Developing a predictive metric to assess school viability.

Developing a predictive metric to assess school viability. This article examines a wide range of parish school indicators thatcan be used to predict long-term viability. ********** Catholic elementary school elementary school:see school. enrollment peaked in 1965, when 4.491million students were educated in over 10,000 Catholic schools acrossthe country (United States United States,officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. Catholic Conference, 1976). Since then,Catholic elementary school enrollment and the number of schools havedropped back to 1920 levels (see Table 1). Numerous reasons for thisdecline have been offered. McLellan (2000) identified the lack ofleadership on the part of the Church hierarchy and their unwillingnessto make critical changes in governance and administration, thediminished value and utility placed on Catholic schools by Catholicparents who had entered the American economic and cultural mainstreamafter World War II, theological shifts regarding the purpose andeffectiveness of Catholic education in the period immediately followingVatican II Noun 1. Vatican II - the Vatican Council in 1962-1965 that abandoned the universal Latin liturgy and acknowledged ecumenism and made other reformsSecond Vatican CouncilVatican Council - each of two councils of the Roman Catholic Church , organizational changes, such as the declining number ofvowed women religious and the hiring of lay teachers, and demographicshifts as reasons for the decline. CHANGING DEMOGRAPHY demography(dĭmŏg`rəfē), science of human population. Demography represents a fundamental approach to the understanding of human society. During the later part of the 20th century, Catholics participatedin the mass migration of the White middle class from the city to thesuburbs (Convey, 1992; Greeley, 1959; McGreevy, 1996). As the Whitemiddle class moved out, African Americans, who are proportionately pro��por��tion��ate?adj.Being in due proportion; proportional.tr.v. pro��por��tion��at��ed, pro��por��tion��at��ing, pro��por��tion��atesTo make proportionate. farless likely to be Catholic, moved into the urban areas. Catholics had abandoned the inner-city parish-schoolinfrastructure, moving to the suburbs en masse en masse?adv.In one group or body; all together: The protesters marched en masse to the capitol.[French : en, in + masse, mass. , thereby greatlyexpanding the population in areas lacking parish schools. The Diocese ofCleveland provides a vivid example of what has happened in many urbancenters. The City of Cleveland lost 409,192 residents between 1950 and1990, while Cuyohoga County grew by 432,000 (Harris, 1996). In 1950,average parish membership in the city and in the suburbs was 2,668 and2,488 respectively; by 1990, the city parishes had dropped in membershipto an average of 1,666, while the suburban parish average membership hadmore than doubled to 5,617 (Harris, 1996). More recent quantitative research Quantitative researchUse of advanced econometric and mathematical valuation models to identify the firms with the best possible prospectives. Antithesis of qualitative research. supports the contention thatchanging demography has contributed to the closure of Catholic schools.McLellan (2000) found that the 20 dioceses with the largest Catholicschool enrollment (the Top 20) accounted for 62% of the nationalCatholic elementary school population in 1940, but only 42% of thenational Catholic elementary population in 1990. Furthermore, Top 20enrollment declines from 1960 to 1990 were related to White populationdeclines in their central cities. Interestingly enough, McLellan (2000)found that the more urban dioceses seemed to be more successful instemming the decline in the proportion of parishes with schools. Thisparadoxical finding is reconciled when consideration is given to theHerculean efforts made by large urban dioceses to keep urban parishschools open even while the White Catholic population vacated the parishboundaries. A recent study by the Center for Applied Research in the Apostolate a��pos��to��late?n.1. The office, duties, or mission of an apostle.2. An association of individuals for the dissemination of a religion or doctrine. (CARA; 2006) found that "some schools reached critical tippingpoints during this period [2000-2005] as the demographic changes thathad been taking place for more than five decades caught up with the mostvulnerable campuses" (p. 1). The study found that in 2005, 22% ofCatholic elementary schools were located in counties that had a loss inCatholic population or very low growth since the 1950s: "Thecurrent and emerging geographic centers for potential Catholicelementary students in the 21st century no longer closely overlap aswell with the Catholic elementary school system that was primarilydesigned and built in the early 20th century" (p. 2). FINANCIAL ISSUES The number of vowed religious serving in Catholic schools droppedprecipitously during the 1960s. By 2005, religious teachers in Catholicschools comprised 5% of the total teaching staff, down from the slightlygreater than 90% common prior to 1950 (see Table 2). Consequently, parish schools had to replace the religious with layfaculty at considerably higher cost, since the religious were often paidvery meager mea��geralso mea��gre ?adj.1. Deficient in quantity, fullness, or extent; scanty.2. Deficient in richness, fertility, or vigor; feeble: the meager soil of an eroded plain.3. stipends far below market value. Unfortunately, the capacity for the parish to accommodate thesefinancial demands diminished during this same time period. Harris (1996)notes that "Catholic giving as a proportion of household incomedeclined sharply between 1965 and 1984" (p. 2). Parishes respondedby cutting back on increases to the subsidy given to the parish school.The percentage of parish school income from the parish dropped from 63%in 1969 (Bredeweg, 1980) to less than 25% at present, while tuition grewto account for over 60% of parish school income (Bimonte, 2004). Thelarge increases in tuition, the diminished value and utility placed onCatholic schools by Catholic parents, and the changing demographics The attributes of people in a particular geographic area. Used for marketing purposes, population, ethnic origins, religion, spoken language, income and age range are examples of demographic data. , ledto smaller enrollments, further exacerbating the precarious financialposition of many Catholic elementary schools. Commenting on these trendsHarris (2000) rightly concludes, "Catholic schools have evolvedfrom a Church-funded endeavor managed by professed pro��fess?v. pro��fessed, pro��fess��ing, pro��fess��esv.tr.1. To affirm openly; declare or claim: "a physics major religious to a systemof largely parent-funded programs for a diminishing portion of theCatholic school population" (p. 56). Lundy (1999) attempted to identify differences between"survivor" and "non-survivor" parish elementaryschools from the Archdiocese arch��di��o��cese?n.The district under an archbishop's jurisdiction.archdi��oc of Chicago for the period spanning1991-1994. "Survivor" schools were those Catholic elementaryschools that were sponsored by a single parish as of June 1994, and werenot scheduled for closure or consolidation the following fall."Non-survivor" schools were defined as those that had beenclosed or consolidated between 1991 and 1994. Tests for correlationswere performed between survivor status and a number of financialvariables, and the best independent predictors were then used in adiscriminant function analysis Discriminant function analysis involves the predicting of a categorical dependent variable by one or more continuous or binary independent variables. It is statistically the opposite of MANOVA. to predict survivor status. Lundy (1999) found that the sum of two ratios, one indicating thefinancial solvency of the parish and the other indicating the financialsolvency of the parish school, was the single best predictor of survivorstatus, correctly classifying 85.9% of the schools. The sum of the tworatios was termed the "keyratio" and defined as the sum ofparish compensation expenditures divided by parish-collection income andschool compensation expenditures divided by tuition-and-fees revenue(see Figure 1).Figure 1. Definition of keyratioKeyratio = Parish compensation expenditures/Parish collection income + School compensation expenditures/School tuition and fees revenue Lundy (1999) found that most parishes with a keyratio of 1.75 orbelow had balanced budgets, while most above that level were in deficitspending Deficit spendingWhen government spending overwhelms government revenue resulting in government borrowing.deficit spendingExpenditures that are in excess of revenues during a given period of time. . Other strong independent predictors of survivor statusincluded the percentage of school income spent on compensation (80.2%correct classification), school compensation costs divided by tuitionand fee revenue (79.7% correct classification), and parish total balance(76.7% correct classification). Two demographic variables also served asstrong predictors of survivor status: K8 enrollment (62.6%) and Catholicenrollment (65.6%). STUDY This study explored the relationship between demographic variables,financial variables, and parish grade school closures in the Archdioceseof Saint Louis Saint Louis(l`ĭs), city (1990 pop. 396,685), independent and in no county, E Mo., on the Mississippi River below the mouth of the Missouri; inc. as a city 1822. St. . Specifically, this study investigated whetherstatistically significant and substantively meaningful differences existbetween open and closed schools on selected demographic and financialvariables. Discriminant function analysis was utilized to create a modelfor predicting parish school viability. The final portion of this studydescribes the translation of this discriminant function analysis into adiagnostic tool that could be used as an early warning system to assessschool viability on an ongoing basis. METHODOLOGICAL CONSIDERATIONS This study was limited to the quantitative analysis Quantitative AnalysisA security analysis that uses financial information derived from company annual reports and income statements to evaluate an investment decision.Notes: of selectdemographic and financial variables. It does not investigate the otherqualitative reasons for school closure cited by McLellan (2000), namely,the influence of the Church hierarchy, the attitudes of Catholic parentstoward Catholic schools, or theological shifts. The first part of the study provided a general profile of both openand closed parish schools through a comparison of means of 21demographic and financial variables using independent t tests. Due tothe large number of t tests conducted, the alpha required forsignificance was lowered to .002 to control for type 1 errors (.002 * 21= .042, below the standard threshold of .05). The study then utilizeddiscriminant function analysis to create a model for predicting parishschool viability. Discriminant function analysis was selected for use inthis study because of its ability to classify clas��si��fy?tr.v. clas��si��fied, clas��si��fy��ing, clas��si��fies1. To arrange or organize according to class or category.2. To designate (a document, for example) as confidential, secret, or top secret. each case (school) into adichotomous di��chot��o��mous?adj.1. Divided or dividing into two parts or classifications.2. Characterized by dichotomy.di��chot category (open or closed). Twenty-one variables thought to potentially capture aspects of thedemographic changes and financial issues were selected for inclusion.While a more sophisticated methodology (such as factor analysis) woulduncover latent Hidden; concealed; that which does not appear upon the face of an item.For example, a latent defect in the title to a parcel of real property is one that is not discoverable by an inspection of the title made with ordinary care. characteristics behind these variables (e.g., the factor"demographic decline" characterized by small parishpopulations, low baptismal bap��tism?n.1. A religious sacrament marked by the symbolic use of water and resulting in admission of the recipient into the community of Christians.2. rates, etc.), this study employed onlyreadily accessible variables with the explicit intent of creating adiagnostic tool that could be used as an early warning system to assessschool viability on an ongoing basis. Parish boundaries were coded into a geographic information system geographic information system (GIS)Computerized system that relates and displays data collected from a geographic entity in the form of a map. The ability of GIS to overlay existing data with new information and display it in colour on a computer screen is used primarily to (GIS (1) (Geographic Information System) An information system that deals with spatial information. Often called "mapping software," it links attributes and characteristics of an area to its geographic location. ) that enabled United States census The United States Census is a decennial census mandated by the United States Constitution.[1] The population is enumerated every 10 years and the results are used to allocate Congressional seats ("congressional apportionment"), electoral votes, and government program data to be linked directly tothe parish boundaries. The demographic and financial variables wereentered into an Excel spreadsheet and then transferred into SPSS A statistical package from SPSS, Inc., Chicago (www.spss.com) that runs on PCs, most mainframes and minis and is used extensively in marketing research. It provides over 50 statistical processes, including regression analysis, correlation and analysis of variance. forstatistical analysis. Using census data disaggregated Broken up into parts. by parish boundaries assumes thatparishioners and school parents have the same demographic and financialcharacteristics as the general population that resides within the parishboundaries. However, Catholics routinely cross parish boundaries inselecting their parish. Therefore, a threshold for including a parish inthe study needed to be created that would maximize the inclusion ofparish schools while ensuring the integrity of data attributed toparishes. The Annual Catholic Appeal provided the best available recordof the addresses of every household within the archdiocese. Theseaddresses were coded into GIS and provided data for every parish on thepercentage of parishioners who actually reside within the parishboundaries. The mean for the archdiocese was determined to be 67%, and athreshold of 50% within-parish residency A duration of stay required by state and local laws that entitles a person to the legal protection and benefits provided by applicable statutes.States have required state residency for a variety of rights, including the right to vote, the right to run for public office, the of parishioners was establishedfor the inclusion of a parish school in this study. This criterioneliminated only a handful of parish schools and none of the closedparish schools. The years spanning 2000-2005 were examined as a group in order togenerate a sufficient number of closed schools to make possible aquantitative statistical analysis. Doing so created the need for a soundmethodology for making comparisons among schools with data fromdifferent years. Comparison year (CY) refers to the most recent year ofoperation of a particular school; that is the 2004-2005 school year forthe open schools and the last year of operation for the closed schools.Fiscal year (FY) refers to the fiscal year for the Archdiocese of St.Louis which runs from July 1 of the prior calendar year to June 30 ofthe year represented. Adjusted median household income The median household income is commonly used to provide data about geographic areas and divides households into two equal segments with the first half of households earning less than the median household income and the other half earning more. and the variablesusing adjusted income were calculated using the urban consumer priceindex (CPI-U) for St. Louis (Bureau of Labor Statistics Bureau of Labor Statistics (BLS)A research agency of the U.S. Department of Labor; it compiles statistics on hours of work, average hourly earnings, employment and unemployment, consumer prices and many other variables. , 2007). Inconstructing this variable, the CPI-U from the spring prior to theacademic year was used. The logic for this choice was that parents areoften informed of tuition and are required to pay a pre-registration feein the spring of the year preceding the school year. Thus thecalculation of affordability on the part of the parents is most likelybased on income in the spring preceding the academic year. Only schoolsthat closed after the 1999-2000 school year used the actual medianadjusted income from the 2000 United States census (1999 annual; UnitedStates Census Bureau The United States Census Bureau (officially Bureau of the Census as defined in Title 13 U.S.C.11) is a part of the United States Department of Commerce. , 2007) without any adjustment. Other variables werenot adjusted unless otherwise noted in their definitions in Table 3. SAMPLE Open schools were defined as Catholic elementary schools sponsoredby a single parish in the Archdiocese of St. Louis that were notscheduled for closure for the 2005-2006 school year. Closed schools weredefined as Catholic elementary schools that had closed in the spring of2000 through the spring of 2004 or were not scheduled to re-open in thefall of 2005. Parishes without parish schools, parishes that sponsoredconsolidated schools, and parish schools that did not meet the thresholdlevel Noun 1. threshold level - the intensity level that is just barely perceptibleintensity, intensity level, strength - the amount of energy transmitted (as by acoustic or electromagnetic radiation); "he adjusted the intensity of the sound"; "they measured the of parishioners living within parish boundaries were not includedin this study. Ultimately 138 schools were used in the analysis (99 openschools and 39 closed schools). DATA Financial and demographic data for parish schools and theirparishes were obtained from the Archdiocese of St. Louis for the periodof 2000 through 2005. Data were obtained from the Archdiocesan arch��di��o��cese?n.The district under an archbishop's jurisdiction.archdi��oc FinanceOffice, the Status Animarum, and the school data form. The StatusAnimarum is an annual report completed each year by pastors thatcontains data on both the school and the parish. The school data form isan instrument that is sent out every summer to every school to becompleted by the administrator. The school data form requests data onenrollment, finances, staffing, programs, and resources. The financialdata from the school data form were cross referenced against financialdata obtained from the Archdiocesan Finance Office for accuracy. Additional financial data such as median household income werecollected from the 2000 Census (United States Census Bureau, 2007).Parish membership data were obtained from the records of theArchdiocesan Annual Catholic Appeal. All data are for the comparisonyear (CY) unless otherwise stipulated. Ultimately 21 variables were usedthat are described below in Table 3. RESULTS: INDEPENDENT T TESTS A series of independent t tests comparing open and closed schoolswere run. Eighteen of the 21 comparisons of means between open andclosed schools were statistically significant at the .002 level (seeTable 4). Among the variables that had means with no statisticallysignificant difference were tuition ($2,591 for open and $2,740 forclosed schools), and giving as a percentage of median household income(1.22% for open and 1.29% for closed schools). The means for giving as apercentage of median household income were remarkably similar to the1.2% giving percentage of Catholics reported in a recent national study(Zech, 2000). The most surprising finding was that there was nostatistically significant difference in the keyratio between open andclosed schools, and that the open schools actually had a higher averagekeyratio. While Lundy (1999) found parishes with non-survivor schoolsspent 77.4% of their income on compensation and parishes with survivorschools spent only 53.2% of theirs on compensation, the present studyfound that parishes with closed schools spent 48% of their income oncompensation and the open ones spent 57% of their income oncompensation. Clearly, the keyratio variable that captured the dynamicoperative in Chicago in the early 1990s was not correlated cor��re��late?v. cor��re��lat��ed, cor��re��lat��ing, cor��re��latesv.tr.1. To put or bring into causal, complementary, parallel, or reciprocal relation.2. with schoolclosures in St. Louis in the early 2000s. The effect size (d) displayed in Table 4 was calculated usingCohen's (1988) d, defined as the difference between means dividedby the pooled standard deviation Pooled standard deviation is a way to find a better estimate of the true standard deviation given several different samples taken in different circumstances where the mean may vary between samples but the true standard deviation (precision) is assumed to remain the same. : d = ([M.sub.1] - [M.sub.2])/[squareroot of ([([SD.sub.1]).sup.2]] + [([SD.sub.2]).sub.2]]/2). Nearly allthe effect size differences were large (.8 and above), with only a fewthat could be classified as medium (.5) and none that could beclassified as small (.2). The most substantively meaningful differences between open andclosed schools as measured by the effect size involved changes inenrollment and total enrollment (pchgsch-1, pchgsch-3, pchgsch-5, andnsch-1), parish finances (ptotinc04, adjhseinc, hseinc00), and parishdemographics (npar, totbapt, nhshlds). A line graph In graph theory, the line graph L(G) of an undirected graph G is a graph such that each vertex of L(G) represents an edge of G; and any two vertices of L(G comparison of openand closed schools' average enrollment for the 5 years prior to thecomparison year can be found in Figure 2. [FIGURE 2 OMITTED] TIPPING POINTS The CARA study (2006) suggests that the recent school closures werethe result of schools reaching "critical tipping points thatquickened between 2000 and 2005" (p. 1). Large effect sizedifferences between open and closed schools in the areas of enrollment(in years 1, 2, 3, and 5 prior to CY), as well as the precipitousdecline in the mean enrollment of closed schools in the 3 years prior toclosure led the investigators to think in terms of a tipping point The point in time in which a technology, procedure, service or philosophy has reached critical mass and becomes mainstream. See network effect. See also tip and ring. . Lundy (1999) found that the average enrollment of survivor schools(345) was nearly twice as large as non-survivor schools (175). Thepresent study found that open schools had an average enrollment one yearprior to CY of 284, while closed schools had an average enrollment of134. Could there be a tipping point, a threshold enrollment, where oncebroken, changes in enrollment become very significant for reasons ofefficiency, capacity, and per pupil cost, not to mention marketability?In order to investigate this possibility, the variable"above200" was created, a dichotomous variable thatdistinguished the schools based upon this threshold enrollment of beingeither above 200 students or not. The rationale for 200 as the thresholdis suggested from the data illustrated in Table 4. The number alsoapproximates school enrollment with a kindergarten kindergarten[Ger.,=garden of children], system of preschool education. Friedrich Froebel designed (1837) the kindergarten to provide an educational situation less formal than that of the elementary school but one in which children's creative play instincts would be through eighth gradeschool with one class per grade and 25 students per class. RESULTS: DISCRIMINANT FUNCTION discriminant functionn. StatisticsA function of a set of variables used to classify an object or event. ANALYSES The 18 variables with significant differences between open andclosed schools and the newly created "above200" variable wereincluded in a discriminant function analysis predicting status.Variables that were highly correlated with one another were removed fromthe analysis. The discriminant function analysis took the remainingvariables ([x.sub.0], [x.sub.1], [x.sub.2], ...) that were reliablycorrelated with another variable (status) and created an equation. Theanalysis generated coefficients ([a.sub.0], [a.sub.1], [a.sub.2], ...)for the variables ([x.sub.0], [x.sub.1], [x.sub.2], ...) and a constant(C) that maximized the difference between the quotients of open andclosed parish schools: DF = [a.sub.0][x.sub.0] + [a.sub.1][x.sub.1] +[a.sub.2][x.sub.2].... + C. When the variables are entered into thisequation, a viability score is produced that can then be compared to theaverage scores for the open and closed parish schools. The discriminantfunction analysis then calculated the probabilities of a particularquotient quotient - The number obtained by dividing one number (the "numerator") by another (the "denominator"). If both numbers are rational then the result will also be rational. being a member of either the open group or the closed group. Itassigned the parish school to either the open group or the closed group,and then checked the accuracy of its prediction based upon the knownoutcome. The analysis included 124 of the 138 cases; 14 cases were notincluded due to missing variables (7 open and 7 closed). Three variablesgenerated a 93% overall correct classification (see Tables 5 and 6), andan 87.5% correct classification of closed schools, a very importantattribute for an early warning system. The mean discriminant functionscore for open schools was .597, while the mean discriminant functionscore for the closed schools was 1.716. APPLICATIONS This study employed only readily accessible variables with theexplicit intent of creating a diagnostic tool that could be used as anearly warning system to assess school viability on an ongoing basis. Thediscriminant function analysis produced an equation (see Figure 3) thatdemonstrated a 93% overall correct classification of open and closedschools based upon three variables (see Tables 5 and 6).Figure 3. Equation produced by discriminant function analysisViability Score = .686 + 1.241(above 200) + 8.106(pchgsch1) - 17.290(tuitpctadjinc) The viability score for an individual school can be calculated byentering the appropriate values into the equation and performing thecalculation. If a school has an enrollment of above 200, a 1 is placedin the parentheses "above200," if not, a zero is placed in theparentheses. The percentage change in enrollment from the previous year(either positive or negative) is placed in the parentheses designated"pchgsch1," and the tuition as a percentage of adjusted medianhousehold income is placed in the parentheses designated"tuitpctadjinc." The tuition as a percentage of median adjusted household income isthe tuition charged for one child in the school divided by the adjustedmedian household income. This adjustment is done by multiplying themedian household income from the 2000 United States Census (1999 annual;United States Census Bureau, 2007) for a particular parish boundary by aratio of numbers obtained from the CPI-U St. Louis, a statistic statistic,n a value or number that describes a series of quantitative observations or measures; a value calculated from a sample.statistica numerical value calculated from a number of observations in order to summarize them. producedby the Bureau of Labor Statistics (see Table 7). An income of $50,000for the year 1999, adjusted forward to the spring of 2006, would be$59,898 ($50,000 * [188.8 / 157.6] = $59,898). The tuition as a function of adjusted median household income isthen calculated by dividing the tuition charged for one child in theschool ($4,275) by the adjusted median household income ($59,898). Thevalue, .07137, is then placed in the parentheses designated"tuitpctadjinc" completing the equation. AN EARLY WARNING SYSTEM FOR THE ARCHDIOCESE A frequency analysis of the viability scores for all the parishschools included in this study were run (see Figure 4). Basicdescriptive statistics descriptive statisticssee statistics. were also run on the distribution of theseviability scores. The standard deviation for open schools was found tobe approximately 1 (see Table 8). [FIGURE 4 OMITTED] The discriminant function analysis, in addition to producing aviability score, also calculated the probability of a school being ineither the open or closed category. A careful examination of the scoresthat approach equal probability provides a method for interpolating thethreshold viability score for predicting closure. This thresholdviability score for predicting closure was found to be approximately-1.0. The descriptors Red, Orange, Yellow, and Green, analogous totraffic light colors, were chosen as the descriptor (1) A word or phrase that identifies a document in an indexed information retrieval system.(2) A category name used to identify data. (operating system) descriptor of school viability.Additionally, since there will be movement from year to year based uponwhether the change in K-8 enrollment is going up or down, whethertuition increases are above or below the CPI-U growth for St. Louis, andwhether the school's enrollment lies above 200 or below, adirectional In one direction. Contrast with omnidirectional. component (+ or -) was added to the descriptor. The divisionline between Green and Yellow lies one standard deviation (1.0) belowthe mean viability score for open schools (approximately .6) which is-.4, the division line between Yellow and Orange lies at the threshold At the Threshold, whose son Lil E. Tee won the 1992 Kentucky Derby for W. Cal Partee, died March 23 of a stroke at Purdue University School of Veterinary Medicine in West Lafayette, Ind. The 21-year-old stallion stood at Wayne Houston's Stoney Creek Horse Farm near Mooreland, Ind. line for predicting closure (-1.0), and the division line between Orangeand Red lies two standard deviations below the mean viability score foropen schools (-1.4). Green represents viability scores of -.4 or higher,Yellow represents scores of -.4 to -1.0, Orange represents scores of-1.0 to -1.4, and Red is anything lower than -1.4. The change in valuefrom one year to the next provides the directional sign. A school thathas a score of .55 one year, but one of .43 the next would be"Green -." A school that has a score of -.84 one year, but hasa score of -.75 the next would be "Yellow +" (see Figure 5). [FIGURE 5 OMITTED] ST. ELSEWHERE ELEMENTARY SCHOOL A parish school named St. Elsewhere will serve as an example forthe viability score calculations. St. Elsewhere has a median householdincome of $50,000 for the year 1999. St. Elsewhere had a precipitousdecline in enrollment from 1999 to 2004, but has recently experiencedmodest growth. The tuition increased only 6% per year over the entireperiod as the parish subsidized sub��si��dize?tr.v. sub��si��dized, sub��si��diz��ing, sub��si��diz��es1. To assist or support with a subsidy.2. To secure the assistance of by granting a subsidy. the heavy losses incurred by the schoolwhen the enrollment plunged. The enrollment, the percentage change inenrollment, the tuition charged for one child, and the adjusted medianhousehold income are all shown in Table 9. Placing the appropriate values into the viability score equationproduced the following viability scores and descriptors for St.Elsewhere (1999-2007; see Table 10). St. Elsewhere's drop in enrollment of five students from1999-2000 to 2000-2001 yielded only a minus sign on an overall healthy(Green) school profile; a viability score of .74 is above thearchdiocesan average of .60 for open schools in the study. Another dropin 10 students puts the viability score below the archdiocesan averageand merits another "Green -" rating. A loss of 15 students anda drop in enrollment below the 200 threshold merits a "Yellow-" rating. A loss of 20 more students the following year puts St.Elsewhere into the "Orange -" category. At this point, themodel indicates that St. Elsewhere's profile is more like theschools that eventually closed than those that remained open. However,the following year, the enrollment decline reverses itself with a modestincrease of 5 students, enough to merit a "Green +" rating.Another increase in students generates a slight decline in the viabilityscore generates a "Green -" A very slight decline inenrollment the following year puts St. Elsewhere back into the"Yellow -" zone. An explanation and interpretation of thesechanges follows below. The example provides some insight into the mechanics of theviability score. As long as tuition rises above the rate of the CPI-Uincreases, the "tuitpctadjinc" term of the equation willprovide a negative drag on Verb 1. drag on - last unnecessarily longdrag outlast, endure - persist for a specified period of time; "The bad weather lasted for three days"2. the viability score. If enrollment dropsbelow 200, a tipping point has been passed, and tuition as a function ofadjusted median household income and changes in enrollment (eitherpositive or negative) become very significant in the viability score;this explains why an increase of five students in one year and a loss oftwo in another can launch St. Elsewhere from "Orange -" to"Green +" and from "Green -" back to "Yellow-" respectively. How did the addition of five students in two successive years takeSt. Elsewhere from "Orange-" to "Green +" in oneyear and from "Green +" to "Green-" the next? Theanswer lies in understanding the viability score as both an assessmentof general health ("above200" and "tuitpctadjinc")and an indicator of the most recent trend ("pctchgsch1"). WhenSt. Elsewhere reversed the enrollment decline, the"pctchgsch1" term of the equation went from a negative ofsubstantial magnitude to a positive. This greatly improved the viabilityscore. Should the addition of 5 students warrant such an extremereversal from "Orange -" to "Green +" in one year?It could be argued that taking a school out of what appeared to be theprototypical "death spiral Death SpiralA type of loan investors lend to a company in exchange for convertible debt, which, like a convertible bond, typically has provisions that allow the investors to convert the bonds into stock at below-market prices. " demonstrated graphically in Table5 by reversing rapid enrollment declines and holding the line on tuitionis also very extreme and significant. So why does the school go to"Green -" the following year by adding the exact same numberof students? The answer lies in both the "pctchgsch1" term andthe "tuitpctadjinc" term of the equation. The former, whilepositive, was a slightly smaller percentage increase over the last year,and the latter, as stated earlier, is a negative drag on the viabilityscore whenever the tuition rises above the increase in the CPI-U St.Louis. A complicating com��pli��cate?tr. & intr.v. com��pli��cat��ed, com��pli��cat��ing, com��pli��cates1. To make or become complex or perplexing.2. To twist or become twisted together.adj.1. issue not addressed in the St. Elsewhere example isthe calculation of the viability scores when less than 50% of thestudents' parents reside within the parish boundaries ormulti-parish grade schools. A logical fix would be a weighted adjustedincome based upon the zip codes of the students actually attending theschool. These and other issues will be monitored over time as the modelis tested against reality. CONCLUSIONS A comparison of means between open and closed schools revealed manystatistically significant and substantively meaningful differences.Subsequent discriminant function analyses identified three variablesthat were utilized to correctly classify 93% of parish schools as eitheropen or closed. The model achieved a correct classification of closedschools of 87.5%, a very important attribute for an early warningsystem, and is significantly higher than the 70% cited by Lundy (1999).It also achieved the same overall predictive value pre��dic��tive valuen.The likelihood that a positive test result indicates disease or that a negative test result excludes disease.predictive valuea measure used by clinicians to interpret diagnostic test results. of Lundy's modelusing far fewer variables (3 variables instead of 14). Therefore, theviability score provides a framework and useful guide for understandingthe variables that affect parish school viability This study has produced an equation that can generate a credibleviability score for parish grade schools within the Archdiocese of St.Louis. The model was also quickly validated in reality. A parish schoolthat was open during the period of this study, but had the lowestviability score in the Archdiocese of St. Louis, closed soon after thecompletion of this study. The viability score captures the key driversthat have led to the closure of schools and therefore holds greatpromise as a component of an early warning system for parish schools.The viability score and the direction of its change has been integratedinto an early warning system for the Archdiocese of St. Louis. The obvious strengths of such a viability score also reveal itsweaknesses. While the utility of a viability score has obvious appeal,parish school viability decisions should not be dependent upon thecalculation of a quotient. Catholic schools are indispensable to theevangelizing mission of the Church. The contribution of Catholic schoolsto the educational mission of the Church cannot be reduced to a singlenumber. The viability score does not capture important elements such asmission, impact of the school on its geographic community, stakeholder stakeholdern. a person having in his/her possession (holding) money or property in which he/she has no interest, right or title, awaiting the outcome of a dispute between two or more claimants to the money or property. satisfaction levels, quality of curriculum and instruction, Catholicidentity, and quality of religious education and formation that arecritically important in crafting a comprehensive early warning system.However, coupled with best practice program evaluation Program evaluation is a formalized approach to studying and assessing projects, policies and program and determining if they 'work'. Program evaluation is used in government and the private sector and it's taught in numerous universities. systems such asthe Archdiocese of St. Louis' Instructional Program Review andAdvancEd's Quality Assurance Review, a comprehensive quantitativeand qualitative instructional assessment of quality and viability can beperformed. In addition, as noted above, the model captures a significantproportion of the variance between schools that closed and schools thatremain open. However, just as persons who score well on health riskassessments are not immune from health problems, and just as persons whoscore poorly may beat the odds, it is essential that the viability scorenot be treated as a self-fulfilling prophecy self-fulfilling prophecy,a concept developed by Robert K. Merton to explain how a belief or expectation, whether correct or not, affects the outcome of a situation or the way a person (or group) will behave. nor as a cause forcomplacency com��pla��cen��cy?n.1. A feeling of contentment or self-satisfaction, especially when coupled with an unawareness of danger, trouble, or controversy.2. An instance of contented self-satisfaction. . The viability score was developed from data in the Archdiocese ofSt. Louis, and while this research has demonstrated that the key termsof the viability quotient capture key drivers of school closure, thesemay not have the same magnitude of importance in other dioceses.Lundy's research (1999) regarding closures in Chicago in the early1990s found the keyratio, a measure of the aggregate parish-parishschool financial picture, as the single strongest key driver, whereasthe present research found enrollment, enrollment changes, and familycosts as the key drivers of school closure. Some of these variables,which attempt to capture universal drivers, might have greater or lesserimpact in other dioceses. The viability score might serve as one of several variables withina comprehensive early warning system that takes a balanced scorecard Balanced ScorecardA performance metric used in strategic management to identify and improve various internal functions and their resulting external outcomes. The balanced scorecard attempts to measure and provide feedback to organizations in order to assist in implementing approach (Kaplan & Norton, 2007). A balanced scorecard approach,taking first and foremost into account the Church's mission in aparticular geographic area, that includes quantitative data such as theviability score, might provide the impetus for regional strategicplanning Strategic planning is an organization's process of defining its strategy, or direction, and making decisions on allocating its resources to pursue this strategy, including its capital and people. . Such an approach by a diocese might actually require regionalstrategic planning and systems-thinking based upon the mission of theChurch in a particular region when a parish or collection of parishesreaches critical thresholds. This approach prevents a parish-by-parishDarwinian survival of the fittest that has heretofore allowed forwholesale loss of schools in large swaths of a particular region withoutregard to overall mission. Given the financial costs of a school, thedemographics required to supply the school with students, and theability of Catholic schools to attract parishioners into a parish,changes in a parish school viability score might well serve as a keyleading indicator Leading IndicatorA measurable economic factor that changes before the economy starts to follow a particular pattern or trend. Leading indicators are used to predict changes in the economy, but are not always accurate. of not only an individual parish school'sviability but that of the parish and other parishes in the Deanery; asgo the parish schools, so go the parishes. RECOMMENDATIONS In light of what has been stated earlier, large archdioceses oughtto seriously consider a similar study, based upon their own historicaldata, to determine the drivers and magnitude of such drivers for schoolclosures. Smaller dioceses without sufficient data to warrant anindependent study, might consider the use of multiple measures ofviability (e.g., the Lundy score as well as the viability score derivedfrom the present research). As was stated earlier, these viabilityscores ought not be the determinant determinant,a polynomial expression that is inherent in the entries of a square matrix. The size n of the square matrix, as determined from the number of entries in any row or column, is called the order of the determinant. in school closures. Rather, thesescores become individual measures of a comprehensive balanced scorecardapproach that gives due consideration to questions of mission, regionalimpact, program quality, and systemic approaches to mission. Within the Archdiocese of St. Louis, additional research needed tofurther refine the model includes: * monitoring the predictive effectiveness of the model over time * examining potential differences in the model's effectivenessfor various types of schools (single parish, multiparish) and forschools in various locations (urban, suburban, rural) * determining further ways to address the median household incomefactor in the cases where more than half of the school families do notlive within parish boundaries * repeating the study when 2010 Census data become available. The balanced scorecard approach lends itself to systems-thinking(Senge, 1990) regarding the importance and impact of the various Churchstructures like parishes and schools, and presupposes a thoughtfuldeliberation deliberationn. the act of considering, discussing, and, hopefully, reaching a conclusion, such as a jury's discussions, voting and decision-making. DELIBERATION, contracts, crimes. regarding mission. The questions of mission and how theChurch ought to utilize its resources at any given point in time areperennial perennial,any plant that under natural conditions lives for several to many growing seasons, as contrasted to an annual or a biennial. Botanically, the term perennial questions that must be revisited in light of changingcircumstance. These are signs of a healthy, thoughtful, and evangelizingChurch. Such approaches may serve as the first steps in the development ofan information architecture for the diocese that helps inform keydecisions regarding mission. Information architecture, as the nameimplies, is real-time information, data, and data analysis that are madeavailable in a timely and useful manner to all those engaged in themission of the Church. Tools such as geographic information systems(GIS) that link any conceivable con��ceive?v. con��ceived, con��ceiv��ing, con��ceivesv.tr.1. To become pregnant with (offspring).2. diocesan di��oc��e��san?adj.Of or relating to a diocese.n.The bishop of a diocese.diocesanAdjectiveof or relating to a dioceseNoun 1. database to geographicallocation (parish, deanery, city, diocese, etc.) and statistical packagesfor the analysis of data (SPSS) that were utilized in this research andhave historically been the province of city planners and academiciansrespectively, ought to be routinely employed by dioceses to makedata-driven decisions in the service of the mission of the Church. Suchtools will be the catalyst for "action research" by individualdioceses and may provide avenues for further research. An example of just such an avenue of inquiry comes from aperipheral finding not explored in this article. There are indicationsfrom the data gathered that parishes with schools have a greater abilityto attract families into the parish (James, 2008). These families arepresumably pre��sum��a��ble?adj.That can be presumed or taken for granted; reasonable as a supposition: presumable causes of the disaster. younger families with children. This research coupled withthat of Zech (2000) regarding the differential giving of Catholic schoolparents, may indeed reconfirm re��con��firm?tr.v. re��con��firmed, re��con��firm��ing, re��con��firmsTo confirm again, especially to establish or support more firmly: reconfirmed the reservations. the original thesis of Greeley (1977) thatCatholic schools are not only an effective mechanism in the evangelicalmission of the Church, but actually are profit-making enterprises overthe lifetime of the students. REFERENCES Bimonte, R. R. (2004). Balance sheet for Catholic elementaryschools: 2003 income and expenses. Washington, DC: National CatholicEducational Association. Bredeweg, F. (1980). Catholic elementary schools and theirfinances, 1980. Washington, DC: National Catholic EducationalAssociation. Bureau of Labor Statistics. (2007). Consumer price index-All urbanconsumers, St. Louis, MO. Retrieved March 12, 2007, fromhttp://data.bls.gov Center for Applied Research in the Apostolate. (2006). Primarytrends, challenges, and outlook: A special report on U.S. Catholicelementary schools, 2000-2005. Washington, DC: Author. Cohen cohenor kohen(Hebrew: “priest”) Jewish priest descended from Zadok (a descendant of Aaron), priest at the First Temple of Jerusalem. The biblical priesthood was hereditary and male. , J. (1988). Statistical power analysis for the behavioralsciences behavioral sciences,n.pl those sciences devoted to the study of human and animal behavior. (2nd ed.). Hillsdale, NJ: Earlbaum. Convey, J. (1992). Catholic schools make a difference: Twenty-fiveyears of research. Washington, DC: National Catholic EducationalAssociation. Greeley, A. (1959). The Church and the suburbs. New York New York, state, United StatesNew York,Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of : Sheed& Ward. Greeley, A. (1977). A preliminary investigation: The profitabilityof Catholic schools. Momentum, 8(4), 43-49. Harris, J. C. (1996). The cost of Catholic parishes and schools.Kansas City Kansas City,two adjacent cities of the same name, one (1990 pop. 149,767), seat of Wyandotte co., NE Kansas (inc. 1859), the other (1990 pop. 435,146), Clay, Jackson, and Platte counties, NW Mo. (inc. 1850). , MO: Sheed & Ward. Harris, J. C. (2000). The funding dilemma facing Catholicelementary and secondary schools. In J. Youniss & J. J. Convey(Eds.), Catholic schools at the crossroads: Survival and transformation(pp. 55-71). New York: Teachers College Press. James, J. T. (2008). The impact of Catholic schools on parishmembership. Unpublished manuscript. Kaplan, R. S., & Norton D. P. (2007). Using the balancedscorecard as a strategic management system. Harvard Business Review Harvard Business Review is a general management magazine published since 1922 by Harvard Business School Publishing, owned by the Harvard Business School. A monthly research-based magazine written for business practitioners, it claims a high ranking business readership and ,85(7/8), 150-161. Lundy, G. (1999). School-parish financial linkages and theviability of urban Catholic elementary schools. Journal of Research onChristian Education, 8, 85-106. McDonald, D. (2005). United States Catholic elementary andsecondary schools 2004-2005: The annual statistical report on schools,enrollment, and staffing. Washington, DC: National Catholic EducationalAssociation. McGreevy, J. T. (1996). Parish boundaries: The Catholic encounterwith race in the twentieth century urban north. Chicago: University ofChicago Press The University of Chicago Press is the largest university press in the United States. It is operated by the University of Chicago and publishes a wide variety of academic titles, including The Chicago Manual of Style, dozens of academic journals, including . McLellan, J. A. (2000). Rise, fall, and reasons why: U.S. Catholicelementary education elementary educationor primary educationTraditionally, the first stage of formal education, beginning at age 5–7 and ending at age 11–13. , 19401995. In J. Youniss & J. J. Convey (Eds.),Catholic schools at the crossroads: Survival and transformation (pp.17-32). New York: Teachers College Press. Senge, P. M. (1990). The fifth discipline: The art and practice ofthe learning organization. New York: Doubleday. United States Catholic Conference. (1976). Where are the 6.6million? A statistical survey of Catholic elementary and secondaryformal religious education 1965-1974. Washington, DC: Author. United States Census Bureau. (2007). United States census 2000.Retrieved March 12, 2007, fromhttp://www.census.gov/main/www/cen2000.html Zech, C. E. (2000). Why Catholics don't give, and what can bedone about it. Huntington, IN: Our Sunday Visitor. JOHN T. JAMES Saint Louis University Saint Louis University,mainly at St. Louis, Mo.; Jesuit; coeducational; opened 1818 as an academy, became a college 1820, chartered as a university 1832. Parks College (est. 1927 as Parks College of Aeronautical Technology) in Cahokia, Ill. KAREN L. TICHY Archdiocese of St. Louis ALAN COLLINS JOHN SCHWOB Archdiocesan Board of Catholic Education, Archdiocese of St. Louis John T. James is Assistant Professor in the Department ofEducational Leadership and Higher Education and Director of the CatholicLeadership Program at Saint Louis University. Karen L. Tichy isAssociate Superintendent for Instruction for the Archdiocese of St.Louis. Alan Collins and John Schwob are on the Archdiocesan Board ofCatholic Education for the Archdiocese of St. Louis. Correspondenceconcerning this article should be sent to Dr. John T. James, Saint James, Saint(St. James the Greater)James, Saint,d. c.A.D. 43, in the Bible, one of the Twelve Apostles, called St. James the Greater. He was the son of Zebedee and the brother of St. John; these brothers were the Boanerges, or Sons of Thunder. St. LouisUniversity, Department of Educational Leadership and Higher Education,Suite 113, McGannon Hall, 3750 Lindell Blvd., St. Louis, MO 63108.Table 1Catholic Elementary School Enrollment (in Millions) and Number ofSchools 1920-2005Year Enrollment Schools1920 1.796 6,5511930 2.223 7,9231940 2.035 7,9441950 2.561 8,5891960 4.373 10,5011970 3.359 9,3661980 2.293 8,1001990 1.983 7,3952000 2.013 6,9232005 1.780 6,574Note. Source: McDonald (2005).Table 2Number of Religious in Catholic Schools and their Percentages inSchools (1920-2005)Year Number Percentage1920 45,563 92.01930 65,601 90.41940 73,960 91.21950 84,925 90.11960 112,029 73.81970 80,615 48.41980 42,732 29.01990 20,020 14.62000 11,011 7.02005 7,990 5.0Note. Source: McDonald (2005).Table 3Variables Used in the AnalysisParish Demographicsnhshlds The number of households within the parish in 2004.npar The number of parishioners in the parish in 2004.totbapt The total number of baptisms in the parish performed in the 3 years prior to the comparison year.School Demographicsnsch-1 The number of students in the parish school 1 year prior to the CY.nsch-2 The number of students in the parish school 2 years prior to the CY.nsch-3 The number of students in the parish school 3 years prior to the CY.nsch-5 The number of students in the parish school 5 years prior to the CY.pchgsch1 The percent change in enrollment for the parish school between 2 years prior to the CY and 1 year prior to the CY.pchgsch3 The percent change in enrollment for the parish school between 3 years prior to the CY and 1 year prior to the CY.pchgsch5 The percent change in enrollment for the parish school between 5 years prior to the CY and 1 year prior to the CY.Parish Financesptotinc04 The parish total income for FY 2004.givpctinc The giving per household in the parish as a percentage of median household income defined as total parish income divided by the number of households in the parish, then divided by the median household income for the parish.hseinc00 The median household income for the parish derived from the 2000 United States census data.adjhseinc The median household income within the parish boundaries adjusted to the CY using the CPI-U (St. Louis).School Financestuition The tuition charged for one child in the school for the CY.schttlinc The parish school's total income.schttlexp The parish school's total operating expense.schtuitinc The parish school's total income from tuition.perpupilcost The total operating expenses divided by the enrollment for one year prior to the CY.Parish and School Financeskeyratio The sum of two ratios: the ratio of school salary and benefit expenses to school income from tuition plus the ratio of the parish's total income minus the parish's subsidy given to the school to the parish's total income.Parent Financestuitpctadjinc The tuition charged for one child in the school for the comparison year divided by the median household income adjusted to the comparison year using the CPI-U (St. Louis).Table 4A Comparison of Means of Open and Closed Catholic Grade SchoolsVariable n M SD SE p dpchgsch-1 open 92 -.0002 .08674 .00904 .000 1.61 closed 32 -.1526 .10173 .01798pchgsch-3 open 92 -.024 .1472 .0153 .000 1.60 closed 37 -.244 .1273 .0209pchgsch-5 open 92 -.020 .2643 .0276 .000 1.36 closed 37 -.306 .1350 .0222ptotinc04 open 99 722028 485984 48843 .000 1.19 closed 33 295602 141189 24578adjhseinc open 98 56825 17928 1811 .000 1.18 closed 39 40082 8915 1428nsch-1 open 97 284.4 176.7 17.95 .000 1.15 closed 39 134.4 53.3 8.54hseinc00 open 98 51647 16294 1646 .000 1.12 closed 39 37300 8026 1285npar open 92 3802 2771 288.9 .000 1.05 closed 37 1665 799 131.5totbapt open 92 160.1 131.9 13.75 .000 1.03 closed 37 60.1 37.2 6.12schttlexp open 98 998032 496030 50107 .000 0.98 closed 32 617634 233230 41230schttlinc open 94 998244 495913 51150 .000 0.96 closed 31 625145 233437 41926nhshlds open 92 1301 $853.6 88.99 .000 0.96 closed 37 688 $300.3 49.37tuitpctadjinc open 97 .0499 .02408 .00244 .000 0.95 closed 31 .0705 .01921 .00345nsch-2 open 99 287.4 176.4 17.73 .000 0.93 closed 34 163.8 64.3 11.03perpupilcost open 88 3794 905 96.49 .000 0.88 closed 29 4714 1172 217.6nsch-3 open 97 292.5 177.1 17.98 .000 0.84 closed 39 178.7 73.3 11.74schtuitinc open 94 563751 378715 39061 .000 0.84 closed 31 320527 157209 28236nsch-5 open 97 292.8 173.0 17.57 .000 0.74 closed 39 194.4 74.7 11.96givpctinc open 92 .01219 .004981 5.19 E-4 .471 -- closed 31 .01294 .004888 8.78 E-4tuition open 99 2591 1000 100.5 .210 -- closed 34 2740 359 61.6keyratio open 93 2.4487 1.96998 .20428 .390 -- closed 25 2.1605 1.24126 .24825Table 5Canonical Discriminant Function Unstandardized Coefficients Function 1above200 1.241pchgsch1 8.106tuitpctadjinc -17.290(Constant) .686Table 6Discriminant Function Analysis Classification Results Predicted group Status membership Total Open ClosedOriginal Count Open 87 5.00 92 Closed 4 28 32 Percentage Open 94.6 5.4 100.0 Closed 12.5 87.5 100.0Cross-validated (a) Count Open 87 5 92 Closed 4 28 32 Percentage Open 94.6 5.4 100.0 Closed 12.5 87.5 100.0(a) Cross validation is done only for those cases in the analysis. Incross validation, each case is classified by the functions derivedfrom all cases other than that case.(b) 92.7% of original grouped cases correctly classified.(c) 92.7% of cross-validated grouped cases correctly classified.Table 7Consumer Price Index--All Urban Consumers--All Items--St. Louis,MissouriYear Annual First half Second half1999 157.6 156.4 158.82000 163.1 162.1 164.02001 167.3 167.5 167.12002 169.1 167.8 170.32003 173.4 172.3 174.52004 180.3 179.1 181.62005 186.2 185.0 187.42006 189.5 188.8 190.3Note. Source: Bureau of Labor Statistics (2007).Table 8Descriptive Statistics on Viability Scores for Parish SchoolsIncluded in the StudyStatus N M SD SEOpen 92 .5968 1.030 .1074Closed 32 -1.716 .9067 .1603Table 9Data for St. Elsewhere Parish Elementary School Enrollment Change Tuition AMHI Tuition/AMHI1999-2000 225 (1)2000-2001 220 (1) -.02222 $3,000 $51,428 .058332001-2002 210 (1) -.04546 $3,180 $53,141 .059842002-2003 195 (0) -.07143 $3,375 $53,236 .063402003-2004 175 (0) -.1026 $3,575 $54,664 .065402004-2005 180 (0) .02857 $3,790 $56,821 .066702005-2006 185 (0) .02778 $4.025 $58,693 .068582006-2007 183 (0) -.01081 $4.275 $59,899 .07137Table 10Enrollment, Viability Score and Descriptor for St. Elsewhere ParishSchool 1999-2007 Enrollment Score Descriptor2000-2001 220 .74 Green -2001-2002 210 .52 Green -2002-2003 195 -.99 Yellow -2003-2004 175 -1.28 Orange -2004-2005 180 -.24 Green +2005-2006 185 -.27 Green -2006-2007 183 -.64 Yellow -