Exploring Demographic and Selected State Policy Correlates of State Level Educational Attainment                      and ...
1. IntroductionAs a nation, we are fascinated by state-by-state comparisons on almost any topic. Ineducation, increasingly...
1. How much variation by state is there in state high school and postsecondary       completion rate indicators; and NAEP ...
2. Procedure, Data and MethodsWe address the questions posed above by a series of descriptive graphing and building explor...
Table 1-a. State aggregated demographic variables included in various models                                              ...
Table 1-b. Selected State education policy or practice variables included in various models                               ...
3. Descriptive Graphing Information on State Variation on the       Outcomes of InterestIn this section, we present descri...
Nevada. One can see that the range of difference between states for the BA or higher hasappears to have grown over the per...
Figure 3.       Percent of population 25 years of age and older who have a BA degree:                Decennial Census Data...
Figure 4.       Percent of total population 25 and older with high school diploma or                equivalent by state: 1...
Figure 5.       Percent of total population 25 and older with BA degree or higher by                state: 1940-2000   45 ...
Figure 6, also using the decennial-census-data, plots by state the gap between the percentof white and black persons 25 ye...
Figure 7.       Plot of gap between percent of white and black population over 25 with                a BA or higher by st...
Figure 8.       Plot of gap between percent of white and black population over 25 with                high school diploma ...
Figure 9.                     Public high school cohort survival rate by state: 2004       N ew J ers ey                  ...
Figure 10. Public School Cohort Survival Rate by State 1990-2004  100    90    80    70    60    50    40    30    20    1...
3.1.2 Postsecondary Pipeline/Completion IndicatorFigure 11 graphs state differences in the postsecondary pipeline/completi...
3.2 Selected Achievement Outcome VariablesFigures 12a to 15 present statistics on the achievement variables included in th...
SOURCE: U.S. Department of Education, National Center for Education Statistics, National Assessmentof Educational Progress...
Figure 13. NAEP average 8th grade math score by state: 1990, 2000, 2005  300  290  280  270  260  250  240    1988        ...
3.2.2 Rate per 1000 High School Graduates who Score above 1200 on SAT or 26 on ACTThe other achievement outcome indicator ...
Figure 15. Rate per 1000 high school graduates who scored 1200 or above on           combined SAT or 26 or above on ACT: 1...
model groups “race” and “ethnicity/immigration” each contribute 7 percent and the“parent employment” variable adds another...
Figure 16. Distribution of variance among “groups” in the demographic model           examining state variation in public ...
Figure 17. Difference between actual and predicted (residuals) high school cohort           survival rate (CSR) from model...
4.1.2 Adding Selected State Policy and Education Related Statistics to the          Demographic Model of High School Cohor...
Table 2.      Summary of forward selection regression model using grouped option              explaining variation in stat...
Figure 18. Distribution of variance among “groups” in the model examining state           variation in public school high ...
4.2       A Postsecondary Pipeline/Completion Indicator—(Rate of Graduation from          High School, Entering Postsecond...
NOTE: This statistic is calculated based on CCD enrollment figures for 9th graders, estimating the numberwho graduate from...
Figure 19. Distribution of variance among “groups” in the demographic model           examining state variation in postsec...
Figure 20. Difference between actual and predicted (residuals) for postsecondary           pipeline/completion indicator f...
4.2.2 Adding Selected State Policy and Education Related Statistics to the         Demographic Model of the Postsecondary ...
Figure 21. Distribution of variance among “groups” in model examining state           variation in postsecondary pipeline/...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicator...
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Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicators aera2007 cahalan

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AERA paper presenting results of analysis of state differences in high school graduation, college going, NAEP scores, ACT-SAT scores. Found state parent education levels most related to differences by state

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Exploring demographic and selected state policy correlates of state level educational attainment and achievement indicators aera2007 cahalan

  1. 1. Exploring Demographic and Selected State Policy Correlates of State Level Educational Attainment and Achievement Indicators Paper prepared for: The American Educational Research Association (AERA) Annual Meeting Prepared by: Margaret Cahalan Jim Maxwell Draft April 10, 2007 Chicago, Ill.Note: All tabulations and views reported in this paper are the responsibility of the authors and donot reflect any review, authorization, or clearance by the Department of Education.
  2. 2. 1. IntroductionAs a nation, we are fascinated by state-by-state comparisons on almost any topic. Ineducation, increasingly, researchers and policy makers are preparing indicators, often withrankings and scores assigned to the states. ED Week’s Quality Counts (QC), grades statesand is dedicated to tracking “state efforts to creating a seamless education system fromearly childhood through the world of work,” and the National Center for HigherEducation Managers Systems (NCHEMS) provides policy makers with a “State ReportCard” system to help managers make decisions. Similarly, major government surveys andassessment tools are increasingly designed to provide state- by-state estimates.In recent years, education policy reform discussion has moved from an emphasis onunderstanding the importance of student background characteristics in explainingdifferences in outcomes to a focus on the importance of state, district, school and teachercontrolled factors. This has shifted some of the focus from attainment to achievementtest scores, and from compensatory programs to state, district, school, and teacheraccountability. This has been accompanied by an increased emphasis on identifyingpractices and policies that all other things being equal, are more effective than others inproviding effective schooling. At the state level, the “state standards movement” andreform has resulted in state level efforts to promote higher achievement through suchthings as increased core curricular requirements, exit exams, higher compulsory schoolattendance age, school size reform, requiring teachers to have a major in field taught,increased technology use, advanced and honors diplomas, and content standards.In this paper we explore relationship of state aggregated student and family relatedbackground characteristics, and selected state policy variation to aggregated measures ofboth student attainment and achievement outcome indicators. We first explore the basicquestion of how much of the measured differences in educational outcomes between thestates can be attributed to demographic differences in the composition of the populationsof the states. Second, taking these compositional differences into account, we explore theextent to which differences in selected state policies are statistically related to differences inobserved outcomes aggregated at the state level. To do this we use aggregated state leveldata from the Census Bureau merged with Department of Education data from theCommon Core of Data (CCD), Integrated Postsecondary Data Systems (IPEDS), and theNational Assessment of Education Progress (NAEP), and various other sources toincrease our understanding of what these state-by-state comparisons represent. Inaddition we provide some state level descriptive historical data on some of the majoroutcomes of interest.1.1 Research QuestionsSpecifically we address the following questions. 2
  3. 3. 1. How much variation by state is there in state high school and postsecondary completion rate indicators; and NAEP and SAT/ACT achievement indicators? 2. How much of the variation is associated with variation in state population demographics? What demographic variables are most related to the outcomes of interest? 3. Are there states that have higher or lower than expected outcomes based on demographics? 4. How much of the variation is related to differences in selected state policies?To a limited extent, we also descriptively address trends over time and the extent of thegap between race and ethnic minority statistics with regard to high school andpostsecondary completion.Figure 1 summarizes the state level statistics examined descriptively and in the regressionmodels. We discuss these measures in more detail as we proceed and appendices provideadditional information on the distribution by state for several of these variables.Figure 1. Summary of demographic, selected state policy/education statistics, and student outcomes variables included in models State Demographics Education levels, Income/poverty, Employment, Race, Ethnicity/immigration, Mobility, Population Selected State Policy/Ed System Statistics Exit exams, Compulsory school age, Course requirements, Technology score, School size, Teacher salary, Advanced diploma, Algebra 8th grade Student Outcomes Attainment Public school high school cohort survival rate Postsecondary entrance and completion indicator Achievement 8th grade NAEP math score Number per 1000 high school graduates scoring 1200 or 26 an above on SAT/ACT 1. 2 Paper StructureThe remainder of this paper proceeds as follows: 2) Procedure data and methods; 3) Descriptivedata on model outcome variables with some historical perspective 4) Regression models results forattainment 5) Regression models results for achievement 6) Conclusion/discussion. 3
  4. 4. 2. Procedure, Data and MethodsWe address the questions posed above by a series of descriptive graphing and building exploratoryregression models. Our first step was to build a state database that consists of state demographicvariables, state education policy variables, and state outcome variables. The primary data sourcefor most of the data is the Census Bureau (Decennial Census, American Community Survey andCurrent Population Reports on Educational Attainment) and the US Department of Education((Common Core of Data (CCD), and the Integrated Postsecondary Education Data System(IPEDS)). In addition, data on college entrance scores comes from the College Board andthe ACT. Many of the derived variables/indicators used were directly taken from compilations ofstate aggregated data published by the Council of Chief States School Officers (CCSSO) StateIndicator Reports, ED-Week Quality Counts, and NCHEMS web based Information Center. All of thedata used in this paper are aggregated at the State level. Graphs typically include the 50 US statesand the District of Columbia; however DC was removed from regressions due to its uniquedemographics. Using these data sources, we first built a database containing about 300 state levelvariables. From this database we selected the variables included in Table 1(a-c) to include in ourmodel building. These are organized conceptually into three groupings (state demographics,selected state policy and education system statistics, and state level outcomes on attainment andachievement). Our focus is on educational measures most applicable to thesecondary/postsecondary level.In the next section, we present descriptive information by state on the outcome variables as a wayof observing the range of differences among the states. We also include some historicalinformation on the outcomes of interest in the form of graphing historical trends by state. Wethen proceed to look at the relationships among the variables and present results of regressionmodels and examination of the expected vs. the actual rates based on state demographics. Finallywe look at the extent to which the introduction of selected state policy variables changes theamount of variation explained controlling for the demographic differences. To assist in theexploratory analysis, we used the SAS proc regression grouped option, which allows for selectedvariables to enter into the model together in logical groupings. We used a grouped Forwardselection option, which starts with no variables in the model and adds variable groups one by onethat maximize the fit of the model. We use selection criteria of .15 for entrance into the model.Predicted and residual values from the estimated regression equation were also tabulated.Observing partial regression results, we also observe the percent of the variation attributed to eachof the groups in the model. In forming the groups, exploratory factor analysis of the variables wasperformed and correlations between the independent variables were observed. These identifiedfactors contributed to decisions about the groupings used in the models. 4
  5. 5. Table 1-a. State aggregated demographic variables included in various models StandardName Label Source Mean DeviationIncome/povertypu18po99 Percent under 18 in poverty Census 15.8 4.7mefain05 Median family income 2005 Census 55834.0 8727.8Employment Census Percent of children in families in which one parentparempl is working full time for year Census 71.3 4.2Education Percent of children in families in which one parent has 2 or 4 yearonparpst postsecondary degree Census 43.9 7.1 Percent of population age 25-and older who have highalhsd20 school diploma or credential Census 82.0 4.4Race/ethnicity Percent Black in populationpblk05 2005 Census 10.4 9.7Ethnicity/Immigration Percent Hispanic inphispa05 population 2005 Census 9.0 9.5 Percent foreign born inpforbo04 2004 Census 7.9 6.0 Percent parents who areparengsk native English speakers Census 90.1 7.8Populationrepo02 Resident population 2002 Census 5756.0 6386.8 Population density perposqm05 square mile Census 189.3 257.7Mobility Percent of population that lived in another state onemobil05 year earlier Census 3.1 1.1Source: US Census Bureau, Decennial Census and American Community Survey.<http://www.census.gov/popest/states/asrh/SC-EST2005-04.html 5
  6. 6. Table 1-b. Selected State education policy or practice variables included in various models Standard Name Content Source Mean DeviationHSEXIT2 Had exit exam by 2004 CCSSO 0.4 0.5 National EducationComsch05 Compulsory school age Association 16.9 .9 QC state indicatorsTecindx5 technology score ED-Week 76.6 6.6 Ratio of teacher salary to perNtesal capita income NCES/Census 1.5 0.1 Average school size forAsssr03 regular secondary schools NCES 772.9 310.8 Number math coursesMcourreq required for graduation CCSSO 2.8 0.7 Major in field required forMajsteac teachers ED-Week QC 80.9 .40Table 1-c. State outcome variables explored Standard Name Content Source Mean Deviation Public 9th grade school CCD/NCEHMS webPCSR04 cohort survival rate site/Mortenson 71.7 9.15 Percent 9th grade graduating high school, entering postsecondary and obtaining program completion in 150 CCD/IPEDS/ACTPG9DCG04 percent of time NCES/NCHEMS/Mortenson 18.3 14.97 Average 8th gradeAvmatsc5 math score NCES/NAEP 278 7.14 Number per 1000 with SAT above 1200 orHISCRT04 ACT above 26 ACT/SAT 173 36.1 Gap between black and non-hispanic white high school completion CensusSource: NCHEMS Higher Information Center http://higheredinfo.org/ and Tom Mortenson—Postsecondary Education Opportunity http://www.postsecondary.org; SAT. The College Board. "2001 SATV+M Score Bands Report," unpublished data; ACT. "Number of 2001 High School Graduates with ACTComposite Scores of 26 or Higher," unpublished analysis, Iowa City, Iowa 6
  7. 7. 3. Descriptive Graphing Information on State Variation on the Outcomes of InterestIn this section, we present descriptive state data on the outcome variables included in themodels. Appendix A contains additional graphs of some of demographic and state policy/system variables also included in the model. By way of introduction, we also include somehistorical data on decennial census data by state on high school and college educationalattainment.3.1 Education Attainment StatisticsThe publication of reports such as One-Third of A Nation (Barton 2005) and Losing OurFuture (Orfield et al. 2004), reflect the refocusing of attention on high school completionrates as a national problem. Trend lines and yearly rates differ depending on whatmeasure of dropping out one chooses. As illustrated in appendix A table 1, recentestimates nationwide of public school high school completion rates range from 68-70percent (and around 50 percent for underrepresented minorities) based on ratios ofentering public school cohort size to diplomas awarded four years later --- to 86 percent asreported by 18-24 year olds in the Current Population Survey and including public andprivate school students, alternative completions, and out of grade completions. 3.1.1 Decennial Census Data on Attainment 1940-2000Figure 2 gives decennial census data on the percent of the total US population 25 years ofage and older that have a high school diploma or equivalent from 1940 to 2000 byrace/ethnicity; and figure 3 gives similar information for those who have a BA degree.These data document the dramatic increase in the percent of the population with highschool diploma or equivalent, and especially among blacks, narrowing the black-white gap,over the last 60 years. The figures also document the slowing of gains in the last decade.Gains for a BA have also occurred over the period with a slowing of rate of increase inrecent years (figure 3).Figures 4 and 5 plot this same information by state (without state labels) for high school orhigher and BA or higher, respectively. In 1940 the high school completion distributionranged from 15 percent in Arkansas to 41 percent in the District of Columbia and 37percent in California. By 2000, the high school distribution ranged from 73 percent inMississippi to 88 percent in 4 states---Utah, Wyoming, Minnesota, and Alaska.1 Figure 4,shows that the variation among states in rates of high school credential attainment hasnarrowed over the period since 1940.In 1940 the distribution for BA or higher ranged from 2 percent in Arkansas and 3 percentin Alabama to 11 percent in the District of Columbia and 7 percent in California and1 This decennial census figure of 88 percent for Alaska is surprising given the relatively lower figure on thecohort survival rate. 7
  8. 8. Nevada. One can see that the range of difference between states for the BA or higher hasappears to have grown over the period since 1940.Figure 2. Percent of population 25 years of age and older who have a high school diploma or equivalent by race/ethnicity: Decennial Census Data 1940-2000 100 90 85 79 84 80 80 78 70 75 72 70 69 67 63 60 55 52 50 51 50 52 43 44 40 41 36 34 31 30 26 24 22 20 14 10 8 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 Black Hispanic White White non-hispanic AllNote: Based on Decennial census. White category does not exclude those of Hispanic Origin. HispanicOrigin can be of any race. White non-Hispanic is available from 1980-2000 only.SOURCE: U.S. Census Population Division Census 2000 PHC-T-41. A Half-Century of Learning: HistoricalStatistics on Educational Attainment in the United States, 1940 to 2000 8
  9. 9. Figure 3. Percent of population 25 years of age and older who have a BA degree: Decennial Census Data: 1940-2000 100 90 80 70 60 50 40 30 27 22 26 20 17 22 11 17 11 14 10 7 8 8 10 5 9 4 8 1 2 4 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 Hispanic Black White White non-Hispanic Note:Based on Decennial census. White category does not exclude those of Hispanic Origin. Hispanic Origin canbe of any race. White non-Hispanic is available from 1980-2000 onlySOURCE: U.S. Census Population Division Census 2000 PHC-T-41. A Half-Century of Learning: HistoricalStatistics on Educational Attainment in the United States, 1940 to 2000 9
  10. 10. Figure 4. Percent of total population 25 and older with high school diploma or equivalent by state: 1940-2000 100 90 80 70 60 50 40 30 20 10 0 1930 1940 1950 1960 1970 1980 1990 2000 2010NOTE: This distribution ranged from 15 percent in Arkansas to 41 percent in the District of Columbia and37 percent in California in 1940; and ranged from 73 percent in Mississippi to 88 percent in 4 states, Utah,Wyoming, Minnesota, and Alaska in the year 2000.SOURCE: U.S. Census Population Division Census 2000 PHC-T-41. A Half-Century of Learning: HistoricalStatistics on Educational Attainment in the United States, 1940 to 2000 10
  11. 11. Figure 5. Percent of total population 25 and older with BA degree or higher by state: 1940-2000 45 40 35 30 25 20 15 10 5 0 1930 1940 1950 1960 1970 1980 1990 2000 2010NOTE: This distribution ranged from 2 percent in Arkansas to 11 percent in the District of Columbia and 7percent in California and Nevada in 1940; and ranged from 15 percent in West Virginia to 39 percent inDistrict of Columbia and 33 percent in Massachusetts in 2000.SOURCE: U.S. Census Population Division Census 2000 PHC-T-41. A Half-Century of Learning: HistoricalStatistics on Educational Attainment in the United States, 1940 to 2000 11
  12. 12. Figure 6, also using the decennial-census-data, plots by state the gap between the percentof white and black persons 25 years of age and older having a high school diploma orhigher from 1940 to 2000; and figure 7 shows similar information for the BA or higherattainment statistic. Figure 6 shows the increase in the high school gap, up to 1960followed by a decline in most states. In 2000, there were 4 states where the percent ofblacks having this credential was higher than that of the white population. In 2000, thehigh school gap nationwide was 12 percentage points (84 compared to 72) and the BA gaprepresenting a much higher percentage difference was similar (11/12 percentage points--26compared to 14). Figure 8 based on figures 2 and 3 plots the national gap at each period1940-2000 and suggests that in periods of majority population rapid growth in educationalattainment, the black-white gap seems to grow, (such as the period between 1950 and 1970for high schools and between 1970 and 2000 for BA attainment).Figure 6. Plot of gap between percent of white and black population over 25 with high school diploma or equivalent by state: 1940-2000 70 60 50 40 30 20 10 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 -10 -20 -30 -40NOTE. The gap ranged from 8 in West Virginia in 1940 to 38 percentage points in California in 1940. In2000 the gap ranged from –8 in North Dakota one of 4 states to have a negative gap to 24 in the District ofColumbia and 19 in Mississippi and 18 in Wisconsin.SOURCE: U.S. Census Population Division Census 2000 PHC-T-41. A Half-Century of Learning: HistoricalStatistics on Educational Attainment in the United States, 1940 to 2000 12
  13. 13. Figure 7. Plot of gap between percent of white and black population over 25 with a BA or higher by state: 1940-2000 70 60 50 40 30 20 10 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 -10 -20NOTE. The gap ranged from less than 1 in Alaska and Hawaii and 1 in West Virginia in 1940 to –8 inMontana and –4 in Vermont and –1 in Idaho to 59 percentage point gap in DC and 20 point gap inConnecticut and 17 percentage gap in Virginia in 2000.SOURCE: U.S. Census Population Division Census 2000 PHC-T-41. A Half-Century of Learning: HistoricalStatistics on Educational Attainment in the United States, 1940 to 2000 13
  14. 14. Figure 8. Plot of gap between percent of white and black population over 25 with high school diploma or equivalent and percent with BA or higher: 1940-2000 25 22.7 23.1 21.5 20 18.5 17.6 15 14.8 11.8 11.3 10 10.1 8.7 6.9 5 4.4 4.6 3.6 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 High school completion gap BA or higher gapNOTE. This chart based on figures 2 and 3 illustrates that in periods of rapid growth in majority populationeducational attainment the gap seems to grow, (such as the period between 1950 and 1970 for high schoolsand between 1970 and 2000 for BA attainment).SOURCE: U.S. Census Population Division Census 2000 PHC-T-41. A Half-Century of Learning: HistoricalStatistics on Educational Attainment in the United States, 1940 to 2000 3.1.2 Public School Cohort Survival RateThe outcome indicator representing high school completion that we used in theregressions in this paper is the public school cohort survival rate (CSR) for 2004 aspublished on the NCHEMS website and developed by Tom Mortenson. This statisticrepresents the ratio of the total 9th grade public school enrollment to public schooldiplomas awarded 4 years later. It is derived from NCES/CCD data on enrollment anddiplomas awarded and provides a standardized measure across states. It is similar to otherCCD based completion statistics such as those noted in appendix table A-1 and the NCESaveraged freshman cohort completion rate. We used this version due to its’ availabilityback to 1990 and ease of merging into our database. The CSR rate by state for 2004 isgraphed below in figure 9. Figure 10 graphs the rate by state for 1990-2004. Apart fromsome outliers, there appears to be little change with a slight downward trend. Nationally,the CSR rate was 71.2 in 1990, 67.1 in 2000, and 69.7 in 2004. 14
  15. 15. Figure 9. Public high school cohort survival rate by state: 2004 N ew J ers ey 91. 3 U tah 85. 1 N orth D akota 84. 7 Iowa 84. 5 N eb ras ka 83. 8 M i n n es ota 83. 6 V ermon t 82. 6 S ou th D akota 81. 5 Id ah o 79. 6 M on tan a 78. 6 P en n s yl van i a 78. 4 W i s c on s i n 78 M ai n e 77. 5 M i s s ou ri 77. 2 K an s as 77 O hio 76 C on n ec ti c u t 75. 9 N ew H amp 75. 7 Il l i n oi s 75. 5 A rkan s as 75. 3 W yomi n g 75. 1 M as s ac h u s ett 74. 6 O kl ah oma 74. 1 M aryl an d 73. 7 V i rg i n i a 73. 2 C ol orad o 73. 2 W es t V i rg i n i a 73. 1 O reg on 72. 4 R h od e Is l an d 72. 2 C al i f orn i a 70. 7 W as h i n g ton 70. 2 In d i an a 70. 1 US 69. 7 M i c h i g an 69. 1 L ou i s i an a 68. 6 T exas 67. 7 D el aware 65. 4 H awai i 64. 9 K en tu c ky 64. 8 A ri z on a 64. 3 N orth C arol i n a 64. 2 T en n es s ee 63 N ew Y ork 62. 5 A l as ka 62. 5 N ew M exi c o 61. 8 Mis s is s ip p i 60. 3 A l ab ama 60. 3 F l ori d a 55 G eorg i a 54. 1 S ou th C arol 52. 1 N evad a 50. 7 0 10 20 30 40 50 60 70 80 90 100NOTE: Calculated based on number of 9th graders/High school graduates four years later (public highschools). Doesn’t account for students who are still enrolled or transfers to private high schools or out-of-stateSOURCE National Center for Higher Education Managers Systems (NCHEMS), Higher EducationInformation System; Tom Mortenson—Postsecondary Education Opportunity http://www.postsecondary.org 15
  16. 16. Figure 10. Public School Cohort Survival Rate by State 1990-2004 100 90 80 70 60 50 40 30 20 10 0 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006NOTE: Calculated based on number of 9th graders/High school graduates four years later (public highschools)SOURCE National Center for Higher Education Managers Systems (NCHEMS), Higher EducationInformation System; Tom Mortenson—Postsecondary Education Opportunity http://www.postsecondary.org 16
  17. 17. 3.1.2 Postsecondary Pipeline/Completion IndicatorFigure 11 graphs state differences in the postsecondary pipeline/completion indicatorstatistic used as the outcome variable in the regression models. This statistics is also TomMortenson’s calculation as included on the NCHEMS web site. It is based on CCDenrollment figures for 9th graders, estimating the number who graduate from high schoolwithin 4 years (based on the public HS graduation rates), the number who go directly tocollege (based on the college going rates of recent HS graduates), the number who returnfor their second year of college (based on the first-year retention rates), and the numberwho graduate from postsecondary program within 150% of program time (based on theIPEDS graduation rates). The calculation for high school graduation doesn’t account fortransfers to private high schools and out-of-state. The calculation for college graduationdoesn’t account for transfers across institutions. By state, rates range from 5.8 in Alaskato 27.9 in South Dakota.Figure 11. Postsecondary pipeline/completion indicator, percent of 9th grade high school cohort estimated to graduate high school, enter postsecondary directly and obtain postsecondary degree within 150 percent of program time by state: 2004 S out h Da kot a 27. 9 Iowa 27. 4 Ne w J e rs e y 27. 3 Minne s ot a 27. 3 P e nns ylva nia 27. 1 Ma s s a c hus e t t s 26. 1 Nort h Da kot a 25. 1 Wyoming 24. 9 Ne bra s ka 24. 7 Ne w Ha mp 24. 5 C onne c t ic ut 24. 0 Wis c ons in 23. 7 V irg inia 22. 4 Ka ns a s 22. 2 V e rmont 22. 1 India na 21. 7 De la wa re 20. 4 C olora do 20. 4 R hode Is la nd 20. 3 Ne w York 20. 2 Ma ine 20. 2 Illinois 19. 9 Mis s ouri 19. 8 O hio 19. 5 Ma ryla nd 19. 4 Mont a na 18. 8 Nort h C a rolina 18. 7 US 18. 4 Mic hig a n 17. 9 C a lifornia 16. 9 Ut a h 16. 8 Te nne s s e e 16. 7 Wa s hing t on 16. 3 We s t V irg inia 15. 7 Ida ho 15. 7 O kla homa 15. 3 A rka ns a s 15. 3 A rizona 15. 3 S out h C a rolina 15. 0 O re g on 15. 0 F lorida 14. 5 L ouis ia na 14. 3 G e org ia 14. 1 A la ba ma 13. 8 Te xa s 13. 3 Ha wa ii 12. 8 Ke nt uc ky 12. 3 Ne w Me xic o 11. 9 Mis s is s ippi 11. 0 Ne va da 9. 9 A la s ka 5. 8 0 5 10 15 20 25 30NOTE: This statistics is calculated based on CCD enrollment figures for 9th graders, estimating the numberwho graduate from high school within 4 years (based on the public HS graduation rates), the number who godirectly to college (based on the college going rates of recent HS graduates), the number who return for theirsecond year of college (based on the first-year retention rates), and the number who graduate frompostsecondary program within 150% of program time (based on the IPEDS graduation rates).The calculation for high school graduation doesn’t account for transfers to private high schools and out-of-state. The calculation for college graduation doesn’t account for transfers across institutions.SOURCE: NCHEMS Information System web site; Tom Mortenson—Public high school graduation rates,Tom Mortenson—College-going rates of students directly from HS, ACT Institutional Survey—Freshmen tosophomore retention rates, NCES-IPEDS Graduation Rate Survey—Graduation Rates 17
  18. 18. 3.2 Selected Achievement Outcome VariablesFigures 12a to 15 present statistics on the achievement variables included in the regressionmodels. Our historical information is much more limited than with attainment.3.2.1 NAEP 8th Grade Math ScoresWe used state 8th grade NAEP math scores for our achievement indicator outcomevariable. Unfortunately, 12th grade NAEP is not state representative. By state, NAEP 8thgrade math average scores range from 262 in Alabama and Mississippi to 292 inMassachusetts and 290 in Minnesota (figure 12a).Figure 12b shows another NAEP statistic, the percent categorized as at or aboveproficient in 8th grade math by state. We use this variable in the regression modelsdiscussed in section 5. Figure 12b shows much the same state line up as in figure 12a, witha few differences. Looking at Figure 13, which graphs the state average score for 1990, 2000, and 2005, wesee the trend upward in the period graphed, continuing a trend that was also apparentnationally between 1980 and 1990.Figure 12a. NAEP average 8th grade math score by state: 2005 Ma s s a c h u s e tts 292 Min n e s o ta 290 V e rmo n t 287 S o u th D a ko ta 287 No rth D a ko ta 287 Mo n ta n a 286 Wis c o n s in 285 Wa s h in g to n 285 Ne w Ha mp s h ire 285 V irg in ia 284 Ne w J e rs e y 284 Ne b ra s ka 284 Ka ns a s 284 Io wa 284 O h io 283 Wyo min g 282 O re g o n 282 No rth C a ro lin a 282 In d ia n a 282 Te xa s 281 S o u th C a ro lin a 281 P e n n s ylva n ia 281 Ma in e 281 Id a h o 281 D e la wa re 281 C o n n e c tic u t 281 C o lo ra d o 281 Ne w Y o rk 280 U ta h 279 A la s ka 279 US 278 Ma ryla n d 278 Illin o is 278 Mic h ig a n 277 Mis s o u ri 276 K e n tu c ky 274 F lo rid a 274 A riz o n a 274 R h o d e Is la n d 272 G e o rg ia 272 A rka n s a s 272 Te n n e s s e e 271 O kla h o ma 271 Ne va d a 270 We s t V irg in ia 269 C a lifo rn ia 269 L o u is ia n a 268 Ha wa ii 266 Ne w Me xic o 263 Mis s is s ip p i 262 A la b a ma 262 245 250 255 260 265 270 275 280 285 290 295 18
  19. 19. SOURCE: U.S. Department of Education, National Center for Education Statistics, National Assessmentof Educational Progress (NAEP) 2005 dataFigure 12b.NAEP percent at or above proficient in 8th grade math by state: 2005 Min n e s o ta 43 Ma s s a c h u s e tts 43 V e rmo n t 38 Wis c o n s in 36 Wa s h in g to n 36 S o u th D a ko ta 36 Ne w J e rs e y 36 Mo n ta n a 36 No rth D a ko ta 35 Ne w Ha mp s h ire 35 Ne b ra s ka 35 C o n n e c tic u t 35 O h io 34 Ka ns a s 34 Io wa 34 V irg in ia 33 O re g o n 33 No rth C a ro lin a 32 C o lo ra d o 32 Te xa s 31 P e n n s ylva n ia 31 Ne w Y o rk 31 U ta h 30 S o u th C a ro lin a 30 Mic h ig a n 30 Ma ryla n d 30 Ma in e 30 In d ia n a 30 Id a h o 30 D e la wa re 30 Wyo min g 29 A la s ka 29 Illin o is 28 Mis s o u ri 26 F lo rid a 26 A riz o n a 26 R h o d e Is la n d 23 G e o rg ia 23 K e n tu c ky 22 C a lifo rn ia 22 A rka n s a s 22 Te n n e s s e e 21 Ne va d a 21 O kla h o ma 20 Ha wa ii 18 We s t V irg in ia 17 L o u is ia n a 16 A la b a ma 15 Ne w Me xic o 14 Mis s is s ip p i 13 0 5 10 15 20 25 30 35 40 45 50SOURCE: U.S. Department of Education, National Center for Education Statistics, National Assessmentof Educational Progress (NAEP) 2005 data 19
  20. 20. Figure 13. NAEP average 8th grade math score by state: 1990, 2000, 2005 300 290 280 270 260 250 240 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006NOTE: Nationwide NAEP 8th grade math scores were 262 in 1990; 270 in 2000; and 274 in 2005. Amongstates included in 1990, the highest score was North Dakota and the lowest was Louisiana with 246. By 2005the highest score was obtained by Massachusetts 292 and the lowest by Alabama, 262 and Mississippi. In1990 state estimates were available only for 32 states and did not include Massachusetts.SOURCE: U.S. Department of Education, National Center for Education Statistics, National Assessmentof Educational Progress (NAEP) 1990, 2000, 2005 data 20
  21. 21. 3.2.2 Rate per 1000 High School Graduates who Score above 1200 on SAT or 26 on ACTThe other achievement outcome indicator we included in our regressions is anotherstatistic from the NCHEMS web site—the rate per 1000 high school graduates who scoreabove 1200 on the SAT or above 26 on the ACT (figure 14). This statistic is limited inthat it does not take into account those who might have taken both tests—and differencesin states in the percent taking two of the tests may affect these tabulations. It should alsobe noted that this is the rate per 1000 high school graduates and differential rates of highschool graduation would also affect comparisons by state. Rates range from 98 inMississippi to 259 in Colorado. Figure 15, graphing results between 1999 and 2004 showthe increase in this rate for most states, and also a little more spread among the states in2004 than in 1999.Figure 14. Rate per 1000 high school graduates who scored 1200 or above on combined SAT or 26 or above on ACT: 2004 C o lo ra d o 259 Ma s s a c h u s e tts 253 Illin o is 237 C o n n e c tic u t 234 Ne w Y o rk 228 Min n e s o ta 2 18 Ne w Ha mp s h ire 2 17 O h io 2 13 Mo n ta n a 207 Ne w J e rs e y 206 Te n n e s s e e 205 Ka ns a s 201 Ne b ra s ka 19 8 Wis c o n s in 19 5 Ma ryla n d 19 4 V e rmo n t 18 9 V irg in ia 18 5 Wa s h in g to n 18 5 US 18 4 Mic h ig a n 18 4 Mis s o u ri 18 2 A la s ka 17 8 O re g o n 17 1 No rth D a ko ta 17 0 Io wa 16 9 F lo rid a 16 7 Id a h o 16 7 G e o rg ia 16 6 S o u th D a ko ta 16 5 U ta h 16 2 Ma in e 16 1 No rth C a ro lin a 16 1 R h o d e Is la n d 15 7 P e n n s ylva n ia 15 7 In d ia n a 15 6 K e n tu c ky 15 6 Wyo min g 15 3 Ha wa ii 15 3 D e la wa re 15 0 C a lifo rn ia 14 6 A la b a ma 14 4 S o u th C a ro lin a 14 0 Te xa s 13 8 A rka n s a s 13 3 O kla h o ma 13 2 L o u is ia n a 13 2 We s t V irg in ia 12 8 Ne w Me xic o 12 7 Ne va d a 12 2 A riz o n a 116 Mis s is s ip p i 98 0 50 10 0 15 0 200 250 300NOTE: The number of SAT Math and Verbal Scores 1200 and above + ACT Composite Scores 26 andabove) per 1,000 high school graduatesSOURCE: SAT. The College Board. "2001 SAT V+M Score Bands Report," unpublished data ACT."Number of 2001 High School Graduates with ACT Composite Scores of 26 or Higher," unpublishedanalysis, Iowa City, Iowa. High School Graduates. Western Interstate Commission for Higher Education.Knocking at the College Door: Projections of High School Graduates by State and Race/Ethnicity 1996-2012. Boulder, C0 21
  22. 22. Figure 15. Rate per 1000 high school graduates who scored 1200 or above on combined SAT or 26 or above on ACT: 1999, 2001, 2004 300 250 200 15 0 10 0 50 0 19 9 8 19 9 9 2000 2001 2002 2003 2004 2005NOTE: The number of SAT Math and Verbal Scores 1200 and above + ACT Composite Scores 26 andabove per 1,000 high school graduatesSOURCE: SAT. The College Board. " SAT V+M Score Bands Report," unpublished data ACT. "Numberof High School Graduates with ACT Composite Scores of 26 or Higher," unpublished analysis, Iowa City,Iowa. High School Graduates. Western Interstate Commission for Higher Education. Knocking at the CollegeDoor: Projections of High School Graduates by State and Race/Ethnicity 1996-2012. Boulder, C04. Results of the Regression Runs on Attainment4.1 High School Cohort Survival Rate4.1.1 Demographic Predictors of High School Cohort Survival RateTable 1 and figures 16 and 17 summarize results from a forward selection regressionmodel for the outcome variable public high school cohort survival rate (CSR) in 2004.The demographic model “explains” 72 percent of the variation, with the group’s entitled“parent education”, “ parent employment”, and “population density” having a positivesign and “mobility”, “race” and “ethnicity/immigration” having a negative sign. In thismodel, the “race” group only includes percent black. Based on a factor analysis, weincluded the Hispanic percentage variable with the “ethnicity/immigration” group thatalso includes percent foreign born and percent speaking English as first language. Thegroup “population density” is the number per square mile and “mobility” is the percent ofstate population that lived in a different state 1 year earlier. The group “parent education”accounts for 40 percent of the variation, with “mobility” adding another 9 percent. The 22
  23. 23. model groups “race” and “ethnicity/immigration” each contribute 7 percent and the“parent employment” variable adds another 3 percent and “population density” 2 percent(figure 15). Differences by state between actual and predicted rates, (figure 17) rangefrom +14 in New Jersey and +11 in Arkansas to –8 in Indiana, South Carolina, andNevada. We note here that the models we ran initially included variables representingpoverty and also income directly; however, the income variables were highly related toeducation levels and so did not enter the models. The poverty variable did enter themodel at the last step, and controlling for the other SES variables already in the model itssign was positive and it explained an additional 3 percent of the variation. We did notinclude it in the model presented here.Table 1. Summary of forward selection regression model using grouped option explaining variation in state differences in public school high school cohort survival rate: demographic variables onlyStep Group Direction Number Partial Model F Pr> F entered of R- R- Value variables Square Square Parent + 1 Education+ 2 0.4022 0.4022 15.81 <.0001 2 Mobility- - 3 0.0916 0.4938 8.32 0.0059 3 Race- - 4 0.0732 0.567 7.61 0.0084 4 Ethnicity/imm- - 6 0.0735 0.6405 4.39 0.0184 Parent + 5 Employment+ 7 0.0276 0.6681 3.49 0.0689 Population + 6 density+ 8 0.024 0.6921 3.2 0.081NOTE: Calculated based on number of 9th graders/High school graduates four years later (public highschools). Doesn’t account for students who are still enrolled or transfers to private high schools or out-of-stateSOURCE National Center for Higher Education Managers Systems (NCHEMS), Higher EducationInformation System; Tom Mortenson—Postsecondary Education Opportunity http://www.postsecondary.orgRepresents residuals tabulated based on SAS PROC REG SIMPLE;PROC REG SIMPLE; ID State1; MODEL PCSR04 = {alhsd20 onparpst} {pblk05} {phispa05 pforbo04}{parempl} {posqm05} {mobil05} / p r cli clm sle = .15 SELECTION = Forward GROUPNAMES = education race ethnicity/immigration employment pop density mobility ; 23
  24. 24. Figure 16. Distribution of variance among “groups” in the demographic model examining state variation in public school high school cohort survival: 2004 Unexplained 31% Education+ 41% Pop density+ 2% Employment+ 3% Ethnicityimmigration- 7% Mobility- Race- 9% 7%NOTE: Model R-square =.69 Allocation of variance based on Partial R-Squares. PCSR04 =Dependent variable public high school cohort survival rate: 2004SOURCE: See table 1 above 24
  25. 25. Figure 17. Difference between actual and predicted (residuals) high school cohort survival rate (CSR) from model with demographic variables only: 2004 Ne w J e rs e y 14 A rka ns a s 11 Ida ho 9 Ut a h 9 L ouis ia na 8 C a lifornia 6 V irg inia 6 Mis s is s ippi 5 V e rmont 5 Ma ryla nd 5 O re g on 4 Illinois 4 Mont a na 3 We s t V irg inia 3 Mis s ouri 3 O kla homa 2 Iowa 2 Te xa s 2 Nort h Da kot a 2 Ne bra s ka 2 A rizona 2 S out h Da kot a 1 Minne s ot a 0 C olora do 0 P e nns ylva nia 0 Nort h C a rolina -1 -1 ine Ma -2 Ha wa ii -2 Wyoming -3 A la s ka -3 Ka ns a s -3 De la wa re -3 Te nne s s e e -3 O hio -4 Ne w York -4 A la ba ma -4 Wis c ons in -4 Wa s hing t on -4 C onne c t ic ut -5 R hode Is la nd -5 Ne w Me xic o -5 Ne w Ha mps hire -5 Ma s s a c hus e t t s -6 Ke nt uc ky -6 G e org ia -7 Mic hig a n -7 F lorida -8 Ne va da -8 S out h C a rolina -8 India na -10 -5 0 5 10 15 20NOTE: Model R-square = .69. Allocation of variance based on Partial R-Squares. PCSR04 = Dependentvariable public high school cohort survival rate: 2004SOURCE: see table 1 above 25
  26. 26. 4.1.2 Adding Selected State Policy and Education Related Statistics to the Demographic Model of High School Cohort Survival RateTable 2 and figure 18 summarize the change in the model when selected state policy andsystem statistics are entered into the model using the same forward selection procedure.The total R-squared is not much increased from the demographic model; however, severalof the state policy variables enter into the model—demonstrating what simple correlationsrevealed that some of the policies are highly related to the demographic differences. The“Parent education” group remains the major explanatory variable with a Partial R-squaredof .36 “School size” is negative and adds 16 percent explanation. “Mobility” adds 9percent and exit exams 5 percent—both with a negative sign. “Parent employment”contributes an additional 2 percent and “technology” marginally significant in the modelalso contributes 2 percent. Note that “race” and “ethnicity/immigration” did not enterthe model with this configuration. “Exit exams” is highly correlated (.40) withrace/ethnicity variables, so it is not clear if the apparent negative effect is related to the exitexams themselves or to other variables with which it is correlated. The findings forschool size are consistent with other research that has found a relationship to high schoolcompletion to this variable (Garrett Z, Newman, Elbourne, Bradley, Noden, Taylor, West 2004).The variables representing “course requirements”, “teacher salaries”, and “teaching requirements”did not reach the significance levels needed to enter the model and were not included. As thesevariables were missing for 9 states, after testing the model with these variables and without, weremoved them from the model. 26
  27. 27. Table 2. Summary of forward selection regression model using grouped option explaining variation in state differences in public school high school cohort survival rate: state policy and system statistics added to demographic modelStep Group Direction Number Partial Model F Pr> F entered of R- R- Value variables Square Square Parent + 1 education 2 0.3639 0.3639 11.16 0.0001 2 school size - 3 0.1629 0.5268 13.08 0.0009 3 mobility - 4 0.09 0.6168 8.69 0.0055 4 exit exam - 5 0.0478 0.6646 5.13 0.0296 5 pop density + 6 0.032 0.6966 3.7 0.0627 Parent + 6 employment 7 0.0244 0.721 2.98 0.0936 7 technology + 8 0.0207 0.7417 2.64 0.1138NOTE: Calculated based on number of 9th graders/High school graduates four years later (public highschools). Doesn’t account for students who are still enrolled or transfers to private high schools or out-of-stateSOURCE National Center for Higher Education Managers Systems (NCHEMS), Higher EducationInformation System; Tom Mortenson—Postsecondary Education Opportunity http://www.postsecondary.orgSOURCE: Represents residuals tabulated based on SAS PROC REG SIMPLE;ID State1;MODEL PCSR04 ={pu18po99}{alhsd20 onparpst}{pblk05}{phispa05 pforbo04 parengsk} {parempl} {posqm05} {mobil05}{Tecindx5} {HSEXIT2} {comsch05} {ASSSr03} {ntesal}{Majsteac} {Mcourreq} / p r cli clm sle = .15 SELECTION = Forward GROUPNAMES = Poverty Education Race Ethnicity EmploymentPopdens Mobility technology Exit examComp age Schoolsize teachsal majteach math course; 27
  28. 28. Figure 18. Distribution of variance among “groups” in the model examining state variation in public school high school cohort survival: demographic and state policy/system variables 2004 U n exp l ai n ed 26% E d u c ati on + 37% T ec h n ol og y+ 2% E mp l oymen t+ 2% P op d en s i ty+ 3% E xi t exam- 5% S c h ool s i z e- 16% M ob i l i ty- 9%NOTE: Model R-square = .74. Allocation of variance based on Partial R-Squares. PCSR04 =Dependent variable public high school cohort survival rate: 2004SOURCE: See table 1 above 28
  29. 29. 4.2 A Postsecondary Pipeline/Completion Indicator—(Rate of Graduation from High School, Entering Postsecondary the Next Year and Completing a Postsecondary Program in 150 percent of Program Time)4.2.1 Demographic Predictors of Postsecondary Pipeline/Completion IndicatorTable 3 and figures 19 and 20 summarize results from a forward selection regressionmodel for the outcome variable representing the postsecondary pipeline/completionindicator. Results show that the demographic variables account for .78 percent of thevariation. “Parent education” has a partial r square of .53, followed by “parentemployment” and “mobility.” Comparing the postsecondary pipeline/completion resultswith those from the high school cohort survival model, we see that “parent education” and“parent employment” both explain a relatively higher proportion of the variation; andethnicity/immigration explains less. The “race” variable does not meet the .15 thresholdfor entrance into this model. Wyoming, Pennsylvania and South Dakota have the largestpositive difference between actual and predicated results and Utah, Alaska, and Marylandthe largest negative differences. As this measure may be subject to bias related todifferences in in-state and out of state postsecondary attendance rates it is difficult tointerpret these results for individual states.Table 3. Postsecondary pipeline/completion indicator, summary of forward selection regression model: demographic variables onlyStep Group Direction Number Partial Model F Pr> F entered of R- R- Value variables Square Square 1 Parent Education+ + 2 0.5384 0.5384 27.41 <.0001 2 Parent Employment+ + 3 0.0783 0.6167 9.4 0.0036 3 Mobility- - 4 0.0888 0.7055 13.57 0.0006 4 Ethnicity/immigration- - 6 0.0418 0.7473 3.56 0.0371 5 Pop density+ + 7 0.0285 0.7759 5.34 0.0258 29
  30. 30. NOTE: This statistic is calculated based on CCD enrollment figures for 9th graders, estimating the numberwho graduate from high school within 4 years (based on the public HS graduation rates), the number who godirectly to college (based on the college going rates of recent HS graduates), the number who return for theirsecond year of college (based on the first-year retention rates), and the number who graduate frompostsecondary program within 150% of program time (based on the IPEDS graduation rates).SOURCE: NCHEMS Information System web site; Tom Mortenson—Public high school graduation rates,Tom Mortenson—College-going rates of students directly from HS, ACT Institutional Survey—Freshmen tosophomore retention rates, NCES-IPEDS Graduation Rate Survey—Graduation RatesSOURCE: Represents residuals tabulated based on SAS PROC REG SIMPLE; ID State1; MODEL PG9DCG04 = {pu18po99}{alhsd20 onparpst} {pblk05} {phispa05 pforbo04}{parempl}{posqm05}{mobil05} / p r cli clm sle = .15 SELECTION = Forward GROUPNAMES = education race ethnicity/immigration employment pop density mobility ; 30
  31. 31. Figure 19. Distribution of variance among “groups” in the demographic model examining state variation in postsecondary pipeline/completion indicator): 2004 Unexplained 22% E duc ation+ 54% P op dens ity- 3% E thnic ity/immi- 4% Mobility- 9% E mployment+ 8%NOTE: Model R-square =. 78 Allocation of variance based on Partial R-Squares. Postsecondarypipeline/completion indicator is calculated based on chance of graduation from highschool, enter postsecondary and complete a postsecondary program in 150 percent ofprogram timeSOURCE: See table 3 above. 31
  32. 32. Figure 20. Difference between actual and predicted (residuals) for postsecondary pipeline/completion indicator from model with demographic variables only: 2004 Wyo min g 6. 6 P e n n s ylva n ia 4. 4 S o u th D a ko ta 4. 4 C a lifo rn ia 3. 1 Io wa 2. 9 A riz o n a 2. 8 Ne w Y o rk 2. 7 We s t V irg in ia 2. 5 Ma s s a c h u s e tts 2. 2 Ne w J e rs e y 2. 0 Mo n ta n a 1. 8 No rth C a ro lin a 1. 8 A rka n s a s 1. 6 Min n e s o ta 1. 4 O re g o n 1. 3 Te n n e s s e e 1. 1 V irg in ia 1. 1 C o lo ra d o 0. 9 L o u is ia n a 0. 8 Wa s h in g to n 0. 6 Ma in e 0. 5 Ne w Ha mp s h ire 0. 3 Wis c o n s in 0. 2 Mis s o u ri 0. 1 - 0. 1 Id a h o - 0 . 2 Mis s is s ip p i - 0 . 2 In d ia n a - 0 . 3 Ne b ra s ka - 0 . 3 - 0 . 3D e la wa re - 0 . 4 S o u th C a ro linis Illin o a - 0. 7 - 0. 8 V e rmo n t Ne w Me xic o - 0. 9 R h o d e Is la n d - 1. 1 rth D a ko ta No - 1. 3 F lo rid a - 1. 7 Ne va d a - 1. 8 Ka ns a s - 1. 8 O kla h o ma - 1. 8 Ha wa ii - 2. 0 G e o rg ia - 2. 3 C o n n e c tic u t - 2. 4 A la b a ma - 2. 4 Te xa s - 2. 5 Mic h ig a n - 2. 6 O h io - 3. 1 K e n tu c ky - 4. 3 Ma ryla n d - 4. 6 A la s ka - 7. 1 U ta h - 8. 0 - 6. 0 - 4. 0 - 2. 0 0. 0 2. 0 4. 0 6. 0 8. 0NOTE: Model R-square = .77. Allocation of variance based on Partial R-Squares. =SOURCE: See table 3 above. 32
  33. 33. 4.2.2 Adding Selected State Policy and Education Related Statistics to the Demographic Model of the Postsecondary Pipeline/Completion IndicatorTable 4 and figure 21 summarize the change in the model when selected state policy andsystem statistics are entered into the model using the same forward selection procedure.The total R-squared is increased to .83. “Parent education” is highly related to thepostsecondary pipeline/completion statistic accounting for 57 percent of the variation.Mobility (percent of population who lived out of the state one year earlier) is persistentlynegative. Of the state education system variables (advanced diploma, teacher salary, mathcourse requirements, technology score, compulsory school age, and exit exam) only schoolsize entered this model.Table 4. Summary of forward selection regression model using grouped option explaining variation in state differences in postsecondary pipeline/completion indicator: state policy and system statistics added to demographic modelStep Group Direction Number Partial Model F Pr> F entered of R- R- Value variables Square Square Parent + 1 Education+ 2 0.5734 0.5734 30.91 <.0001 Parent - 2 Mobility- 3 0.0777 0.6511 10.02 0.0028 Parent + 3 Employment+ 4 0.1041 0.7552 18.72 <.0001 4 School size - 5 0.0499 0.8051 11.01 0.0019 5 Pop Density + 6 0.0224 0.8275 5.45 0.0244SOURCE: see table 3 above Represents residuals tabulated based on SAS PROC REG SIMPLE; PROC REG SIMPLE;ID State1;MODEL PG9DCG04 ={alhsd20 onparpst} {pblk05}{phispa05 pforbo04 parengsk} {parempl} {posqm05} {mobil05}{Tecindx5} {HSEXIT2} {comsch05} {ASSSr03} {advdiplo} / p r cli clm sle = .15 SELECTION = Forward GROUPNAMES = Education Race Ethnicity EmploymentPopdens Mobility technology Exit examComp age Schoolsize advdiploma /*teachsal majteach mathcourse*/; 33
  34. 34. Figure 21. Distribution of variance among “groups” in model examining state variation in postsecondary pipeline/completion indicator: demographic and state policy and system variables: 2004 Unexplained 17% Popdens 2% Schoolsize 5% Education 58% Employment 10% Mobility 8%NOTE: Model R-square = .85 Allocation of variance based on Partial R-Squares.SOURCE: See table 3 above 34

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