Effect of Teaching Style on Students Scores - Essay Example

Effect of Teaching Style on Students Scores Abstract This study explores the effect of teaching styles on students’ scores using different assessment tools. A study of first year medical students’ examination results is used; consequently the final exam results of the students for two consecutive years (2008 and 2009) are compared to determine the effect of different teaching styles. The study covers 122 students in 2008 and 123 in 2009. The following instruments were used to gauge the statistical analyses: the One Way Analysis of Variance (ANOVA) comparing the three assessment groups; Duncan’s Multiple Range Test (MRT) for detecting the variation between the variables; and Pearson Correlation Coefficient (PCC) for determining the correlation between the assessment tools results for 2009 cohorts. The results revealed no significant changes between the two groups of students when using the ANOVA but depicted elevated levels in 2009 using the MRT. The PCC test revealed some positive correlation between the sampled assessment test scores. Therefore the study demonstrates that teaching styles are correlated to the final scores among students hence innovative teaching style has a favorable consequence among the learners’. Introduction and Literature Review Conventional teaching techniques are increasing being viewed as deficient in terms of upholding extensive scholarship and lasting preservation of essential educational concepts. Additionally, these teaching processes are alleged of encouraging quiescent rather than active interactive scholarship (Meyers & Jones, 1993). The advent of sophisticated ICT learning tools has compelled hitherto conservative educationalists to adopt modern methods of teaching especially in the scientific fields of study of science, mathematics, engineering, and technology (SMET) education. Considerable research has proven the wisdom of assimilating these innovative techniques, as growth have been demonstrated in the education milieu particularly in adult education (Hein & Irvine, 2004). Kolb (1985) in his pragmatic learning cycle claimed that a learner undergoes various stages of knowledge sequence; whereby there is a certain foremost useful stage that is conducive to instruction and understanding. DePorter (1992) further observed that perceived disparities in a student learning style are often caused by ‘a conflict between the student’s preferred learning modality and the teacher’s teaching style’ (p.120). Most teachers are rigid in the application of teaching methods and usually maintain the learning style they assimilated in their formative schooling when they in turn become instructors. Stitt-Gohdes (2001) asserts that ‘research supports the concept that most teachers teach the way they learn’ (p.137). This contrasts to the learners expectations; Felder (1993) observed that, students ‘preferentially focus on different types of information, tend to operate on perceived information in different ways, and achieve understanding at different rates’ (p. 286). A conflict of the teaching style employed by instructors and learning style prevalent among different students is inevitable and instructors have to adapt their TS to avoid a mismatch of styles and effective learning. Harrelson et al (1998) in a survey using Kolb LSI at NATA Professional Educators' Conference 1999 observed that 16 percent of respondents were accommodators, eight percent divergers, 39 percent convergers, and 37 percent assimilators hence indicating an abundance of abstract students or 76 percent convergers or assimilators. This can consequently be easily translated into the teaching style (TS) adopted by the instructors. Zeeb (2004) argues that “many times students struggle in school because a teachers teaching style (TS) conflicted with the students learning style (LS)” (p. 2). The assortment of various LS from the students as opposed to the singular TS from the instructor results in poor communication and teachers are forced to re-teach courses or students re-sit the class for the content to be assimilated. Felder (1993) observed that students who had LS compatible to the TS of the instructor are more predisposed to garner more from the course in addition to having a more positive post-course attitude than those who did not enjoy similar attributes. Jensen (2003) therefore urges for a more concrete matching of the instructors TS and the students LS to enhance a more flourishing interaction between the pupils and instructors. In the Johnston & Dainton Learning Combination Inventory (LCI) tool a Personal Development Planning (PDP) file is mandated to ‘help each individual become more responsible for their own learning and career development and to gain a better understanding of their own skills’ (Hermon, 2007, p. 12). Coffield et al, (2004) have however disparaged many of these proposed innovative models terming them as lacking any tangible supporting research. A case in point was the reputable instrument, the Myers-Briggs Type Indicator that focuses on personality traits rather than learning styles. Coffield et al argued that this model was deficient of any to link values generated by the test concurrent to learning. Nevertheless they acknowledged that modern TS techniques have led to enhanced examination scores that were lacking in the traditional rigid TS methods. Modern Teaching Aids Sidman & Jones (2007) argue that contemporary learners who are already extremely exposed to various visual and interactive technologies require “innovative, pedagogical approaches that effectively utilize technology and meet students’ needs” (pg.448). However in their study of the impact of the use interactive skeletal (partial) PPT slides on students, they did not find significant improvement in exam scores when the method was applied but the results indicated that there was still substantial elevation of exam scores when the slides were applied on higher level coaching courses. Woolner (2004) in a study at the CEM Centre, Durham University, UK found compelling evidence of the advantage of verbal teaching styles when contrasted with visual TS techniques when teaching mathematics; whereby a post-intervention test of mathematical competency evaluated two differing classes in which the verbally instructed class attained considerably higher marks. This application of modern technology to learning has also been advocated by Prensky, (2001) and (Salopek, 2003), who cite the evolvement of digital technologies and multi-tasking that necessitate educators to capitalize on this novel motivational methods of conveying information to students. Others are not so enthusiastic about incorporating modern technologies in as teaching aids, Hooper & Rieber (1995) and Oblinger (2003) in particular advocate caution arguing that some students may not be comfortable with the new methods. The traditional methods include black/white-board and lecture note-taking. Although most instructors are reluctant to provide either the matrix notes or whole notes prior or after the lectures, Kiewra (1985) revealed that the widespread incidences of poor note-taking by students led to poor scores, with a further study by Kiewra, et al (1988) indicating that dispensing lecture notes outlines was far more helpful than distributing entire complete text notes. The latter method helped improve performance of students tremendously. However Sutliff & Baldwin (2001), propagate the adoption of both the traditional and modern teaching styles to engage the best of both worlds. Some early studies on education style theory suggest that students learn in three ways, visually, auditory, and tactilely (or kinaesthetically) also known as VAK (visual, auditory, kinaesthetic) learning (Clark, 2000). Jones & Mungai (2003) study on the impact of technology assisted teaching techniques insisted that ‘Technology can be the answer to filling the gaps caused by the differences between learning and teaching styles’ (p. 3499). Digitized video clips, interactive internet sites that encompass online self assessments and course management systems, replication, and PowerPoint slides are some of the modern methods applied to the stimulate the ‘millennial learners’ in contemporary learning styles. Among these, PowerPoint presentations are the most popular for both teachers and students (Bartsch & Cobern) 2003; GodwinJones (2002) ; Lewis, 2003) Students have nonetheless demonstrated a liking for new ICT based methods. Preszler et al (2007) in a study to a New Mexico State University biology class found that most students were positively enthusiastic about the interactive wireless technology to generate feedbacks to their lecturers. Interactive online training and the application of information and communication technologies (ICT) in instructive strategies have transformed the process of content delivery in training students. Instructors are thus now increasingly challenged to sharpen their cognitive skills in enhanced gathering, analyzing, appraising, summarizing, and combining of data to convince the much more informed modern student who have access to online ‘search engines’ like the popular Google or Yahoo! which offer a surfeit educational material expediently (Altun & Cakan, 2006). Jonassen & Grabowski (1993) project an evolution from cognitive styles to learning styles whereby the former involves the content or the level of cognitive action while the latter invoke the method and structure of education. In a study of the ‘effects of teaching styles on motivation for self-training by students’ at Keio university –Tokyo, two aspects of teaching were discerned: the content of the course and the procedure employed. Tanake (2005) concluded that students were more inclined to change the stratagem of their learning dependent on the system of evaluation employed by the instructors. Thus a term examination led to students memorising the contents, a method that has been proven to be unproductive in schooling. Felder and Silverman (1988) categorize the various favoured learning styles (LS) into four parts which are based on the nature of data individual selectively identify, the sensory conduit by which individuals mainly sufficiently observe data, the cerebral procedure by which apparent data is transformed to knowledge, and the method in which individuals comprehend and master the content and the bipolar predilections are defined for each element. Felder (1993) also categorize teaching styles into four elements: content can be tangible/conceptual (intuitive learners), presentation as ocular/oral, (verbal learners), learner involvement as dynamic/dormant (reflective learners) or perspective as chronological or widespread (global learners). Another teaching technique termed folder activities has been advocated by Hein (1998) whereby a two-pocket paper folder use was encouraged. One of the folders is reserved for keeping personal class notes which are evaluated and students receive feedbacks concerning their grasping of the prevailing topics and suggested further reading. This folder will later form a valuable tool for them when revising their coursework. Purpose/Objectives of the Study The purpose of this study was to examine the influence of teaching styles on students’ scores using different assessment tools. Students were instructed to develop their answers based on lecture notes, textbooks and other related teaching material available in the university library and on-line. Other teaching practices remained the same, i.e. tutorials and practical sessions including teaching content. The post-test period was evaluated to detect the impact of the self-evaluation testing in the after-lecture tests. Specifically, the research objectives were: 1. To describe the teaching styles of instructors and the learning styles of students among the medical classrooms. 2. To establish the influence of selected teaching styles on the students’ scores in regards to the methods applied in the course of instruction. 3. To illustrate levels of congruence and incongruence between the instructors and the learning styles of sampled medical students and the impact on performance scores. 4. To asses whether other factors apart from revision questions influenced the students scores in the pos-test period. 5. Have any changes occurred between the cohorts? Methodology The design of the study is pre-post test investigational process. In this study, performance scores are the dependent variable while the diverse learning period are the independent variables. The pre-test period (2008) was prior to the application of revision questions (short answer-type questions) to assist their learning of general and basic pathology after each lecture based on the learning objectives for that specific lecture. The questions were provided on-line without answers and students were required to research in further detail the topic when compared to its presentation in class. Sample Population The target population and the subject of this investigation are some undergraduate first year medical students who were assessed in their final semester exams between the year 2008 and 2009. The cohorts comprised 122 students in 2008 and 123 in 2009. Although the instructor’s assessment was not evident in the survey, they were still reflexively evaluated on against their teaching methods. Instruments: For the statistical analyses the following instruments were used: The One Way Analysis of Variance (ANOVA) to compare the three groups of assessments; The Duncan Multiple Range Test to find the level of variation between the variables; The Pearson Correlation Coefficient to determine the correlation between the assessment tools results for the 2009 cohort For the purposes of this study, the ANOVA model was chosen due to its proven compatibility with scholarly statistical data. It is particularly effective in analysing data between two variables and detecting the variance of diverse models. It has been variously applied to identify the covariance, standard deviation and mean. The Duncan Multiple Range Test (MRT) is liberal analytic method applied to detect multiple comparison methods for scholarly statistics and is noted for filtering against negative errors (Type II). In our analysis, the MRT is used to detect the deviation in the multiple test variables accrued from the two cohorts. The Pearson product-moment Correlation Coefficient (PMCC) is widely employed in scientific analysis and was preferred due to its reliability in evaluating between two variables x and y yielding a value between +1 and 01 inclusive. The PMCC indicates whether there is a linear relation between two variables, and in our case the 2008 cohorts compared to the 2009 cohorts. These three analytical tools were also chosen due to amenable quality among other important reasons, including their previous application by numerous respected academic researchers and their reputable elevated consistency and construct legitimacy. Data Collection The data used in this survey was made of examination results and collected from first year medical students at the close of the semester exams. Other data was gathered from the revision questions and practical applied questions results. Procedures During the second year the author/researcher introduced revision questions (short answer-type questions) which were administered online at the lessons. These were self-assessment questions with no answers whereby the students were required to research on the answers from the lecture topics. Cross (1993) advocates for the utilisation of a classroom assessment technique which offers a controlling determining appraisal stratagem. Feldgen & Osvaldo (2009) while also praising the classroom assessment techniques (CAT) assert that ‘the learning approach is a spiral approach based on reflection and knowledge collaboration for problem resolution’ (pg.1). At the end of the semester the students were assessed or tested using the regular term exams which included multiple choice-type questions (MCQs) and short answer type-questions (SAQs). Similarly, the students were required to undergo a practical assessment or an applied exam, which consists of short answer-types questions covering material from laboratory practical sessions. These assessed their understanding of various scientific materials such as histopathology slides, micrographs and photographs, and learn to integrate the concepts and principles presented in lectures with the relevant teaching material. the control data is from the final semester exam which tests the student’s acumen from the term teaching and is therefore a veritable appraisal of the teaching method employed in the course of the term lessons. In the main exam, the use of multiple-choice method reduces bias since it is an objective test. RESULTS of the Study A total of 245 first year medical students were assessed in the test. The evaluation stratagems considered for use in the task are both formative and summative in nature. The formative data was collected from the students prior to the assessment and also through various activities; the summative data was garnered from various classroom tests administered to the students at the end of their semester. The examination adopted in 2009 was more comprehensive as the college used a review panel to set the examination unlike the previous year. Consequently it was revealed that the SAQ test in 2008 was less engaging in terms of functional type answers when contrasted with the more extensive 2009 exam that mandated a more profound understanding from students. Applied Exam (Applied) A review of the Applied Exam using ANOVA revealed no significant changes in the p value whereby p>0.46 occurred between the two cohorts. The Duncan Multiple Range Test (MRT) generated a higher means in the 2009 cohort that was not quite significant in comparison to the 2008 cohort. Short Answer-type Questions (SAQs) In the SAQs, the ANOVA test indicated a more significant p value (p>0.007) in the 2009 cohort as compared to the lower value in 2008. The MRT in contrast revealed a lower mean in 2009 as compared to that of 2008. (See Appendix) Multiple Choice-type Questions (MCQs) Among the MCQs, the ANOVA test demonstrated a significantly marked p value (p>0.01) in the 2009 cohort and similarly the MRT test indicated a corresponding elevation. The Pearson Correlation Coefficient (PMCC) This test was used to contrast the three assessment tools (SAQ, MCQ, and Applied exams) in the final semester examination of 2009. The test indicated a positive correlation between SAQ and Applied exams whereby students who performed well in the SAQ exam similarly exemplary in the Applied assessment hence indicating an elevated correlation between the two methods. Likewise there was a distinct correlation between the SAQ and MCQ assessments illustrated by the performance of students in the two sets of examinations. However, there was no distinguishable correlation between the MCQ and Applied assessment tests. Discussion and Analysis Based on the results of our survey, it’s quite evident that the new method of teaching using the self-administered review questions has an impact on the performance of students. This was noticeably manifest in the short answer type-questions (SAQs) and multiple choice-type questions (MCQs) while the Applied test was revealed no marked elevation during the same period. The use of impromptu quizzes or continuous assessment tests has been credited with elevated examination scores. Thus it gives a better self-evaluation tool to the students in the course of their studies as well as providing a formal assessment tool. Several assessment criteria are employed to appraise the development and understanding of the students as regards the teaching techniques utilised by the instructors. This method is preferred to the passive end term assessment that assumes collective absorption of the course without taking in cognize the differing LS of the individual students. In a study of the ‘effects of unannounced tests on student performance’ Kamuche (2007) discerned that students who were tested with impromptu quizzes had far better scores than those that had pre-designated evaluation tests. This confirmed earlier studies by Graham (1999); Ballard and Johnson (2004) and Bell (1997) who called for active participation of students in class to enhance their cognitive abilities and higher retention of assimilated material. The prevalence of recurrent assessments encouraged learners to actively study harder to avoid poor results from unexpected quizzes. The physical learning environment is also essential to a student LS, this includes: the aesthetic environment, lighting, seating, furnishings, and temperature, among others. Other factors like emotional environment are critical factors to learning as well as periodic speech interruptions; congested lecture rooms, perverse regulations, or impracticable deadlines together induced an oppressive environment not conducive for learning. Extreme concentration burden on the students generates resentful learners which usually due to the instructors having an inflexible teaching technique that does not incorporate progressive modern teaching methods. Nevertheless many educators regard modern teaching techniques as impracticable and are mainly opposed to changes in their curriculum and teaching methods. Conclusion The effectiveness of periodic after lecture testing has been variously advocated by several researchers who argue that the prevalence of interactive teaching methods encourages students to be more attentive in class in anticipation of the evaluation at the end of the session. This has therefore led to better examination scores in the final term exams. According to Callahan (1999), the ‘real success of our classrooms and schools should be measured not simply by what we cover, test scores, or how many students finish on time, but by the degree to which classrooms are places where truly every student is learning’(p.26). The study has therefore confirmed that regular after lesson assessments rather than the traditional end term examination is a more effective teaching style and has veritable impact on the students’ scores at the end of the semester. The adoption of more innovative teaching styles is recommended including: multimedia teaching aids, democratic TS, collaborative teaching and learning methodologies. The importance of this study is acknowledged when considering the evolving nature of modern teaching methods that require a more interactive session between students and their instructors. References Akkoyunlu M Y S (2002). The Effect of Learning Styles on Achievement in Different Learning Environments. Ankara, Turkey: The Turkish Online Journal of Educational Technology. Altun A & Cakan M. (2006) Undergraduate Students’ Academic Achievement, Field Dependent/Independent Cognitive Styles and Attitude toward Computers. Educational Technology & Society, 9 (1), 289-297 Ausburn L J & Ausburn F B (1978) Cognitive styles: Some information and implications for instructional design. Educational Communications and Technology Journal, 26, 337-354 Callahan. C. M. (1999). Classrooms for learners, not winners and losers. High School Magazine, 7(1), 22-28. Clua M F (2009) The use of CATs and Case-based Teaching for Dealing with Different Levels of Abstractions 39th ASEE/IEEE Frontiers in Education Conference-T4F-2 (pp. 1-7) San Antonio, TX: University of Buenos Aires, Argentina Coffield F et al (2004) Should we be using learning styles? What research has to say to practice Learning & Skills Network Available at https://www.lsneducation.org.uk/ [Accessed on October12, 2009] Clark D (2000) VAK learning styles Available at: [Accessed October 12, 2009] Cross K. P (1993) On College Teaching Journal of Engineering Education Vol 82 No 1 9 – 14. Dunn R, K Dunn R I Sklar and J Beaudry 1991 Effects of Matching and Mismatching Minority Developmental College Students’ Hemispheric Preferences on Mathematics Scores Journal of Educational Research vol 83 no. 5 pp 283 – 288. Dwyer F M & Moore D M (1995) Effect of colour coding and test type (visual/ Verbal) on students identified as possessing different field dependence level ERIC Document No. Ed 380 078. Felder R M (1993) Reaching the second tier: Learning and teaching styles in college science education Journal of College Science Teaching, 23(5), 286-290. Garsha A F (2002) Teaching With Style. San Barnadino, CA: Alliance Publishers. Godwin-Jones B (2002) Ten ways to enhance teaching through technology. Available at: [Accessed October 12, 2009] Harrelson G, Leaver-Dunn D, Wright K (1998) An assessment of learning styles among undergraduate athletic training students. J Athl Train. pg.33:50–53. Hein T L (1998) Using Student Writing as a Research and Learning Tool AAPT Announcer vol. 27 no 4, pp 79 Hein T L and S E Irvine (1998) Assessment of Student Understanding Using On-line Discussion Groups Proceedings 1998 Frontiers in Education Conference FIE pp.130 – 135. IEEE Catalog No. 98CH36214. ISBN 0-7803-4762-5. Hermon J P (2007) The Use of Learning Styles as a Tool for Curriculum and Personal Development. Proceedings of the 3rd International CDIO Conference, MIT (pp. 1-14) Cambridge, Massachusetts: Queen’s University Belfast. Hooper S & Rieber L P (1995). Teaching with technology. In A. C. Ornstein (Ed.), Teaching: Theory into practice, (pp. 154170). Needham Heights, MA: Allyn and Bacon. Irvine, T. L. (2004). Technology as a Teaching and Learning Tool: Assessing Student Understanding in the Introductory Physics Lab. Washington, DC: Department of Physics/School of Education: American University. Jonassen, D. H., & Grabowski, B. L. (1993). Handbook of Individual Differences, Learning, and Instruction, Hillsdale, NJ: Lawrence Erlbaum. Jones, C. L. (2007). Addressing Students’ Learning Styles through Skeletal PowerPoint Slides: A Case Study. MERLOT Journal of Online Learning and Teaching, Vol. 3, No. 4; pg. 1-12. Jones D C & Mungai, D. (2003). Technology enabled teaching for maximum learning. International Journal of Learning, 10 Jones D C (2006) Scavenger hunt enhances students’ utilization of blackboard. Journal of Online Learning and Teaching, 2 Kamuche, F. U. (2007). The Effects of Unannounced Quizzes on Student Performance: Further Evidence. College Teaching Methods & Styles Journal – Second Quarter, Vol 3 No 2 pg 21-24. Kolb D A (1984) Experiential Learning: Experience as the Source of Learning and Development. New Jersey, Prentice-Hall Inc Kiewra, K A (1985) Providing the instructor's notes: An effective addition to student note-taking. Educational Psychologist, 20, 3339. Kiewra K A, Dubois N F, Christian D, & McShane A (1988) Providing study notes: Comparison of three types of notes for review. Journal of Educational Psychology, 80(4), 595597. Lynch, T. G., Woelf, N. N., Hanssen, C. S., & Steele, D. J. (1998). Learning style influences student examination performance. American Journal of Surgery, 176, 62-66. Meyers C and T B Jones (1993) Promoting Active Learning: Strategies for the College Classroom. San Francisco: Jossey-Bass Publishers. Pin Ng, J. P. (2008). The Effect of Learning Styles on Course Performance: A Quantile Regression Analysis. Flagstaff, AZ: Northern Arizona University: The W. A. Franke College of Business. Prensky M (2001) Digital natives, digital immigrant. On the Horizon, NCB University Press, 9(5), 16. Ralph W. Preszler, A. D. (2007). Assessment of the Effects of Student Response Systems on Student Learning and Attitudes over a Broad Range of Biology Courses. American Society for Cell Biology Salopek J J (2003) Going native: Cross the generation gap by learning to speak game. Training and Development, 57(6), 1719. Schell, J. C. (1998). Aligning Student Learning Styles with Instructor Teaching Styles. Journal of Industrial Teacher Education, Vol neither 35 Nor 2 35(2), pg. 41-56. Stephanie L. Stradley, B. D. (2002). A Nationwide Learning-Style Assessment of Undergraduate Athletic Training Students in CAAHEP-Accredited Athletic Training Programs. Journal of Athletic Training, J Athl Train. 2002 Oct–Dec; 37(4 suppl): S-141–S-146. Tanabe, H. (2005). Effects of Teaching Styles on Motivation for Self-Training by Students in Teaching of Presentation in L2. ACADEMIC REPORTS Fac. 2 Eng. Tokyo Polytech. Univ. , Vol. 28 No.2. Yerrick, R., & Johnson, J. (2009). Meeting the needs of middle grade science learners through pedagogical and technological intervention. Contemporary Issues in Technology and Teacher Education, 9(3). [Online: Accessed October 12, 2009] Available at: Witkin, H. A., Oltman, P. K., Raskin, E., & Karp, S. A. (1971). A Manual for the Embedded Figures Test, Palo Alto, CA: Consulting Psychological Press Woolner, P. (2004). A Comparison of a Visual-Spatial Approach and a Verbal Approach to Teaching Mathematics. Conference of the International Group for the Psychology of Mathematics Education (pp. 449-456, Vol 4). Newcastle: University of Newcastle Upon Tyne. Zeeb, M. S. (2004). Improving Student Success Through Matching Learning and Teaching Styles. Phoenix: University of Phoenix. Appendices Statistical Analyses Appendix 1: The ANOVA Procedure Class Level Information Class Levels Values Year 2 2008 / 2009 Data for Analysis of Applied Number of Observations Read 249 Number of Observations Used 248 Data for Analysis of SAQ Number of Observations Read 249 Number of Observations Used 245 Data for Analysis of MCQ Number of Observations Read 249 Number of Observations Used 247 NOTE: Variables in each group are consistent with respect to the presence or absence of missing values. Appendix II: The ANOVA Procedure 1. Dependent Variable: Applied Sum of Source DF Squares Mean Square F Value Pr > F Model 1 55.19758 55.19758 0.53 0.4672 Error 246 25608.60484 104.10002 Corrected Total 247 25663.80242 R-Square Coeff Var Root MSE Applied Mean 0.002151 19.08099 10.20294 53.47177 Source DF Anova SS Mean Square F Value Pr > F Year 1 55.19758065 55.19758065 0.53 0.4672 (not significant) Appendix III: Duncan's Multiple Range Test for Applied NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 246 Error Mean Square 104.1 Number of Means 2 Critical Range 2.552 Means with the same letter are not significantly different. Duncan Grouping Mean N Year A 53.944(54) 124 2009 A 53.000 124 2008 Appendix IV: Dependent Variable: SAQ Sum of Source DF Squares Mean Square F Value Pr > F Model 1 2091.93580 2091.93580 7.28 0.0075 Error 243 69873.71318 287.54614 Corrected Total 244 71965.64898 R-Square Coeff Var Root MSE SAQ Mean 0.029069 23.58373 16.95719 71.90204 Source DF Anova SS Mean Square F Value Pr > F Year 1 2091.935798 2091.935798 7.28 0.0075 ** (highly significant) Appendix V: Duncan's Multiple Range Test for SAQ NOTE: This test controls the Type I comparison-wise error rate, not the experiment-wise error rate. Alpha 0.05 Error Degrees of Freedom 243 Error Mean Square 287.5461 Harmonic Mean of Cell Sizes 122.498 NOTE: Cell sizes are not equal. Number of Means 2 Critical Range 4.268 Duncan Grouping Mean N Year A 74.836 122 2008 (higher than in 2009) B 68.992 123 2009 Appendix VI: Dependent Variable: MCQ Sum of Source DF Squares Mean Square F Value Pr > F Model 1 2188.60121 2188.60121 6.56 0.0110 Error 245 81738.51620 333.62660 Corrected Total 246 83927.11741 R-Square Coeff Var Root MSE MCQ Mean 0.026077 31.82313 18.26545 57.39676 Source DF Anova SS Mean Square F Value Pr > F Year 1 2188.601212 2188.601212 6.56 0.0110 **(highly significant) Appendix VII: Duncan's Multiple Range Test for MCQ NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 245 Error Mean Square 333.6266 Harmonic Mean of Cell Sizes 123.4818 NOTE: Cell sizes are not equal. Number of Means 2 Critical Range 4.579 Means with the same letter are not significantly different. Duncan Grouping Mean N Year A 60.410 122 2009 B 54.456 125 2008 Appendix VIII: The MEANS Procedure N Year Obs Variable Mean Std Dev Std Error N Minimum 2008 125 Applied 53.00 10.51 0.94 124 25.00 SAQ 74.84 16.49 1.49 122 15.00 MCQ 54.46 17.17 1.54 125 13.00 2009 124 Applied 53.94 9.88 0.89 124 25.00 SAQ 68.99 17.41 1.57 123 20.00 MCQ 60.41 19.33 1.75 122 13.00 N Year Obs Variable Maximum 2008 125 Applied 76.00 SAQ 100.00 MCQ 100.00 2009 124 Applied 76.00 SAQ 100.00 MCQ 100.00 Appendix IX: The CORRELATION Procedure 3 Variables: Applied SAQ MCQ Simple Statistics Variable N Mean Std Dev Sum Minimum Maximum Applied 248 .47177 10.19323 13261 25.00000 76.00000 SAQ 245 71.90204 17.17385 17616 15.00000 100.00000 MCQ 246 57.63008 18.13997 14177 13.00000 100.00000 Pearson Correlation Coefficients Prob > |r| under H0: Rho=0 Number of Observations Applied SAQ MCQ Applied 1.00000 0.25484 0.02073 Read More