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The effect of quality management theory on assessing student learning outcomes.

Senators and Representatives in the United States Congress and the Department of Education have demanded evidence from administrators at colleges and universities that the quality of education they provide was worth the billions of federal dollars being spent. In 2010, for example, members of Congress wanted to know that the outstanding balance of more than $850 billion, which, in that year was loaned to more than 19 million students, promoted positive learning results (Sparks, 2011).

Regulations from multiple stakeholders require reports on student learning outcome assessment in higher education. Government officials demand evidence of student learning to justify federal expenditures on higher education (Culver, 2010). Duque and Weeks (2010) have noted the importance of assessing student learning outcomes in response to requirements from external stakeholders, such as the government. However, a lack of guidance on how to assess student learning outcomes has made it difficult to meet the requirements of the regulators. One reason regulators may not provide useful guidance could be perceptions regarding academic freedom (Eaton, 2010).

The U.S. Department of Education formed a committee to report to the Secretary of Education on measures of student success (USDE, 2011). The committee noted that data and measures of student learning were being collected for numerous stakeholders, but that stakeholders only agreed on a few standardized measures (USDE, 2011). Ohia (2011) noted that administrators, faculty, and staff struggle to identify useful models that allow them to assess and report effective student learning outcomes.

Accreditation has been the primary tool used by the government to determine whether or not institutions of higher education were qualified to receive federal funding. However, government officials have also been demanding evidence of student learning (Culver, 2010).

Overview

This study provided evidence that applying quality management theory (internal quality assurance) enhanced the assessment processes of student learning outcomes (external quality assurance). In short, the synergism generated through a linkage with quality management standards improved the ability to assess student learning outcomes. The study added to existing knowledge in management and quality management theory by providing insight into the impact of quality management principles and the extent to which implementing them enhanced the process of assessing student learning outcomes (Bontas, 2011).

Management theory building must include factors responsible for observed patterns in specific management contexts (Dierksmeier, 2011; Klefsjo, 2010; Prabhu, 2011). This research studied student learning outcome assessments as factors responsible for observed patterns. The other requirement of management theory building is the specific management context (Prabhu, 2011), and the specific context studied here was accredited business schools in higher education.

Institutions of higher education with accredited business programs are required to report periodically on their quality management implementation and student learning outcome assessment processes. Therefore, it was possible to score the quantitative results of the application of quality management principles from these reports, as well as the quantitative results of student learning outcomes assessment processes.

Administrators who lead and manage business schools using quality management principles routinely apply the following concepts: They apply systematic approaches to their educational processes that build in the opportunity for evaluation, improvement, and sharing, and 2) They also apply the approach to address the requirements of the management principles. In addition, administrators acquire new knowledge or skills through evaluation, study, experience, and innovation. Business schools operate with effective quality management procedures embedded in their processes. Enhanced student learning outcome assessment processes should be a synergistic result when accredited business schools apply quality management principles.

Purpose of the Study

The purpose of this quantitative methodological study was to determine if the application of quality management principles at institutions of higher education with accredited business programs resulted in enhanced student learning outcomes assessment processes.

Literature Review

A review of the literature showed the need for more research on quality management in higher education, as well as a need and opportunity to research the assessment of student learning outcomes. Research concluded there was empirical evidence that quality management was an important key to improved performance in many organizations (Chung et al., 2008; Prajogo and Sohal, 2006; Sila, 2007; Tseng and Lin, 2008). Research also concluded that quality management principles and concepts benefited institutions of higher education (Emiliani, 2005; Imran & Mahmood, 2011; Keller, 1992; and Man and Kato, 2010).

Internal and external stakeholders could not provide guidance to administrators, faculty, and staff members who were responsible for reporting on student learning outcomes. Indeed, they struggled to identify models that allowed them to assess and report effective student learning outcomes (Ohia, 2011).

All quality management systems in higher education require the development of effective, measurable quality outputs. Student learning outcome assessment was one of the outputs measured and reported (Becket and Brookes, 2008; Nasser and Ghada, 2011 ; Srikanthan and Dalrymple, 2004; Templeton, Updyke and Robert, 2012).

A search of 28 databases found 12 studies that examined similar relationships between the effectiveness of quality management and other variables such as student and institutional characteristics, student experiences, and effects on faculty. Only a few of the studies included student learning outcomes as one of multiple variables (Harper and Lattuca, 2010; Ziegler, 2005). These findings underscored the need for a study regarding quality management systems and student learning outcome assessment in higher education. The results and conclusions of our study build on the existing knowledge in the field of quality management.

Research Question

Does the application of quality management principles at institutions of higher education with accredited business programs enhance student learning outcomes assessment processes? The following hypotheses were purported:

Hypothesis 1. The application of quality management principles at institutions of higher education with accredited business programs does not enhance student learning outcomes assessment processes. [[mu].sub.1] [not equal to] [[mu].sub.2]

Hypothesis la. The application of quality management principles at institutions of higher education with accredited business programs does enhance student learning outcomes assessment processes. [[mu].sub.1] = [[mu].sub.2]

Methodology

This quantitative methods study was used to evaluate the relationship between quality management theory and student learning outcome assessment at institutions of higher education with business programs accredited by ACBSR The quantitative methods study scored secondary data from variable number one, quality management theory, and from variable number two, student learning outcome assessment.

The analysis of the two variables provided quantitative results. A valid and reliable rubric was used to score both variables, and the scores resulted in quantitative data from an ordinal scale that was statistically analyzed (Creswell, 2009). The result was that the hypotheses were evaluated using the quantitative methodological research design.

Primary data from the random sample of 45 institutions with accredited business programs were scored using a valid and reliable process scoring guideline rubric (see Appendix A) to provide secondary data that were statistically analyzed for this study. Five quality management constructs were scored for each random sample and four student learning outcome assessment constructs for each random sample. The mean of the quality management constructs and the mean of the student learning outcome assessment constructs for each random sample institution provided scores that were statistically analyzed. The means for quality management were tabulated in one column using SPSS statistical analysis computer software, and the means for student learning outcome assessment were tabulated in an adjacent column using the same program.

The mean of the constructs produced an ordinal number for the variables. Since the scale was ordinal, the Pearson correlation could not be used. Spearman's rank-order correlation is a nonparametric measure of association using ordinal numbers and was used for this study. SPSS statistical analysis software computed the means of the two variables for each of the 45 institutions. The correlation coefficient was subjected to test of significance at 0.01 levels. Therefore, the statistical analysis determined whether the correlations were sufficiently different from chance expectations and not due to random sampling error (Zikmund, 1994).

The population consisted of 370 institutions of higher education worldwide with business programs accredited by the ACBSP. Business degree programs in higher education had to meet accreditation standards, and all accredited business programs used a quality management system to meet those standards. The standards consisted of the principles of a quality management system (ACBSP, 2012). Therefore, all business programs that were accredited implemented quality management principles.

The technique of simple random sampling was used to select a sample of 45 ACBSP-accredited business units from the population of 370 institutions. Each of the 370 reported how they met the quality management accreditation standards. The researcher had access to the primary data in these reports. No individuals participated in this study.

Non-parametric measures of bivariate relationships statistically analyzed the results from the data collected. The Spearman's rank-order correlation was performed on the results from the quantitative data collected from the random sample (Zikmund, 1994). The random sample represented the population.

The quantitative effectiveness of the quality management system implemented at accredited business programs was scored with a process-scoring guideline rubric developed by the Baldrige Performance Excellence Program (2012). This program was managed by the American Society for Quality through the National Institute of Standards and Technology. The process-scoring guideline rubric is in Appendix A. Scores from the rubric reflected the business unit's overall progress and maturity in quality management, as well as student learning outcomes assessment.

The Baldrige Performance Excellence Program developed objective, standardized rubrics. The rubrics have been used and improved over the years by hundreds of examiners in hundreds of schools, businesses, and medical facilities (2013). These rubrics were used to evaluate data and information from hundreds of accredited post-secondary business school reports, which were available to the researcher.

The process-scoring guideline rubric met the criteria of construct and content validity. Content was validity was established by agreement among quality management professionals that the scale accurately reflected what it was supposed to measure and the content of the scale was adequate (Zikmund, 1994). The theory of quality management as studied through the Baldrige Performance Excellence Program provided evidence of construct validity.

Several studies established the construct validity of the process-scoring guideline rubric through hypotheses testing (Arora, 2006; Evans, 2010; Leonard and Relier, 2004; Werner, 2007). Evans (2010) hypothesized that the median of independent review scores, using the process-scoring guideline rubric for three periods for manufacturing, were equal medians. The statistical analysis reported the mean independent review scores for each period as 463.82, 533.36, and 460.43, respectively. The Welch procedure yielded a p-value of 0.0005, rejecting the null hypothesis. The Kruskal-Wallis rank test yielded a p-value = .007, rejecting the null hypothesis of equality of medians (Evans, 2010). Rejecting the null hypothesis provided empirical evidence that validated the construct validity of the process-scoring guideline rubric (Zikmund, 1994).

The variable of quality management was scored by calculating the mean of five quality management constructs, using the process-scoring guideline rubric. The five constructs of a quality management system were (a) leadership, (b) strategie planning, (c) student and stakeholder focus, (d) faculty and staff focus, and (e) educational and business process management (ACBSP Standards and Criteria, 2012).

The rubric had an ordinal scale of six numbers; 1 through 6 made up the scale for scoring and statistical analysis. The higher the number, the more effective and efficient the quality management system. The process-scoring guideline rubric is in Appendix A.

The variable of student learning outcome assessment was scored by calculating the mean of four student learning outcome assessment constructs using the process-scoring guideline rubric. The four constructs scored were (a) systematic approach, (b) deployment, (c) results, and (d) improvements. Calculating the mean of the four constructs from the rubric assessment of student learning outcome assessment at the institutions provided the variable score. Scoring for both variables used the same rubric.

Institutions with accredited ACBSP business programs provided data and information for the study. Each business unit submitted self-evaluation reports containing primary data on quality management and on student learning outcome assessment. The researcher had access to the primary data and permission to use it.

The population of 370 institutions of higher education with accredited business units was used to gather a random sample of 45 business units using an Excel random sample generator. A number was assigned to each institution selected as part of the random sample, and the name of the institution was removed from the spreadsheet to ensure confidentiality.

Scoring the data and information resulted in ordinal scaled numbers for each construct. The total number of scores computed using the scoring guideline rubric from the sample of 45 was 5,355. These scores were turned into means for each of the six standards, and the total number of means used for statistical analysis was 270. The grand mean of the constructs produced an ordinal number for both variables. There were two columns of variable data and 45 rows of mean data. Each row of data represented the mean score for variables of quality management and the variables of student learning outcome assessment. The measures of correlation were subjected to test of significance. The results of this study showed that it was unlikely that the null hypothesis was true. Therefore, there appears to be an association between quality management and student learning outcomes.

Column two of Table B1 in Appendix B recorded the results for student learning outcome assessment scores. Column eight recorded the grand mean of the scores from columns three through seven, and the data in columns three through seven represented quality management standards.

Non-parametric measures of bivariate relationships analyzed the results from the data collected. SPSS statistical analysis software was used to perform Spearman's rank-order correlation on the results from the quantitative data collected from the random sample of 45 schools. Statistical analysis of the mean of student learning outcome assessment processes in relation to the grand mean of quality management standards using SPSS statistical application software answered the research question. Spearman's rank-order correlation test resulted in a correlation coefficient of .72. The correlation was significant at the 99% confidence interval or the 0.01 significance level. The correlation coefficient of .72 showed that it was unlikely that the null hypothesis was true.

The first assumption was that the data from this study employed an ordinal scale. The ordinal scale allowed statistical analysis using Spearman's rank-order correlation coefficient (Zikmund, 1994). The scoring instrument was not an equal scale. The results from the process-scoring rubric provided ordinal data.

The statistical assumption that there was a monotonie relationship between variables was required. When the value of one variable increased, the value of the other variable increased, or when the value of one variable decreased, the value of the other variable decreased (Reiss, 2009). Thus, a monotonie relationship was required to use Spearman's rank-order correlation.

The data resulting from measuring the random samples were analyzed using a scatter plot diagram within the SPSS statistical analysis software to determine if there was a monotonie relationship between the variables scored. A monotonie relationship was validated and the assumption was met.

Findings

The finding produced an overall correlation coefficient of .72 significant at the 0.01 level. This positive correlation added data and information to existing knowledge in management theory and provided evidence that implementing quality management correlates positively to processes of assessing student learning outcomes. The results of the scores taken for the study provided an opportunity to analyze additional segments.

Eight additional segments were developed from the constructs scored. The results were analyzed between student learning outcome assessment and baccalaureate and graduate degree business programs, associate degree business programs, and global programs located outside the United States. In addition, the results were analyzed between student learning outcome assessment and leadership, strategic planning, student and stakeholder focus, faculty and staff focus, and educational and business process management. The results proved to be interesting.

The two highest correlation coefficients were with strategic planning and baccalaureate/graduate degree business programs. The .85 correlation coefficient for baccalaureate and graduate business degree programs was the highest of the study and was significant at the 0.01 level. Strategic planning had the second highest correlation coefficient at .72 and was significant at the 0.01 level. The sample size for the baccalaureate and graduate degree sample was n- 23. The sample size for strategic planning was n - 45.

The two lowest correlation coefficients were for global business programs outside the United States and associate degree business programs. The correlation coefficient for associate degree business degree programs was .42. The correlation was not significant for the results of the associate degree business program segment. The correlation coefficient for global business programs scored outside of the United States was .27 and was not significant for the results of this business degree program segment. The global business degree program segment had the lowest correlation coefficient of all the segments scored and also the lowest sample size in the study.

A possible basis for the results of these two lowest business degree program segments may have been small sample sizes. The associate degree sample size was n= 14, and that of business programs located outside the United States was n = 8. A future study could be conducted with a sample size of 45 from each of these three segments.

The study proved correlation was significant and was associated with the variables. Even though correlation did not mean causation, the high correlation coefficient indicated a clear association of quality management with enhanced student learning outcome assessment results and was significant at the .01 level. Multivariate regression analysis was used to provide more in depth understanding of the relationships between all the variables studied. Therefore, the next step was to evaluate causation. Upon completion of the original study the data were converted to percentages, and multivariate regression analysis was used to evaluate the variables in much greater detail.

Drs. Plessner and Dumont ran the multivariate regression analysis and provided interpretations of the results (see Table 1). The initial two variable models (Models 1-5) used simple ordinary least squares regression, providing evidence that strategic planning had the greatest overall impact on student learning outcome assessment. Leadership was a significant factor in explaining student learning outcome assessment because the p-value for the t-test was less than or equal to 0.05. For every one unit value increase in leadership there was a Vi unit (.54) unit increase in student learning outcome assessment. The strategic planning p-value of the F indicates the model significantly explains student learning outcome assessment over and above what we would expect by chance. The Rsq for this model indicates that 33% of the variance in student learning outcome assessment was explained by quality management theory.

From the multiple regression models (Models 6-9), we see that Model 9 including all the standards had the highest adjusted R square, 33%, which indicates that 33% of the variation in student learning outcome assessment is explained by the other standards. Two thirds of the variation is still unaccounted for.

None of the standards significantly contributed to the model when controlling for the effects of the others, although a likely reason for this was the small sample size, which makes significant results more difficult to achieve. However the p-value for the F test (0.001) does show that the model is a significant improvement over chance in explaining student learning outcome assessment. Models 6 and 7 also showed that some standards had a significant effect when some variables were not included in the model.

Conclusions

The results demonstrated how the methodological choice was the best one to achieve the purpose of this study to provide answers to the specific research question. Spearman's rank-order correlation test resulted in a correlation coefficient of .72. The correlation coefficient was significant at the 99% confidence interval, 0.01 levels. The correlation coefficient of .72 supported the results of this study, proving that the null hypothesis was unlikely to be true. Therefore, there does appear to be an association between quality management and student learning outcomes.

Institutions of higher education are being challenged by internal and external stakeholders to provide evidence that they are achieving positive results educating students. Toward this end, they are often required to report on student learning outcome results for degree program learning objectives.

Assessing the results of educational processes was challenging due to the diverse missions of hundreds of institutions of higher education and thousands of degree programs. The ability to assess the results of educational processes was even more challenging since stakeholders who wanted the evidence were not able, or not willing, to provide guidance on how to assess student learning outcomes.

Logical conclusions that answered the research question and hypotheses evolved from the results. Institutions of higher education can use the results of this study to develop practical applications to assess student learning outcomes. In addition, the conclusions may be used to implement and sustain business quality management theory.

This research study significantly proved that the application of quality management principles at institutions of higher education with accredited business programs did result in an association with improved student learning outcomes assessment processes. The research question was answered from the data collected and statistically analyzed utilizing bivariate analyses and measures of association. The statistical analysis resulted in high correlation between the variables associated with the research question.

Variables that are not causally related can be statistically related, even though correlation does not indicate causation (Zikmund, 1994). The variables in the research question were statistically related, as proven through analyses. As noted, Spearman's rank-order correlation coefficient of .72 was significant at the 0.01 level. Therefore there does appear to be an association between quality management and student learning outcomes, so the research question was answered.

This study fulfilled the need for more information about the influence of quality management systems on performance outcomes such as student learning, student retention, and graduation rates in higher education (Elmuti et ah, 1996). The results of this study provided correlation coefficients for all of the standards scored that were significant at the 0.01 level. This was evidence that quality management systems made processes more effective and efficient at universities--as called for by Mehralizadeh and Safaeemoghaddam (2010) when they noted a clear need for such research.

Limitations

This study was limited to business units accredited by ACBSP. However, there are two other accreditors in the United States that are recognized as providers of program accreditation for business degrees. The Association to Advance Collegiate Schools of Business, International had 694 institutions of higher education accredited as of January 2014, according to their Web site. The International Assembly for Collegiate Business Education had 169 institutions of higher education accredited as of January 2014, according to their Web site. These two organizations did not participate in this study, and none of their information was available.

Implications for Administrators

Faculty and staff members must develop, deploy, evaluate, and report robust processes to comply with assessment standards and to maintain accreditation (Stivers and Phillips, 2009). Institutions of higher education continue to struggle to identify effective models that allow them to assess and report effective student learning outcomes (Ohia, 2011). The problem has been a lack of standardization, systematic process, and no consistent guidance on how to develop, implement, evaluate, and report student learning outcomes (Gehart, 2011; Kelley, Tong and Beom-Joon Choi 2010; Munoz, Jaime, McGriff and Molina, 2012; Petropoulou, Vassilikopoulou, and Retalis, 2011; Sidney and Chad, 2010).

The results of this study provided evidence of strong correlation coefficients at significant levels for all variables. Even though this could not be assumed to be causation, the statistical analysis makes a strong case for the theory of quality management being the cause of the relational correlation with the variables.

Empirical evidence indicated statistically significant differences in efficiency and effectiveness between quality management firms and nonquality management firms (Ahire et al., 1996). This conclusion contributed to our study. Researchers concluded that quality management principles were beneficial to institutions of higher education (Emiliani, 2005; Imran and Mahmood, 2011). This study provided a clearer picture, on a micro level, of the relationship between quality management and student learning outcomes.

All business schools, programs, and departments should implement quality management theory through the employment of accreditation processes. In 2014, 15,731 institutions of higher education had business programs worldwide, according to AACSB 's Business School Data Guide Book 2014 (AACSB International, 2014). Just over 1,000 had implemented quality management theory through accreditation as of February 2014--less than 7% worldwide. Therefore, 93% of these institutions could benefit from the results of this study. In the U.S., almost half of such institutions had implemented quality management theory through accreditation as of February 2014. The other half could benefit from doing so.

Future Studies

The study could be expanded using different methodologies that provide direct data and information from individuals, such as administrators and faculty members, relating to their personal and professional experiences working with quality management, stakeholders, and student learning outcome assessment.

Steven L. Parscale, Accreditation Council for Business Schools and Programs (ACBSP)

John F. Dumont, Northwest State Community College

Von R. Plessner, University of Liverpool-Laureate

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Appendix A
Process Scoring Guideline Rubric

SCORE     PROCESS (for use with categories 1-6)

0% or     * No systematic approach to item requirements is evident;
5%          information is anecdotal. (A)
          * Little or no deployment of any systematic approach is
            evident. (D)
          * An improvement orientation is not evident; improvement is
            achieved through reacting to problems. (L)
          * No organizational alignment is evident; individual areas
            or work units operate independently. (I)

10%,      * The beginning of a systematic approach to the basic
15%,        requirements of the item is evident. (A)
20%, or   * The approach is in the early stages of deployment in most
25%         areas or work units, inhibiting progress in achieving the
            basic requirements of the item. (D)
          * Early stages of a transition from reacting to problems to
            a general improvement orientation are evident. (L)
          * The approach is aligned with other areas or work units
            largely through joint problem solving. (I)

30%,      * An effective, systematic approach, responsive to the basic
35%,        requirements of the item, is evident. (A)
40%, or   * The approach is deployed, although some areas or work
45%         units are in early stages of deployment. (D)
          * The beginning of a systematic approach to evaluation and
            improvement of key processes is evident. (L)
          * The approach is in the early stages of alignment with your
            basic organizational needs (I)

50%,      * An effective, systematic approach, responsive to the
55%,        overall requirements of the item, is evident. (A)
60%, or   * The approach is well deployed, although deployment may
65%         vary in some areas or work units. (D)
          * A fact-based, systematic evaluation and improvement
            process and some organizational learning, including
            innovation, are in place for improving the efficiency and
            effectiveness of key processes. (L)
          * The approach is aligned with your overall organizational
            needs identified in response to the Organizational Profile
            and other process items. (I)

70%,      * An effective, systematic approach, responsive to the
75%,        multiple requirements of the item, is evident. (A)
80%, or   * The approach is well deployed, with no significant gaps.
85%         (D)
          * Fact-based, systematic evaluation and improvement and
            organizational learning, including innovation, are key
            management tools; there is clear evidence of refinement as
            a result of organizational-level analysis and sharing. (L)
          * The approach is integrated with your current and future
            organizational needs identified in response to the
            Organizational Profile and other process items. (I)

90%,      * An effective, systematic approach, fully responsive to the
95%, or     multiple requirements of the item, is evident. (A)
100%      * The approach is fully deployed without significant
            weaknesses or gaps in any areas or work units. (D)
          * Fact-based, systematic evaluation and improvement and
            organizational learning through innovation are key
            organization-wide tools; refinement and innovation, backed
            by analysis and sharing, are evident throughout the
            organization. (L)
          * The approach is well integrated with your current and
            future organizational needs identified in response to the
            Organizational Profile and other process items. (I)

Education Criteria for Performance Excellence, 2012.

Appendix B
Random Sample Scoring Data Results

      Standard #4   Standard #1   Standard #2   Standard #3
        Student     Leadership     Strategic    Stakeholders
       Learning                      Plan

1        5.00          5.00          5.00           5.00
2        5.00          5.00          5.00           5.00
3        4.00          5.00          5.00           5.00
4        5.00          4.00          5.00           5.00
5        3.50          4.80          4.67           5.00
6        5.00          4.80          4.75           4.86
7        6.00          5.00          5.00           5.00
8        4.50          4.00          5.00           4.86
9        4.50          4.00          4.25           4.43
10       5.00          4.00          4.00           5.00
11       4.00          4.17          4.29           4.14
12       5.75          5.50          5.50           5.50
13       4.50          4.86          4.71           5.14
14       5.00          6.00          5.40           5.60
15       5.00          4.75          5.00           5.00
16       4.75          5.00          4.50           4.00
17       5.00          5.00          5.25           5.57
18       4.75          5.30          5.00           5.75
19       6.00          5.00          5.13           5.25
20       5.08          4.88          4.86           5.00
21       5.50          4.75          5.15           5.43
22       5.00          4.50          5.00           4.75
23       5.72          5.25          5.25           5.33
24       5.00          5.00          4.50           4.71
25       5.75          5.50          5.50           5.25
26       5.70          5.00          4.73           5.27
27       5.75          5.56          5.63           5.25
28       5.53          5.63          5.71           5.43
29       2.88          2.00          4.50           4.00
30       4.19          4.40          4.45           4.80
31       5.67          5.67          5.40           5.60
32       6.00          4.71          5.75           5.29
33       5.28          5.25          4.92           5.40
34       5.35          5.13          5.00           5.14
35       5.35          5.25          5.20           5.38
36       5.59          5.71          5.75           5.71
37       5.13          5.12          5.14           5.33
38       5.54          5.43          5.33           5.43
39       5.31          5.14          5.29           5.14
40       5.16          5.13          5.13           5.13
41       5.39          5.25          5.20           5.11
42       5.38          5.29          5.20           5.38
43       5.22          5.25          5.00           5.09
44       5.15          5.25          5.20           5.22
45       3.13          3.00          3.17           3.00

      Standard #5   Standard #6     Grand Mean
        Faculty       Process     (- Standard 4)
                    Management

1        4.00          5.00            4.80
2        6.00          5.00            5.20
3        5.00          4.00            4.80
4        5.00          5.00            4.80
5        5.00          5.00            4.89
6        5.00          5.00            4.88
7        5.00          5.30            5.06
8        4.86          5.00            4.74
9        4.63          4.67            4.40
10       5.00          5.00            4.60
11       4.63          4.00            4.25
12       5.25          5.50            5.45
13       4.88          5.00            4.92
14       5.00          4.90            5.38
15       4.89          5.00            4.93
16       5.00          5.00            4.70
17       5.00          6.00            5.36
18       4.88          5.00            5.19
19       5.00          5.00            5.08
20       5.17          5.24            5.03
21       5.25          5.00            5.12
22       4.86          5.33            4.89
23       5.28          5.57            5.34
24       5.63          5.45            5.06
25       5.01          5.06            5.26
26       5.19          5.10            5.06
27       5.51          5.50            5.49
28       5.28          5.43            5.50
29       3.25          3.11            3.37
30       4.22          4.85            4.54
31       5.33          5.60            5.52
32       5.38          5.43            5.31
33       5.14          5.35            5.21
34       5.19          5.32            5.16
35       5.13          5.15            5.22
36       5.30          5.58            5.61
37       5.16          5.09            5.17
38       5.20          5.28            5.33
39       5.10          5.17            5.17
40       5.05          5.17            5.12
41       5.13          5.13            5.16
42       5.22          5.13            5.24
43       5.18          5.09            5.12
44       5.25          5.08            5.20
45       3.16          3.00            3.07


Dr. Parscale has been chief accreditation officer for the ACBSP since 2004. He retired from the U.S. Air Force after 28 years, has taught at a number of colleges and universities in the fields of leadership, management, and quality, and served on the Board of Examiners for the Malcolm Baldrige National Quality Award. Dr Plessner, a former Dean of Business and now professor has taught accounting economics, management, marketing, and statistics at Northwest State Community College and other institutions. Dr. Dumont is with the University of Liverpool-Laureate Online Education program.
Table 1. Summary of Regression Analysis Results

Model #         DV   IVs   Cor,   RSq   AdjRsq   P val,   P val,   b
                           r                     t-tst    F

Least Squares
Regression

1               #4   #1    .45    .20   .19      .002              .54
2                    #2    .58    .33   .32      .000              .84
3                    #3    .50    .25   .24      .001              .78
4                    #5    .42    .17   .15      .006              .71
5                    #6    .48    .23   .21      .001              .69

Multiple
Regression

6                    #5           .26   .23      .193     .002     .37
                     #6                          .036              .52
7                    #1           .34   .29      .037     .001     .37
                     #5                          .549              .17
                     #6                          .043              .48
8                    #1           .36   .29      .214     .002     .26
                     #3                          .297              .30
                     #5                          .599              .15
                     #6                          .123              .38
9                    #1           .41   .33      .443     .001     .16
                     #2                          .080              .84
                     #3                          .761              .09
                     #5                          .849              .05
                     #6                          .155              .35
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Publication:SAM Advanced Management Journal
Date:Sep 22, 2015
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