scholarly journals THE IMPORTANCE OF ATTITUDES IN STATISTICS EDUCATION

2012 ◽  
Vol 11 (2) ◽  
pp. 57-71
Author(s):  
CAROLINE RAMIREZ ◽  
CANDACE SCHAU ◽  
ESMA EMMİOĞLU

People forget what they do not use. But attitudes “stick.” Our article emphasizes the importance of students’ attitudes toward statistics. We examine 15 surveys that purport to assess these attitudes and then describe the Survey of Attitudes Toward Statistics, a commonly used attitude survey. We present our conceptual model of Students’ Attitudes Toward Statistics (SATS-M), which is congruent with Eccles and colleagues’ Expectancy-Value Theory (Eccles’ EVT), as well as others. The SATS-M includes three broad constructs that impact Statistics Course Outcomes: Student Characteristics, Previous Achievement-Related Experiences, and Statistics Attitudes. We briefly describe Eccles’ EVT and other theories that support our SATS-M. We relate findings from research using the SATS to our model and end with implications for statistics education. First published November 2012 at Statistics Education Research Journal: Archives

2017 ◽  
Vol 16 (2) ◽  
pp. 487-510
Author(s):  
WARREN PAUL

We used the Survey of Attitudes Toward Statistics to (1) evaluate using pre-semester data the Students’ Attitudes Toward Statistics Model (SATS-M), and (2) test the effect on attitudes of an introductory statistics course redesigned according to the Guidelines for Assessment and Instruction in Statistics Education (GAISE) by examining the change in attitudes over the semester and, using supplementary data from an annual Student Feedback Survey, testing for a change in overall satisfaction following implementation of the redesigned course. We took an exploratory rather than confirmatory approach in both parts of this study using Bayesian networks and structural equation modelling. These results were triangulated with analysis of focus group discussions and the annual Student Feedback Survey. First published November 2017 at Statistics Education Research Journal Archives


2012 ◽  
Vol 11 (2) ◽  
pp. 6-25
Author(s):  
MARJORIE E. BOND ◽  
SUSAN N. PERKINS ◽  
CAROLINE RAMIREZ

Although statistics education research has focused on students’ learning and conceptual understanding of statistics, researchers have only recently begun investigating students’ perceptions of statistics. The term perception describes the overlap between cognitive and non-cognitive factors. In this mixed-methods study, undergraduate students provided their perceptions of statistics and completed the Survey of Students’ Attitudes Toward Statistics-36 (SATS-36). The qualitative data suggest students had basic knowledge of what the word statistics meant, but with varying depths of understanding and conceptualization of statistics. Quantitative analysis also examined the relationship between students’ perceptions of statistics and attitudes toward statistics. We found no significant difference in mean pre- or post-SATS scores across conceptualization and content knowledge categories. The implications of these findings for education and research are discussed. First published November 2012 at Statistics Education Research Journal: Archives


2011 ◽  
Vol 10 (1) ◽  
pp. 35-51
Author(s):  
STIJN VANHOOF ◽  
SOFIE KUPPENS ◽  
ANA ELISA CASTRO SOTOS ◽  
LIEVEN VERSCHAFFEL ◽  
PATRICK ONGHENA

Although a number of instruments for assessing attitudes toward statistics have been developed, several questions with regard to the structure and item functioning remain unresolved. In this study, the structure of the Survey of Attitudes Toward Statistics (SATS-36), a widely used questionnaire to measure six aspects of students’ attitudes toward statistics, is investigated. This study addresses the previously unexplored issue of individual item functioning. Based on confirmatory factor analysis using individual items, the results suggest that the SATS-36 can be improved by removing some poorly functioning items and that depending on the goals of a specific study either six subscales could be used or three of them (Affect, Cognitive Competence, and Difficulty) can be combined into one subscale without losing much information. First published May 2011 at Statistics Education Research Journal: Archives


2012 ◽  
Vol 11 (2) ◽  
pp. 103-123
Author(s):  
MEAGHAN M. NOLAN ◽  
TANYA BERAN ◽  
KENT G. HECKER

Students with positive attitudes toward statistics are likely to show strong academic performance in statistics courses. Multiple surveys measuring students’ attitudes toward statistics exist; however, a comparison of the validity and reliability of interpretations based on their scores is needed. A systematic review of relevant electronic databases yielded 532 citations, 78 of which were reviewed, and 35 included in a final analysis. Fifteen instruments were identified; however, evidence of validity and reliability has only accumulated for the Statistics Attitude Scale, Attitudes Toward Statistics Scale, and Survey of Attitudes Toward Statistics (two versions). In conclusion, a number of surveys exist, but there is a paucity of peer-reviewed validity and reliability evidence. First published November 2012 at Statistics Education Research Journal: Archives


2010 ◽  
Vol 9 (1) ◽  
pp. 6-26
Author(s):  
FRANCESCA CHIESI ◽  
CATERINA PRIMI

The aim of this study was to investigate students’ achievement in introductory statistics courses taking into account the relationships between cognitive and non-cognitive factors. It was hypothesised that achievement was related to background in mathematics (a cognitive variable), as well as to attitudes toward statistics and anxiety (non-cognitive variables). Students were presented with measures assessing their attitudes, mathematical competence, and anxiety toward courses and examinations at the beginning and at the end of their statistics course. Achievement was assessed by tasks assigned during the course, as well as by students’ final grades and the number of exam failures. The results reveal the reationships between cognitive and non-cognitive factors, their changes during the course, and how both interact in predicting achievement. First published May 2010 at Statistics Education Research Journal: Archives


2019 ◽  
Vol 18 (1) ◽  
pp. 83-93
Author(s):  
INGER PERSSON ◽  
KATRIN KRAUS ◽  
LISBETH HANSSON ◽  
FAN YANG WALLENTIN

Research on students’ attitudes toward statistics has attracted many statistics instructors and statistics education researchers. In this study, we use confirmatory factor analysis to analyze data collected from an introductory statistics course using the Survey of Attitudes toward Statistics. The results suggest that the items and six factors are conceptually relevant, confirming the six-factor structure of the pretest version of SATS-36 on this sample of Swedish students, with a few suggested modifications of the original model structure. Two items are excluded from the Difficulty component, two items on the Affect component are allowed to correlate, and two items on the Cognitive competence component are also allowed to correlate. First published May 2019 at Statistics Education Research Journal Archives


2011 ◽  
Vol 10 (1) ◽  
Author(s):  
ROBERT DELMAS ◽  
PETER PETOCZ

First published May 2011 at Statistics Education Research Journal: Archives


2012 ◽  
Vol 11 (2) ◽  
pp. 72-85 ◽  
Author(s):  
MICHELLE HOOD ◽  
PETER A. CREED ◽  
DAVID L. NEUMANN

We tested a model of the relationship between attitudes toward statistics and achievement based on Eccles’ Expectancy Value Model (1983). Participants (n = 149; 83% female) were second-year Australian university students in a psychology statistics course (mean age = 23.36 years, SD = 7.94 years). We obtained demographic details, past performance, attitudes, and expectancies in Week 2, and attendance records (effort) and course marks (achievement) at the end of semester. Path analysis was conducted via AMOS 19. The final model fit well and explained 40% of the variance in achievement. Past performance (22%), effort (8%), and expectancies (2%) made significant direct contributions. There were significant indirect contributions by each attitude component. Cognitive competence made the largest indirect contribution. First published November 2012 at Statistics Education Research Journal: Archives


2017 ◽  
Vol 16 (2) ◽  
pp. 476-486
Author(s):  
APRIL T. KERBY ◽  
JACQUELINE R. WROUGHTON

Statistics educators have been investigating how students’ attitudes change in the introductory statistics course for many years. Typically, an overall decrease in mean attitudes over the course has been noted. However, when and how do students’ attitudes change during the term? Do they steadily decrease or is there a point when students’ attitudes might actually be increasing? If so, can instructors use this to their advantage? This research introduced a mid-semester survey of attitudes. We found that students’ attitudes are not necessarily strictly declining from the beginning to the end of the semester. We also found it might be advantageous to follow individual student attitude trends throughout the semester instead of just looking at aggregate mean scores for the different surveys. First published November 2017 at Statistics Education Research Journal Archives


2012 ◽  
Vol 11 (2) ◽  
pp. 45-56
Author(s):  
JAMES D. GRIFFITH ◽  
LEA T. ADAMS ◽  
LUCY L. GU ◽  
CHRISTIAN L. HART ◽  
PENNEY NICHOLS-WHITEHEAD

Students’ attitudes toward statistics were investigated using a mixed-methods approach including a discovery-oriented qualitative methodology among 684 undergraduate students across business, criminal justice, and psychology majors where at least one course in statistics was required. Students were asked about their attitudes toward statistics and the reasons for their attitudes. Five categories resulted for those with positive and negative attitudes and were separated on the basis of discipline. Approximately 63% of students indicated a positive attitude toward statistics. Business majors were most positive and were more likely to believe statistics would be used in their future career. Multiple methodological approaches have now provided data on the various domains of attitudes toward statistics and those implications are discussed. First published November 2012 at Statistics Education Research Journal: Archives


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