, it seems that such a change in the data collection can be dangerous because it could result in a substantial increase of the response burden. Then, this article presents the process based on SIRUS that leads to the computation of key indicators on R&D at enterprise level. To conclude, it compares these key indicators with the ones calculated at the legal unit level to show the impact of moving to the enterprise level on French R&D statistics.

This article explains an Excel based simulation approach applied to teaching foundational concepts in chi-square goodness of fit hypothesis tests. Students gain hands-on experience using a popular, multipurpose methodology for analyzing whether observed counts are statistically different from expectations. Such applications commonly appear in a variety of areas in business such as market share changes and training effectiveness programs, for example. Our approach allows students to experience for themselves a demonstration of important foundations regarding the methodology. We have found for business students, non-theoretical approaches such as this are effective. In this paper, we describe how to deploy an interactive Excel simulation to bring sometimes esoteric statistical theory to life using a commonly taught methodology. [ABSTRACT FROM AUTHOR]

Research on learning environments at the higher-education level has been quite sparse compared with studies at other educational levels. Because statistics is perceived as a difficult subject across disciplines, it suffers from low passing rates in many universities. This study involved validating questionnaires for assessing the psychosocial environment and student attitudes associated with learning business statistics in computing laboratory workshops. The Business Statistics Computer Learning Environment Inventory (BSCLEI) and Attitude to Business Analytics instrument were validated with 275 students enrolled across various business degree programs in the United Kingdom over two academic years. Various data analyses (including exploratory and confirmatory factor analyses) supported the validity of these two questionnaires, thereby paving the way for their future use in research and practical applications relevant to learning environments in higher-education statistics workshop classrooms. [ABSTRACT FROM AUTHOR]

Due to the full-scale outbreak of COVID-19, many universities have adopted the way of online teaching to ensure the orderly development of teaching plans and teaching contents. However, whether online and offline teaching can develop homogeneously and how to ensure the teaching effect is a major challenge for colleges and universities. Therefore, it is urgent to construct a reasonable index system and evaluation approach for the quality of network teaching. Combined with the influencing factors and characteristics of online teaching, this study first puts forward a multi-index evaluation index system and then proposes a novel evaluation method for online teaching based on the analytical hierarchy process (AHP) and Dombi weighted partitioned Muirhead Mean (PMM) operator under Fermatean fuzzy (FF) environment. This presented method not only adapts to changeable evaluation information but also handles the elusive interrelationships among indexes, realizing the flexibility and comprehensiveness both in form and in the polyaddition process. The applicability and feasibility of this presented method are then discussed through the practical online teaching quality evaluation of a business statistics course case, and a group of tentative about the sensitivity analysis and comparative analysis further demonstrates the effectiveness and flexibility of the proposed method.

To meet the growing demand for detailed, precise, accurate and timely estimation of entrepreneurship and economic conditions, it is necessary to systematically extend the scope of information provided by business statistics. In view of the policy aimed at reducing survey costs and burdens for business units, the only way in which this objective can be achieved is by modernizing survey methodology. One area where this kind research is being conducted are applications of indirect estimation based on auxiliary sources of information from administrative sources. Hence, the purpose of the study described in this article is to evaluate the precision of estimates of revenues of small businesses for domains defined by spatial aggregation and business classification by applying stratification in composite estimators based on information collected from administrative registers.

National Statistical Institutes (NSIs) strive to produce short-term business statistics (STS) indicators with the high quality estimates in a timely manner. NSIs are usually faced with the challenges, such as differences in definitions, incompleteness of administrative data, periodicity and timeliness, coverage issues, etc. Administrative Value Added Tax (VAT) turnover data can be used to partially or completely replace survey data for the estimation of short-term business turnover indicators. In this paper, main characteristics of administrative VAT turnover data in Bosnia and Herzegovina will be examined through cleaning of VAT turnover data and matching them with survey data. Hence, the main objective of this study is to investigate the relationship between VAT turnover data and survey data in in Bosnia and Herzegovina. The Monthly Retail Trade Turnover Indices (RTI) for in Bosnia and Herzegovina will be estimated by using administrative VAT turnover data. Spearman’s correlation coefficients are used to examine the presence of a linear relationship between VAT turnover data and survey data. Results gained by using survey and administrative VAT turnover data will be compared. Based on the results of the analysis, future challenges and perspectives for expansion of using administrative VAT turnover data will be identified.

This paper highlights the key characteristics and implications of the strategic and data production frameworks designed and progressively implemented by the United Nations Committee of Experts on Business and Trade Statistics (UNCEBTS) to enhance the relevance, accuracy and coverage of business statistics, according to an internationally comparable, result-oriented and sustainable approach. The strategic framework aims to expand the traditional scope of official business statistics by including all relevant environmental and social related issues. NSOs may achieve relevant improvements by focusing their efforts upon specific global goals consistent with their national ones, and sourcing from knowledge sharing with other countries and international coordination. It also highlights the relevance of an enterprise-centered approach for a better understanding of emerging phenomena by official statisticians, and for priority setting in improving the quality of business statistics. The data production framework is dominated by the crucial role of the Statistical Business Register (SBR) as the backbone of any current and future improvements in the relevance and accuracy of business statistics. Its implications, both in terms of sustainability of production lines, data integration and production of new indicators that exploit the variability dimension of business statistics are further investigated in the paper. [ABSTRACT FROM AUTHOR]

Classification of enterprises by main economic activity according to NACE codes is a challenging but important task for national statistical institutes. Since manual editing is time-consuming, we investigated the automatic prediction from dedicated website texts using a knowledge-based approach. To that end, concept features were derived from a set of domain-specific keywords. Furthermore, we compared flat classification to a specific two-level hierarchy which was based on an approach used by manual editors. We limited ourselves to Naïve Bayes and Support Vector Machines models and only used texts from the main web pages. As a first step, we trained a filter model that classifies whether websites contain information about economic activity. The resulting filtered data set was subsequently used to predict 111 NACE classes. We found that using concept features did not improve the model performance compared to a model with character n-grams, i.e. non-informative features. Neither did the two-level hierarchy improve the performance relative to a flat classification. Nonetheless, prediction of the best three NACE classes clearly improved the overall prediction performance compared to a top-one prediction. We conclude that more effort is needed in order to achieve good results with a knowledge-based approach and discuss ideas for improvement. [ABSTRACT FROM AUTHOR]

Currently, statistics on Research and Development (R&D) carried out in the business sector are computed in France on the sole basis of legal units: firstly, a survey is addressed to them to collect the data and then, statistics on R&D are disseminated at legal unit level. Considering the increasing importance of the enterprise group in the French economy, it seems difficult today to go on using only the legal units to calculate business statistics. Indeed, assimilating the legal unit to the enterprise is not relevant anymore for group's affiliates and subsidiaries. Taking into account the European definition of an enterprise will help to disseminate more consistent and relevant R&D statistics on the business sector. The French business statistical register established by the French National Statistical Institute (INSEE), called SIRUS, contains notably all the legal units and all the enterprises. The main contribution of this register is to make possible the calculation and dissemination of statistics at another level than the legal unit one: the enterprise level. This article first describes why the data should go on being collected at the legal unit level and not at the enterprise one. Indeed, it seems that such a change in the data collection can be dangerous because it could result in a substantial increase of the response burden. Then, this article presents the process based on SIRUS that leads to the computation of key indicators on R&D at enterprise level. To conclude, it compares these key indicators with the ones calculated at the legal unit level to show the impact of moving to the enterprise level on French R&D statistics. [ABSTRACT FROM AUTHOR]

Purpose – the purpose of this article is to review a quasi-experiment study examining whether business students’ math anxiety is reduced after participating in mandala coloring activities. Research methodology – the research methodology integrated quantitative methods including independent t-tests and ANOVAs in a non-random convenient sample of 106 undergraduate students in 2018 in Texas, United States. Findings – results from the one-way ANOVA and t-test analyses revealed that anxiety levels differed across groups, such that after coloring a pre-drawn mandala, math anxiety was significantly reduced in comparison to the control (doodling) group. Paired sample t tests also demonstrated that when comparing the anxiety levels at the baseline and post-treatment, math anxiety was reduced after performing both the pre-drawn and free-coloring mandala activities. Additionally, an independent sample t-test and a two-by-two factorial ANOVA demonstrated that males experienced a significant reduction in their math anxiety than the females did after performing the mandala coloring activity. Research limitations – the study used a convenient sample, self-reported items, and a math anxiety measurement. Also, the findings found short-term evidence of math anxiety. Practical implications – the findings of this study suggest that business statistics instructors who integrate a mandala coloring activity in anxiety-provoking undertakings may help to reduce their students’ math anxiety. Originality/Value – This study is the first to investigate mandala coloring to reduce math anxiety in business students. Unlike previous studies that focus on anxiety in general, this study examines the benefit of mandala coloring on students’ math anxiety.

This article questions the quality of intertwined teaching and learning of business statistics with a focus on student learning. It claims that the “system” and instructors together are spoiling the students. The former adversely restricts the students’ ability to develop learning skills, which yields a lower quality of learning than expected. The article starts with reported facts on student failure in a business statistics course across the author’s university, including low class performance statistics despite new teaching methods and reforms suggested in previously published literature. It qualitatively contrasts present-day teaching and learning to that of the 1960s and 1970s, which let students self-discover how to learn. It also quantitatively measures student learning difficulty and its root causes. The article presents an experimental change in instructors’ teaching and learning in order to achieve higher student success rates and advancement. Although the article is constructed based on our own data in our own college environment, it might also be of some value to others. [ABSTRACT FROM AUTHOR]

While statistics is an essential topic for business students, many students experience barriers to successful learning due to anxiety, motivation, or difficulty with quantitative understanding. This research examines the use of online videos with an effort to explore the relationship between student characteristics, video usage, and course performance measures. In addition, the study examines student perceptions of the supplemental resources in an effort to examine the efficacy related to student learning and performance. This study seeks to confirm previous research regarding the benefit of supplemental video resources and extend the knowledge base by understanding the impact for various types of students (e.g., level of course, level of previous academic performance, etc.). The study found that the use of the video resources varies based on students' previous math performance and the difficulty of the material. Student ratings indicate that the videos were helpful in learning the content and that they provided an important resource when students needed additional tools to master the material. Analysis of the results provides implications for understanding how different students use online videos and offers recommendations for educators and researchers about how to enhance student success in difficult statistics coursework. [ABSTRACT FROM AUTHOR]

AACSB has now mandated that analytics be integrated into the undergraduate business curriculum. Given that the subject of statistics provides the underpinnings of the developing discipline of business analytics, this article focuses on effective course delivery aimed at enhancing the learning of introductory business statistics in the modern world of big data. To this end, the choice and use of computational software tools are essential to the successful delivery of the course. A set of seven competing tools are compared and contrasted, their advantages and disadvantages are discussed, and a link is provided to show demonstrations of needed instructions and resulting output. [ABSTRACT FROM AUTHOR]

Purpose -- the purpose of this article is to review a quasi-experiment study examining whether business students' math anxiety is reduced after participating in mandala coloring activities. Research methodology -- the research methodology integrated quantitative methods including independent t-tests and ANOVAs in a non-random convenient sample of 106 undergraduate students in 2018 in Texas, United States. Findings -- results from the one-way ANOVA and t-test analyses revealed that anxiety levels differed across groups, such that after coloring a pre-drawn mandala, math anxiety was significantly reduced in comparison to the control (doodling) group. Paired sample t tests also demonstrated that when comparing the anxiety levels at the baseline and post-treatment, math anxiety was reduced after performing both the pre-drawn and free-coloring mandala activities. Additionally, an independent sample t-test and a two-by-two factorial ANOVA demonstrated that males experienced a significant reduction in their math anxiety than the females did after performing the mandala coloring activity. Research limitations -- the study used a convenient sample, self-reported items, and a math anxiety measurement. Also, the findings found short-term evidence of math anxiety. Practical implications -- the findings of this study suggest that business statistics instructors who integrate a mandala coloring activity in anxiety-provoking undertakings may help to reduce their students' math anxiety. Originality/Value -- This study is the first to investigate mandala coloring to reduce math anxiety in business students. Unlike previous studies that focus on anxiety in general, this study examines the benefit of mandala coloring on students' math anxiety. [ABSTRACT FROM AUTHOR]

A case study based on the social media adoption inquiry of an eyewear company are presented. The case study primarily focuses on summary statistics and provides a structured outline for creating various types of tables and graphs for qualitative and quantitative data analysis. More specifically, guidelines for calculating numerical measures such as mean, median, range, variance, standard deviation, correlation as well as making observations and recommendations are provided with regards to the case. The case can serve as an effective hands-on project to assess student mastery of descriptive statistics while fostering critical thinking skills in the areas of social sales and social media marketing management. An instructor's manual detailing recommendations for student case report requirements is also provided as a resource for effective course integration. [ABSTRACT FROM AUTHOR]

In the face of dynamic changes in the economy, there is a growing demand for multivariate statistics for cross‑classified domains. In economic statistics, this demand poses a particular challenge owing to the unique character of the population of enterprises, which is what motivates the search for estimation methods that can exploit administrative sources to a greater extent. The adoption of new solutions in this area is expected to increase the scope of statistical outputs and improve the efficiency of estimates. The purpose of the presented study is to test the application of the robust GREG estimator based on the LS method and least median of squares regression to estimate characteristics of small trade firms operating in 2012. The estimation process is supported with delayed variables from administrative registers used as auxiliary variables. The paper refers to small area estimation methods. The variables of interest are estimated at the low level of aggregation represented by cross‑section province and NUTS 2.

Python has become one of the leading programming languages used by Data Scientists. As such, Python has also become a highly marketable skill for business students entering the job market. This paper describes efforts to incorporate statistical computing with Python into a second semester undergraduate business statistics course in which most incoming students have limited programming experience. Two free learning platforms, datacamp.com and repl.it, are trialed among students with mixed but generally favorable results in terms of classroom utility and student learning outcomes. An analysis of course evaluations indicates some students are reluctant to embrace statistical computing, preferring a traditional mathematics class structure, while others are grateful for the opportunity to learn new marketable technology skills. [ABSTRACT FROM AUTHOR]

Stats NZ has moved to an 'administrative data first' approach for quarterly financial statistics. This paper gives an overview of the transformation process, describes some of the methods and techniques used, as well as outlines the benefits, such as increased flexibility, improved quality, and reduced respondent burden that we have realised in adopting this approach. Some of the challenges faced with this significant paradigm shift, particularly the changes in thinking needed are discussed, as well as the ongoing commitment required to maintain this 'administrative data first' approach. [ABSTRACT FROM AUTHOR]

ABSTRACT: Because business statistics students often experience a lack of statistical motivation, business educators need to understand the factors that influence students’ motivation levels in undergraduate‐level business statistics courses. The purpose of this study was to examine the role of math self‐efficacy and self‐compassion on students’ motivation levels and the gender differences among the main variables. The regression results showed that math self‐efficacy was a positive predictor of statistics motivation. Self‐compassion was also found to be a partial mediator of the relationship between math self‐efficacy and statistics motivation. Gender differences were also found in business students’ math self‐efficacy, self‐compassion, and statistics motivation. [ABSTRACT FROM AUTHOR]