UsingParetoAnalysiswithTrendAnalysis:StatisticalTechniquesto InvestigateIncidentReportsWithinaHousingSystemAndrew L. LunaUNIVERSITYOF lOUISVlllE

INTRODUCTION

Although the concepts of Total OualityManagement (TOM) have reached the halls ofacademe, they still have not gained wideacceptance among many higher educationprofessionals. Many still are skeptical about theoutcomes of a system which was primarilydesigned for use throughout the businesscommunity (Fisher, 1993). Through the works ofEdward Demming (1986) and Juran and Gryna(1988), TOM is identified as a way of increasingquality while decreasing costs through acombination of teamwork and scientificmeasurement.

Within higher education, professionals havebecome interested in TOM's declaration of qualityservice, cost efficiency, and customer satisfaction(Astin, 1985; Barr, Upcraft, & Associates, 1990;Coate, 1991; Kuh, Schuh, Whitt, & Associates,1991; Seymour, 1992; Sherr & Teeter, 1991;Upcraft, Gardner, & Associates, 1989). BecauseTOM is still rather new to higher education, someproblems have existed which have raisedquestions regarding the effectiveness of TOMwithin the educational or service environments(Van Allen, 1994).

Although higher education and industrydiffer substantially in their ideologies andmissions, the problems both groups mayexperience with TOM can be traced to a fewcommon factors such as time limitations, a lackof definitive measurements, vague missionstatements, and too much time spent on trivialities(Early & Godfrey, 1995).

Probably one of the greatest obstacles higher

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education professionals face with implementingTOM is a lack of consistent and definitivemeasurement techniques to synthesize andexamine quality. Measurement is an essentialfunction in the science of TOM in order to make

significant change to any process (Luna, 1996;Teeter & Lozier, 1993). In TOM, statistical rulesand empirical observations of a process are usedto make inferences about the quality of thatprocess (Mawhinney, 1992). Through thisstatistical process control, it is possible for TOMteams to scientifically measure the effects andchanges of their new ideas to any given process.

This article addresses two simplemeasurement techniques, used in tandem, whichany higher education professional can use tounderstand a particular process, make changesto it, and measure whether or not those changesare effective in increasing quality within thatprocess. The focus of this study, therefore, is toshow how the statistical techniques of TOM canbe used to stimulate and focus discussion on

improving process quality within any institution.

METHOD

The purpose of this study was to determine trendsand difficultiesconcerning student incident reportswithin the residence halls as they relate to theincident reporting system from the Department ofHousing and Residential Life at a SoutheasternDoctoral I Granting Institution. Furthermore, thisstudy was used by housing staff to identify areasof change to the current system which wouldincrease the overall quality and integrity of theincident reporting process.

The incident reporting system is a processwhereby housing staff kept track of any physicalor social problems occurring within the housingcommunity. Resident Assistants, Hall Directors,and Area Coordinators were responsible forinitiating a report after an incident occurred.Each report was logged into a database for usein the department's future planning.

Incident classifications were separated into12 categories: Alcohol, Assault (Physical), FireSafety, Harassment (Verbal), Medical,Miscellaneous, Noise, Sanitation, Security, Theft,Vandalism, and Visitation. The classification ofMiscellaneous included all incidents which could

not be placed into any of the remaining 11classifications.

This study used the frequency distributionsof each classified incident by month for the 1993-94 and 1994.95 academic years (N = 773).

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Because this study was interested in the trends aswell as the problems within the system, both thePareto Analysis and Trend Analysis were usedwith the data.

The Pareto Analysis is a bar graph whichdisplays the frequency and importance of eachclassification (Tague, 1995). The principalbehind the Pareto Analysis is to identify the fewvital causes that often are attributed to most of

an effect ~uran & Gryan, 1988). In this study,each classification of incident is plotted on thebottom of the chart and the number of occurrences

are plotted on the left axis (see Figure 1). Thecumulative percent of each classification is plottedalong the right axis. A line graph is used to plotthe cumulative percentage of occurrence. Theclassifications which made up 80% of theoccurrences were considered vital problems. Thiscumulative ranking is referred to as the 80%margin of concern in the Pareto Analysis.

Although the Pareto Analysis is a useful toolfor TQM, it has the following drawbacks: (a) itsinability to effectively analyze small amounts ofdata, (b) its inability to measure variation overtime, (c) its inability to measure trends (Duffy,1995).

To limit these problems, the Trend Analysisor Run Chart was used. The Trend Analysis showsa measurement located on the vertical axis that

is plotted against time on the horizontal axis (seeFigure 2). As the measurement increases ordecreases over time, the upward or downwarddirection (trend) can be visualized easily on thechart. The purpose of this analysis was tomeasure variation within each of the incident

reporting categories and to analyze any trendsor patterns that were present (Tague, 1995).

RESULTS

The results of the Pareto Analysis revealed thatMiscellaneous, Noise, Visitation, Fire Safety,Alcohol, and Vandalism classifications were

within the 80% margin of concern (see Figure3). Because Security, Assault, and Sanitationincidents individually made up such a smallpercentage of the total number of incidents, theywere combined into the bar labeled "Other,"One would assume, after examination of the

Pareto Analysis, that those classifications withinthe 80% margin of concern were the mostimportant and, therefore, should be addressed

immediately. By utilizing the trend analysis,however, the direction and pattern of eachclassification could be analyzed to determine if

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it indicated a downward or upward trend, or ifthe variation was due to a seasonal pattern.When the Pareto Analysis was compared withthe Trend Analysis, and each classification wasindividually explored, the following findings weremade:

1. The Miscellaneous classification was too

high. To begin, a miscellaneous classificationshould have included all incidents that were not

part of the other 11 classifications. Ordinarily,the frequency within this classification should notexceed the frequency of the other individualclassifications. In this case, the total number ofMiscellaneous incidents amounted to 246, whichwas much greater than any of the otherclassifications. After analyzing the individualrecords, it was found that many incidentscontained within this classification were

mistakenly identified and should have beenplaced in one of the 11 other classifications. Theindividual records also revealed a substantial

number of incidents relating to maintenanceproblems. Because the maintenance classificationwas not present in the reporting system, it wasdifficult to identify trends and patterns within thisarea.

2. The trend for Noise and Vandalism

indicated a significant decrease in frequency.Although Noise and Vandalism classificationswere the second and sixth largest problemscontained in the Pareto Analysis respectively, theTrend Analysis indicated a significant decreasein the frequency of incidents over the two-yearperiod. The higher number of incidents at thebeginning of the measurement period for eachof the classifications affected the overall number

of incidents within these classifications, causingthem to show a higher cumulative percentage.This inflation within the cumulative percentagecould not have been detected by the Pareto alone.

3. The variations in Alcohol and Fire Safetyseemed to show seasonal patterns. Althoughclassifications were shown in the Pareto Analysisas important, their importance may only be ofconcern during particular times of the year-especially during the beginning of the fall andspring semesters for Alcohol incidents and duringFebruary for Fire Safety incidents. Higher incidentnumbers during peak times for theseclassifications, again, caused an inflatedcumulative percentage value within the ParetoAnalysis.

4. Visitation was a consistent problem.According to both the Pareto and Trend Analyses,visitation incidents seemed consistenf and within

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the 80% margin of concern. A visitation incident. occurred when a student of one gender was found

in a residence hall housing the opposite genderduring times not assigned for visitation. Althoughthis classification indicated a decreasing trendtoward the end of each academic year, both thePareto and Trend Analyses indicated that thefrequency and consistency of this type of incidentshould still be a concern for housingadministrators in order to maintain the safety andcomfort level of student residents.

In addition to these findings, the study alsoshowed a significant drop-off in Security incidentsafter the fall 1993 semester. Although some ofthis drop-off can be attributed to misclassification,the frequencies of Security incidents also wereinconsistent with other internal and external

reporting methods. Furthermore, although theMedical problem classification was not within the80% margin of concern, its trend indicated apossible area of future concern for housingadministrators.

IMPLICATIONS AND RECOMMENDA-TIONS

This study demonstrated the combined use of thePareto and Trend Analysis tools for detectingtrends and problems within the residence hallincident reporting system at a SoutheasternDoctoral I Granting Institution. By using theseTQM tools in tandem, this study identified bothtrends of incidents and problems with the incidentreporting process.

Although the Pareto Analysis indicated thatsix classifications were within the 80% margin ofconcern, the Trend Analysis indicated that Noiseand Vandalism incidents were significantlydecreasing in number and that Alcohol and FireSafety incidents were seasonal. Both analysesindicated the Visitation classification was a majorconcern and both indicated problems existed withincidents classified as Miscellaneous.

Through this study, a better understandingof the incident reporting system was achievedand recommendations for the improvement of thesystem were made. The first recommendationconcerned the Miscellaneous classification.

Because hall staff tend to mis-classify incidentsunder this category, staff entering incident reportsinto the database should be better trained and a

system for checking mistakes should be includedwithin the overall process.

Many of the Miscellaneous classificationsincluded maintenance problems within the

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personal and common space of the buildings.Because of the large frequency of incident reportsinvolving maintenance, a separate maintenanceclassification should be included within the

system.Both the Pareto and Trend Analysis tools

indicated a concern with the visitation incidents.

After careful observation, visitation was seen as

a consistent problem and in need of interventionby the hall staff.

Although Noise and Vandalism wereconsidered important under the Pareto Analysis,they revealed a decreasing trend month to month,indicating some sort of intervention by hall staffor other variable. If this intervention came from

the hall staff, it could have been in the form ofeducation, enforcement, or communication. It is

important, therefore, for the hall staff to identifythe most effective areas of intervention and

continue with their efforts to keep these numberslow.

Alcohol and Fire Safety also were consideredimportant under the Pareto Analysis but indicatedonly seasonal activity under the Trend Analysis.This simply means that hall staff need to becomemore aware of the peak times these incidentsoccur and develop programming or otherintervention activities beforehand to decrease

incident numbers during these times.Two additional recommendations resulted

from this study. The staff should re-evaluate theirefforts to identify security incidents so that theseproblems will be more easily detected and thatthe Security incident will be consistent with otherinternal and external reporting. Furthermore, thestaff evaluate each medical incident and

determine what measure, if any, housingpersonnel could have taken to decrease thesetypes of incidents or, by intervening, prevent themfrom occurring in the future.

CONCLUSION

Although TQM has yet to win acceptancethroughout the academic community, its conceptsand tools can be effectively used to stimulatediscussion on improving process quality withinhigher education. It is important, however, tounderstand that measurement techniques arecritical to the successful implementation of TQM.As seen in this study, useful measurement toolsare available to synthesize and evaluate qualitywithin higher education. This study also foundthat the combination of the Pareto and Trend

Analysis provides a powerful instrument for higher

JOURNAL OF COLLEGEAND UNIVERSITY STUDENT HOUSING

education professionals by giving them moreinformation than if these tools were used

separately.Not only do these tools identify the major

problems within a system, they assess long-termand seasonal trends, and the effectiveness ofintervention or corrective actions. When used

as an ongoing measurement tool, problems mayeven be eliminated before they become problems.Furthermore, the design and use of these tools isfast and easy, allowing most higher educationprofessionals to utilize them even if they havelittle time or TQM training.

REFERENCES

Astin, A. W. (1985). Achieving educational ex-cellence: A critical assessment of prioritiesand practices in higher education. SanFrancisco: Jossey-Bass.

Barr, M. J. Upcraft, M. L., & Associates.(1990). New futures for higher educa-tion: Buifding a vision for professionaffeadership and practice. San Francisco:Jossey-Bass.

Coate, L. E. (1991). Implementing total qualitymanagement in a university setting. In L.A. Sherr, & D. J. Teeter (Eds.). Totafqualitymanagement in higher education (pp. 27-38). San Francisco: Jossey-Bass.

Demming, W. E. (1986). Out of the crisis. Cam-bridge, MA: Center for Advanced Engineer-ing Study, Mil

Duffy, R. E. (1995). Pareto analysis and trendcharts: A powerful duo. Quafity Progress,28(1), 152.

Early, J. F., & Godfrey, A. B. (1995). But it takestoo long...: A Juran Institute study identifiesspecific causes for excessive time spent com-pleting quality improvement projects. Qual-ity Progress, 28(7), 51-55.

Fisher, James, L. (1993). TQM: A warning forhigher education. Educational Record,74(2), 15-1 9.

Juran, J. M., & Gryna, F. M. (Eds.). (1988).Juran's quality control handbook. NewYork: McGraw Hill.

Kuh, G. D., Schuh, J. H., Whitt, E. J, & Associ-ates. (1991). Involving colleges: Success-Fulapproaches to fostering student learn-ing and development outside the cfassroom.San Francisco: Jossey-Bass.

Luna, A. L. (1996). Workorder nonconform ityand the TQM process. Journal of Collegeand University Student Housing, 25(2), 31-37.

Mawhinney, 1 C. (1992). Total quality manage-ment and organizational behavior manage-ment: An integration for continual improve-

VOLUME27, NUMBER2 1998

ment. Journal of Applied Behavior Analy-sis, 25(3), 525-543.

Seymour, D. 1 (1992). On Q: Causing qualityin higher education. New York:Macmillan.

Sherr, L.A., & Teeter, D.J. (Eds.). (1991). Totalquality management in higher education.Sqn Francisco: Jossey-Bass.

Teeter, D. J., & Lozier, G. G. (Eds.). (1993). Pur-suit of quality in higher education: Case stud-ies in totalqualitymanagement. San Fran-cisco: Jossey-Bass.

Tague, N. R. (1995). The quality toofbox. Mil-waukee, ASQC Press.

Upcraft, M. L., Gardner, J. N., & Associates.(1989). The Freshman year experience:Hefping students survive and succeed incollege. San Francisco: Jossey-Bass.

Van Allen, G. H. (1994). Failures of total qual-ity management: Products of leadership andorganizational culture. Community CollegeJournal of Research and Practice, 78(4),381-390.

For additionaf inFormation contact Andrew L.

Luna, Research Anafyst, University of Louisville,University of Louisville, Louisville, KY40292.

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