Attitude Of Students Towards Statistics Application
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This document summarizes a study that investigated factors influencing students' acceptance of statistics. The study surveyed 200 undergraduate students in Malaysia about their affect, cognitive competence, perceived value of statistics, and acceptance of statistics. Results found that affect and perceived value significantly predicted students' acceptance of statistics, but cognitive competence did not. The study aimed to help improve statistics education by better understanding what influences students' views of the subject.
![Norizan Anwar, Aniza Jamaluddin and Hanis Diyana Kamarudin
83
Global J. Bus. Soc. Sci. Review 1 (3) 78 â 83 (2013)
majority students have a positive attitude towards statistics. Consistently finding shows by Thapa& Bharti
(2012) where positive attitude found towards the use and application of statistics.
The learning value of statistics subject gives a lot benefits to the students. Whenever the students able to
portray a good thinking skills, applying knowledge acquired in daily life and enjoyed throughout the class, it
comes from positive attitude of the students (Judi, et. al., 2011). It will be good if the students stay focused and
able to see the value of statistics for their future development at the beginning of their statistics introductory
class (Slootmaeckers, 2012).
Acknowledgements
Many thanks to management of Faculty of Information Management, Puncak Perdana Campus, Universiti
Teknologi MARA (UiTM) for their support. On top, our thanks are also to Nor Hafiza Binti Che Ani and Siti
Balkis Binti Hussien for all their assistance in data collection and entry collaboration.
References
Bradstreet, T. E. (1996), "Teaching Introductory Statistics Courses So That Nonstatisticians Experience Statistical
Reasoning", The American Statistician, 50, 69-78.
Bryman, A., and Crammer, D. (2001). Quantitative data analysis with SPSS release 10 for Windows (also suitable for
SPSS release 11): a guide for social scientist. New York: Routledge.
Gal, I., Ginsburg, L., & Schau, C. (1997). Monitoring attitudes and beliefs in statistics education. In I. Gal & J.B. Garfield
(Eds.), The assessment challenge in statistics education (pp. 37-51). Netherlands: IOS Press.
Kirk, R. E. (2002), "Teaching Introductory Statistics: Some Things I Have Learned," paper presented at the Annual
Conference of the American Psychological Association, Chicago, IL. (ERIC Document Reproduction Service No.
ED 473 611).
Nunnally, J. C. (1978). Psychometric Theory, McGraw-Hill, New York.
Schau, C. (2003). Studentsâ attitudes: The âotherâ Important outcome in statistics education. 2003 ASA Proceedings:
Papers presented at the AmericanStatistical Association Joints statistical Meetings. Alexandria, VA: American
Statistical Association, Section on Statistical Education, [CD-ROM] pp. 3673-3681.
Schield, M. and Schield, C. (2008), Student Attitudes toward Statistics at Augsburg College. Section on Statistical
Education â JSM 2008, pp. 2150-2157.
Suanpang, P., Petocz, P. and Kalceff, W. Student Attitudes To Learning Business Statistics Online Vs Traditional
Methods.
Thapa, A. & Bharti, A. (2012). Studentâs attitude towards application of statistics: a study of University of Jammu.
International journal of research in commerce, it & management, 2 (3), 135-138.
Slootmaeckers, K. (2012). Too afraid to learn?!attitudes towards statistics as a barrier to learning statistics and to acquiring
quantitative skills. 4th International Conference on Education and New Learning Technologies (EDULEARN12),
Barcelona (Spain).
Vanhoof, S. et. al. (2011). Measuring statistics attitudes: structure of the survey of attitudes toward statistics (sats-36).
Statistics Education Research Journal, 10(1), 35-51.
Judi, H. M. et. al. (2011). Students Profile Based on Attitude towards Statistics. Procedia Social and Behavioral Sciences,
18 (2011) 266â272.](https://image.slidesharecdn.com/attitudeofstudentstowardsstatisticsapplication-230806181002-da41326d/85/Attitude-Of-Students-Towards-Statistics-Application-6-320.jpg)
Attitude Of Students Towards Statistics Application
- 1. ISSN 2289-8506 Š 2013 Global Academy of Training & Research (GATR) Enterprise. All rights reserved. Global Journal of Business and Social Science Review Journal homepage: www.gatrenterprise.com/GATRJournals/index.html Global J. Bus. Soc. Sci. Review 1 (3) 78 â 83 (2013) Attitude of Students towards Statistics Application Norizan Anwar1* , Aniza Jamaluddin2 and Hanis Diyana Kamarudin3 1,2,3 Faculty of Information Management, Universiti Teknologi MARA, Puncak Perdana Campus, Section U10, 40150 Shah Alam, Selangor, Malaysia ABSTRACT Objective â This study aims to investigate the factors that influence the studentsâ statistics acceptance and its relationship. Methodology/Technique â Method used to conduct this research is quantitative and this study is using simple random sampling technique. Findings â The findings of this study show that, out of three investigated independent variables, only two variables turned out to be influential in predicting studentâs statistics acceptance among undergraduate students of program Bachelor of Science Information System Management (Hons) Faculty of Information Management Shah Alam, Malaysia. Novelty â This study only caters students who took statistics with no mathematics qualification required in the entry level of the program enrolment. Type of Paper: Empirical Keywords: Studentâs Statistics Acceptance; Affect; Cognitive Competence; Value; Undergraduate Students. JEL Classification: I20, I21, I23. _______________________________________________________________________________________ 1. Introduction The statistics course provides vital resources for functioning effectively in making decisions based on numerical data. Research in statistics education would be helpful to investigate the factors that influence the studentsâ statistics acceptance and its relationship. Many statistics educators think that studentsâ attitudes toward statistics are significant. The study was conducted among under-graduate students of program IM221/IM225 (Bachelor of Science Information System Management (Hons), Faculty of Information Management in Puncak Perdana Campus, Shah Alam, Malaysia. This program basically offers one (1) subject statistics in semester 3. 2. Literature Review Some people believe that statistics is useful both in professional and personal lives. Therefore, they might recognize that statistics can be interesting and willing to invest the effort needed in statistical thinking and skills learning even though realizing that statistics is not easy, but it also is not too difficult to learn (Gal, * Paper Info: Received: July 19, 2013 Accepted: August 25, 2013 * Corresponding author: E-mail: norizananwar@gmail.com Affiliation: Faculty of Information Management, Universiti Teknologi MARA, Malaysia
- 2. Norizan Anwar, Aniza Jamaluddin and Hanis Diyana Kamarudin 79 Global J. Bus. Soc. Sci. Review 1 (3) 78 â 83 (2013) Ginsburg, & Schau, 1997; Garfield, Hogg, Schau, &Whittinghill, 2002; Schau, Stevens, Dauphinee, & Del Vecchio, 1995). Kirk (2002) reported that students believe an introductory statistics course to be demanding, to involve lots of maths, and to be irrelevant to their career goals. Therefore, many students who completed the introductory statistics course often have negative perceptions of the course and are dissatisfied with the experience (Garfield 1997). However, the ultimate goal of statistics education is to produce individuals who appropriately use statistical thinking. Most college students take only one statistics course which is the introductory course. According to Bradstreet 1996, many non-statistics majors in algebra-based introductory statistics courses suffer from statistics anxiety and due to their negative attitude, some colleges and universities are currently using an introductory statistics course as a main way to meet the general education requirement for mathematics. Schield and Schield (2008) stated that about 61% of Augsburg students saw more value in statistics after their course than before and the difference was not statistically significant. It was found that these students had a statistically significant increase in their feeling of cognitive competence after their course even though they found it more difficult than expected. However, Suanpang et. al. in their study indicated that there is highly significant differences in students attitudes towards learning statistics online and using a traditional approach. It has been also found that there is no significant difference between the attitude of students studying on campus and those studying by distance. Based on this literature, this paper is purposely conducted to investigate the factors that influence the students' statistics acceptance and its relationship among undergraduate students of Bachelor of Science Information System Management, Faculty of Information Management UiTM Puncak Perdana Campus. 3. Research Framework Figure 1 shows the framework for studying factors that influence the studentâs statistics acceptance and its relationship. The framework is adopted and adapted from (Thapa & Bharti, 2012). Therefore, the dependent variable is Studentâs Statistics Acceptance (SSA). The independent variables were Affect (A), Cognitive Competence (CC) and Value (V). Figure 1: Research Framework 4. Methodology 4.1 Instruments and Method The instrument used for collecting the data was a questionnaire. The questionnaire is adapted and adopted from (Thapa & Bharti, 2012). Furthermore, some questions have made modification to suit and cater the environment. The questionnaire is divided into 5 parts, where part A captures information on demographic, Affect (A) Cognitive Competence (CC) Value (V) Student's Statistics Acceptance
- 3. Norizan Anwar, Aniza Jamaluddin and Hanis Diyana Kamarudin 80 Global J. Bus. Soc. Sci. Review 1 (3) 78 â 83 (2013) part B to part E capture information for measuring the independent and dependent variables. Items used in Part A were 4 questions, Part B were 6 questions, Part C were 6 questions, Part D were 9 questions and Part E were 7 questions. As an overall, there are all total of 32 items used in the questionnaire. All measures for the variables shown in Figure 1 were using several scale style. As Part B to Part E using a Likert scale with five extremes with 1 for âStrongly Disagreeâ, 2 for âDisagreeâ, 3 for âUndecided / Neutralâ, 4 for âAgreeâ and 5 for âStrongly Agreeâ. Table 1: Items in Instrument Affect (A) B1 I like statistics. B2 Feel insecure to do statistics problems. B3 Frustrated over using statistic tests. B4 Under stress during statistics class. B5 Enjoy taking statistics courses. B6 Afraid of learning statistics. Cognitive Competence (CC) C1 Trouble in understanding statistics. C2 No idea of whatâs going on in statistics. C3 Makes lot of math errors in statistics. C4 I can learn statistics. C5 I understand statistics equations. C6 Find difficult to understand statistics concepts. Value (V) D1 Statistics is worthless D2 Statistics as a part of my professional training D3 Statistical skills will be more employable D4 Statistics is not useful to the typical professional D5 Statistical thinking is not applicable outside job D6 Use statistics in everyday life D7 Statistics conclusions are rarely presented D8 No application for statistics in my profession D9 Statistics is irrelevant in my life Studentâs Statistics Acceptance (SSA) E1 Statistics formulas are easy to understand. E2 Statistics is a complicated subject. E3 Statistics is quickly learned by most people. E4 Learning requires a great deal of discipline. E5 Statistics involves massive computations. E6 Statistics is highly technical. E7 Learn a new way of thinking to do statistics. 4.2 Population and Sampling The study was conducted among under-graduate students of program IM221/IM225 (Bachelor of Science Information System Management (Hons), Faculty of Information Management in Puncak Perdana Campus, Shah Alam, Malaysia. This program basically offers one (1) subject statistics in semester 3. Thus, the questionnaire was distributed to only semester 3 and above. Students in semester 1 and 2 were excluded. Furthermore, this study also captured from the difference mode of study, which either the students are in full time mode or part time mode (e.g. undergraduate program offer PLK (Pelajar Luar Kampus) and E-PJJ (Elektronik-Pelajar Jarak Jauh). This study is using simple random sampling, 230 questionnaires were distributed to these information professionals. 210 questionnaires were returned, however 10 questionnaires
- 4. Norizan Anwar, Aniza Jamaluddin and Hanis Diyana Kamarudin 81 Global J. Bus. Soc. Sci. Review 1 (3) 78 â 83 (2013) were found unusable after data cleaning is made and 200 for data analysis. Statistical Package for the Social Sciences version 20 is used to analyze the data. 5. Results and Discussion 5.1 Respondents Profile Table 2 presents the respondentâs demographic profile. 116 out of all respondents are male and 84 are female. Looking to the semester, majority of the respondents are semester 3 while the minority i.e. 17% is in others semester. In term of study mode, 71% were full time students, 21% were e-PJJ students and 8% were PLK students. Table 2: Respondents Profile Characteristics Items Frequency Percentage Gender Female 84 42% Male 116 58% Semester 3 61 30% 4 46 23% 5 29 14% 6 47 24% Others 17 8% Study Mode Full Time 142 71% e-PJJ 42 21% PLK 16 8% 5.2 Reliability Analysis Reliability analysis was performed to ensure the scaleâs internal or reliability consistency strength. Table 3 indicated that all variables are above the recommended cut-off value which were 0.6. Hence suggesting that the scale used in the study was highly reliable (Nunnally, 1978). Table 3: Reliability Analysis Variables No. of Items Cronbach's Alpha Affect (A) 6 0.735 Cognitive Competence (CC) 6 0.716 Value (V) 9 0.772 Studentâs Statistics Acceptance (SSA) 7 0.796 5.3 Correlation Analysis Table 4 and 5 exhibits the details of correlation coefficients across all variables. Hence, according to (Bryman & Crammer, 2001), it is provisioned when any study measure strengths of a relationship. Table 4: Correlation Analysis Correlations Variables SSA A CC V SSA 1.000 0.452 0.245 0.554 A 0.452 1.000 0.470 0.597 CC 0.245 0.470 1.000 0.316 V 0.554 0.597 0.316 1.000
- 5. Norizan Anwar, Aniza Jamaluddin and Hanis Diyana Kamarudin 82 Global J. Bus. Soc. Sci. Review 1 (3) 78 â 83 (2013) Table 5: Detail Correlation Analysis Independent Variable Dependent Variable r value Correlation Analysis Affect (A) Studentâs Statistics Acceptance (SSA) 0.452 Positive Moderate Relationship Cognitive Competence (CC) 0.245 Positive Weak Relationship Value (V) 0.554 Positive Strong Relationship 5.4 Regression Analysis To this effect, the following equation is formulated i.e. factors that influence Studentâs Statistics Acceptance (SSA) = β1 A + β2 CC + β3 V + Ξ. Table 6 and 7 exhibits the results of the multiple regression analysis. R square value recorded of 0.331 as shown in Table 6. Hence implying that 33.1% variance in SSA can be explained by the combination of the independent variables which are Affect (A), Cognitive Competence (CC) and Value (V). Table 6: Model Summary of Regression Analysis between Independent Variables and Dependent Variables Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.575a 0.331 0.320 0.45532 a. Predictors: (Constant), A, CC, V a. Dependent Variable: SSA The results showed that, out of three (3) investigated independent variables, only two (2) variables turned out to be influential in predicting SSA. These variables were Affect (t = 2.290, p < 0.05) and Value (t = 6.042, p < 0.05). The other one (1) variable is found not to be significant as the recorded p-values were greater than 0.05. Hence, based on the results, the equation is revised to SSA = 0.154 A + 0.415 V + 1.543. Table 7: Coefficient Table for Variables Predicting SSA Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1.543 0.219 7.032 0.000 A 0.154 0.067 0.180 2.290 0.023 CC 0.020 0.064 0.021 0.318 0.751 V 0.415 0.069 0.441 6.042 0.000 a. Dependent Variable: SSA 6. Conclusions and Recommendation The objectives of this study is to measure factors that influence the acceptance of statistics subject among students in semester 3 and above who already taken the subject of IMS502 (Data analysis for decision making). Based on the analysis and finding above, the results showed that, out of three (3) investigated independent variables, only two (2) variables turned out to be influential in predicting SSA which are affect and value. Align with other studies, such measuring bachelor and master students conducted by Slootmaeckers (2012) shows that the attitudes of the students have an effect on the learning of statistics. Meanwhile, a few studies conducted using SAT and SATS-36 by Judi, et. al. (2011) and Vanhoof, et. al. (2011) respectively. There are about six components in the assessment, i.e. affect, cognitive ability, value, difficulty, interest and effort. However SAT-36 has undergone certain modification. Therefore, Vanhoof, et. al. (2011) emphasis to all researchers or lecturer out there to use the improved version for future research. Their studies found that
- 6. Norizan Anwar, Aniza Jamaluddin and Hanis Diyana Kamarudin 83 Global J. Bus. Soc. Sci. Review 1 (3) 78 â 83 (2013) majority students have a positive attitude towards statistics. Consistently finding shows by Thapa& Bharti (2012) where positive attitude found towards the use and application of statistics. The learning value of statistics subject gives a lot benefits to the students. Whenever the students able to portray a good thinking skills, applying knowledge acquired in daily life and enjoyed throughout the class, it comes from positive attitude of the students (Judi, et. al., 2011). It will be good if the students stay focused and able to see the value of statistics for their future development at the beginning of their statistics introductory class (Slootmaeckers, 2012). Acknowledgements Many thanks to management of Faculty of Information Management, Puncak Perdana Campus, Universiti Teknologi MARA (UiTM) for their support. On top, our thanks are also to Nor Hafiza Binti Che Ani and Siti Balkis Binti Hussien for all their assistance in data collection and entry collaboration. References Bradstreet, T. E. (1996), "Teaching Introductory Statistics Courses So That Nonstatisticians Experience Statistical Reasoning", The American Statistician, 50, 69-78. Bryman, A., and Crammer, D. (2001). Quantitative data analysis with SPSS release 10 for Windows (also suitable for SPSS release 11): a guide for social scientist. New York: Routledge. Gal, I., Ginsburg, L., & Schau, C. (1997). Monitoring attitudes and beliefs in statistics education. In I. Gal & J.B. Garfield (Eds.), The assessment challenge in statistics education (pp. 37-51). Netherlands: IOS Press. Kirk, R. E. (2002), "Teaching Introductory Statistics: Some Things I Have Learned," paper presented at the Annual Conference of the American Psychological Association, Chicago, IL. (ERIC Document Reproduction Service No. ED 473 611). Nunnally, J. C. (1978). Psychometric Theory, McGraw-Hill, New York. Schau, C. (2003). Studentsâ attitudes: The âotherâ Important outcome in statistics education. 2003 ASA Proceedings: Papers presented at the AmericanStatistical Association Joints statistical Meetings. Alexandria, VA: American Statistical Association, Section on Statistical Education, [CD-ROM] pp. 3673-3681. Schield, M. and Schield, C. (2008), Student Attitudes toward Statistics at Augsburg College. Section on Statistical Education â JSM 2008, pp. 2150-2157. Suanpang, P., Petocz, P. and Kalceff, W. Student Attitudes To Learning Business Statistics Online Vs Traditional Methods. Thapa, A. & Bharti, A. (2012). Studentâs attitude towards application of statistics: a study of University of Jammu. International journal of research in commerce, it & management, 2 (3), 135-138. Slootmaeckers, K. (2012). Too afraid to learn?!attitudes towards statistics as a barrier to learning statistics and to acquiring quantitative skills. 4th International Conference on Education and New Learning Technologies (EDULEARN12), Barcelona (Spain). Vanhoof, S. et. al. (2011). Measuring statistics attitudes: structure of the survey of attitudes toward statistics (sats-36). Statistics Education Research Journal, 10(1), 35-51. Judi, H. M. et. al. (2011). Students Profile Based on Attitude towards Statistics. Procedia Social and Behavioral Sciences, 18 (2011) 266â272.