Airbnb has grown very rapidly over the past several years, with millions of tourists having used the service. The purpose of this study was to investigate tourists’ motivations for using Airbnb and...

Why Tourists Choose Airbnb: A Motivation-Based Segmentation Study:

Entrepreneurial intention among engineering students: The role of entrepreneurship education

The random intercept cross-lagged panel model (RI-CLPM) is rapidly gaining popularity in psychology and related fields as a structural equation modeling (SEM) approach to longitudinal data. It deco...

https://www.tandfonline.com/doi/pdf/10.1080/10705511.2020.1784738

Three Extensions of the Random Intercept Cross-Lagged Panel Model

Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.

/pdf/sample-size-for-survey-research-review-and-recommendations-4tzlfvj38o.pdf

Sample Size for Survey Research: Review and Recommendations

https://isiarticles.com/bundles/Article/pre/pdf/40718.pdf

Entrepreneurship Education at University Level and Students’ Entrepreneurial Intentions

PART ONE: GENERAL ORIENTATION TO RESEARCH IN BUSINESS AND MANAGEMENT Research, Statistics and Business Decisions Contrasting Philosophies and Approaches to Research Ethical Issues in Research Selecting the Topic and Conducting a Literature Review Theory, Problem Definition, Frameworks and Research Design PART TWO: ENTERING, DESCRIBING AND OBTAINING DATA How To Enter Data into SPSS and Undertake Initial Screening Describing and Presenting your Data Normal Distribution, Probability and Statistical Significance Sampling Issues Hypothesis Formation, Types of Error and Estimation Power and Effect Size PART THREE: STATISTICALLY ANALYSING DATA Hypothesis Testing for Differences between Means and Proportions Analysis of Variance Techniques (ANOVA) Chi Square Methods of Correlation Testing Hypotheses of Relationships Prediction and Regression Reliability and Validity Factor Analysis PART FOUR: SURVEY METHODS FOR RESEARCH IN BUSINESS AND MANAGEMENT Attitude Questionnaires and Measurement Structured Interview and Questionnaires Surveys PART FIVE: REPORTING AND PRESENTING RESEARCH Writing Up and Communicating Research

Business Research Methods and Statistics Using SPSS

Background The cross-lagged panel (regression) model (CLPM) is the usual framework of choice to test the longitudinal reciprocal effects between self-concept and achievement. Criticisms of the CLPM are that causal paths are over-estimated as they fail to discriminate between- and within-person variation. The random-intercept cross-lagged panel model (RI-CLPM) is one alternative that extends the CLPM by partialling out between-person variance. Aims We compare analyses from a CLPM and a RI-CLPM which examine the reciprocal relationships between self-concept, self-efficacy, and achievement and determine the extent CLPM estimates are inflated by between-person variance. Sample(s) Participants (n = 314) were first-year undergraduate psychology students recruited as part of the STudent Engagement with Education and Learning (STEEL) project. Methods Participants completed measures of self-efficacy and self-concept prior to completing fortnightly quiz assessments. Results Cross-Lagged Panel (regression) Model estimates are likely over-estimated in comparison with RI-CLPM estimates. Cross-Lagged Panel (regression) Model analyses identified a reciprocal effects relationship between self-concept and achievement, confirming established literature. In RI-CLPM analyses, these effects were attenuated and a skill development association between achievement and self-concept was supported. A reciprocal relationship between self-efficacy and achievement was supported. Better model fit was reported for the RI-CLPM analyses. Conclusions Prior findings relating to the reciprocal effects of self-concept and achievement need to be reconsidered. Whilst such a relationship was supported in a CLPM analysis in this study, within an RI-CLPM framework, only achievement predicted self-concept. However, in both CLPM and RI-CLPM models a reciprocal effects model of self-efficacy and achievement was supported.

Re-examining the reciprocal effects model of self-concept, self-efficacy, and academic achievement in a comparison of the Cross-Lagged Panel and Random-Intercept Cross-Lagged Panel frameworks.

Whilst many people around the world are becoming obese, some at an alarming rate (Organisation for Economic Co-operation and Development 2005), informed observers and researchers recognise that childhood obesity is the real problem to be tackled as research has shown that childhood obesity tracks with fidelity into adulthood. Advertisers are firmly in the line of fire in terms of allocating blame. This paper builds on systematic and other reviews of this area conducted in recent years and focuses on some salient issues raised by them which may prove fertile ground for a research program advancing knowledge in this complex field. The research agenda presented is highly focused, with a particular emphasis on the responsibility of marketing communicators.

Tackling Obesity: Developing a Research Agenda for Advertising Researchers

A hierarchical and multi-dimensional model of self-concept is well-validated. Despite increasing evidence that self-concept comprises two latent factors related to perceptions of ‘competence’ and ‘affect’, many researchers continue to examine the impact of a unitary self-concept on educational outcomes. This study explores evidence for a 2-factor academic subject-specific self-concept factor structure and examines the association between these factors with self-efficacy in a sample of higher education students. Participants from two studies (N = 314; N = 475) were enrolled in introductory psychology courses. Exploratory (EFA) and confirmatory (CFA) factor analysis examined and confirmed the factor structure of two oblique self-concept factors, reflecting affect and competence, in both studies. Temporal invariance of the 2-factor model was supported. Despite a substantial literature that discriminates between self-concept and self-efficacy, self-efficacy appears to be itself a facet of competency self-concept.

Competence and affect dimensions of self-concept among higher education students: a factorial validation study of an academic subject-specific self-concept

Robert B. Burns

Papers

Business Research Methods and Statistics Using SPSS

Re-examining the reciprocal effects model of self-concept, self-efficacy, and academic achievement in a comparison of the Cross-Lagged Panel and Random-Intercept Cross-Lagged Panel frameworks.

Tackling Obesity: Developing a Research Agenda for Advertising Researchers

Competence and affect dimensions of self-concept among higher education students: a factorial validation study of an academic subject-specific self-concept

Age and mental ability: re‐testing with thirty‐three years' interval