## .center[Community and Institutional Factors in Community College Outcomes:] ## .center[A Path Analysis] ### Mark Perkins, Ph.D. ### Johannes Addido, Ph.D. Candidate #### .center[University of Wyoming] --- ## Content of the Presentation - Problem and Background - Review of Literature - Theoretical Framework - Methodology - Results - Discussion --- # .center[Background] - Community college (CC) students face many financial and personal struggles (Foong et al., 2017) - CC students are more likely to be first generation, non-white, and low income (Juszkiewicz, 2015) - Only 28% of first-time IPEDS CC students graduate in three years (150% time) - This is in comparison to 62% of four-year students - Transfer rates are also a factor in determining graduation rates --- ## .center[Purpose] ### The purpose of this study was to understand the relationship between - Institution and community factors, - IPEDS reported public community college graduation and retention rates (n = 880). - U.S. Census data on county economic and demographic factors. ### We ask three main research questions - What CC and community factors best predict institutional full-time retention and 150% graduation rates? - How do the strongest predictors from the ML model best predict graduation rates? - To what extent does persistence mediate graduation rates? --- ## .center[Academic Factors and Retention/Completion] - Of the 326 articles found on higher education and retention, only 7% were related to community colleges - However, about 41% of all undergraduate students are CC students (2020 Community College Snapshot) - Transferring to other institutions has an effect on the graduation rates (Xu et al, 2018; Aulck & West, 2017) - Taking developmental courses reduces chances of graduation and persistence (Fike & Fike, 2008; Linderman & Kolenovic, 2013; Crisp & Delgado, 2014) - Having a high school diploma (McKinney & Burridge, 2015) --- ## .center[Academic Factors and Retention/Completion (Continued)] - High school GPA as a better predictor than ACT/SAT (Hachey et all, 2014) - Online learning negatively correlates (Shea & Bidjerano, 2014; Hutington-Klein et al., 2017) - Access to federal funding programs e.g. work study, TRIO, etc. (Yu et al., 2020; Fike & Fike, 2008) --- ## .center[Campus Factors and Retention/Completion] - Student mentoring programs improve outcomes (McArther, 2005; Jacobs et al., 2015; Garza et al., 2021) - Living in campus housing improves outcomes, but is it practical? (Turk & González Canché, 2019) --- ## .center[Gender and Retention/Completion] - Students who identify as women are more likely to graduate than students who identify as men (Ewert, 2012; Gant, 2020) - Women often face challenges and stereotyping as suitable for certain programs (Jackson & Laanan, 2011) - Women are more likely to be caretakers of children and family members (Hess et al., 2014) --- ## .center[Race, Ethnicity and Retention/Completion] - Minority students are less likely to be retained than white students (McKinney & Burridge, 2015; Strayhorn, 2018) - Community colleges graduate only 12% of African American men and only 14.6% of Latinx males within three years (Wood, 2014, 2012) - Further, community colleges graduate African American men at lower rates than Native American (18.7%), Asian American (23.5%), and white (22.1%) men (Wood, 2014) --- ## .center[Immigration and Retention/Completion] - The United States has a rich history of immigration and many of these people seek to better themselves through community college education (Kim & Díaz., 2013; Suárez-Orozco et al., 2015; Teranishi et al., 2011) - These student face unique circumstances in comparison to white and minoritized populations (Gonzalez et al., 2013). - Some estimates purport that 25% of community college students are either immigrants or children of immigrants (Leo, 2021). - Immigrants or children of immigrants face unique challenges including childcare responsibilities, full-time jobs, English Language learning, and many of them are not documented, adding additional stress (Kim & Díaz., 2013; Teranishi et al., 2011). --- ## .center[Tinto and Astin: Theory of Student Success] ### Tinto's 1975 four key aspects - Student attributes - Individual Characteristics - Student interaction with college environment - Institutional characteristics and success ### Astin's Pre-Entry Factors (1999) - High school grades - Admissions scores - Race/Gender/Socio-economic status - "Presage" student variables --- ## .center[Quantitative Critical Theory (QuantCrit)] - We approached this using a QuantCrit framework - Emphasizes the use of quantitative research methods through a critical theory lens - Awareness of potential racism, oppression colonization, and white supremacy in institutions (Garcia et al., 2018; Gillborn et al., 2018; López et al., 2018; Stage & Wells, 2014) ### Four Premises of Quant/Crit - Racism and oppression as central facets of society and shape the way we define and collect data - Numbers are not neutral, there is always a story behind them - Categories are not natural and reports of unequal outcomes between races may perpetuate problems - Data cannot speak for themselves and require a fair voice to address biases in statistical modeling --- # .center[Deficit Thinking] - Occurs when the reader of research explicitly, subtly, or implicitly blames the research participant or population for perceived deficits ### Intentional Thinking - In this study, we took care to avoid deficit thinking and to frame this study around the goals of emancipation of oppressed populations within our results under the lens of QuantCrit - Instead of thinking like this: "Martians are less likely to graduate", we think like this: "Institutions are less likely to help Martians succeed" - Thus the rhetoric shifts the focus a bit from the subject to the system --- ## .center[About the Data] ### Integrated Postsecondary Educational Data System (IPEDS) - National Center of Educational Statistics <https://nces.ed.gov/ipeds/use-the-data> - 100%, 150%, and 200% Graduation Rates (we used 150% for this study) - Full-time and Part-time Persistence Rates - A plethora of demographic data ### United States Census Bureau - One of the largest data collections in the world <https://www.census.gov/> - Use of the tidycensus package in RStudio <https://walker-data.com/tidycensus/> - Over 14,000 rows of data in the key - Requires manual calcuation (see programming notes later) --- ## .center[Methodology] - Visit this site to find the data query code <https://rpubs.com/IPEDS/Data_Pull> - You also need to be sure to load all relevant packages ```r library(tidyverse) library(plyr) library(plotly) library(ggplot2) library(scales) library(RColorBrewer) library(tidycensus) library(ipeds) library(plyr) library(data.table) library(R.utils) library(stringr) library(questionr) library(rattle) graduationmodel<- read.csv("graduationmodel.csv") ``` --- # .center[Machine Learning] - In general, ML consists of several applications where a computer is able to act with minimal programming because the computer (or machine) learns while performing its programming (Bishop, 2006) - ML models use statistical techniques to build several models, run those models, converge them and then compare their results to yield optimal predictions (Brownlee, 2016). <img src="https://marksresearch.shinyapps.io/PictureSite/_w_c36be1e2/mlmodel.png" width="450" height="300" style="display: block; margin: auto;" /> --- # .center[Machine Learning (Continued)] - Random forest machine learning models stem from earlier decision tree models that calculate the most probable path to an outcome (Breiman et al., 1984; Landau & Barthel, 2010; Loh, 2011) - These non-parametric models have less statistical assumptions and use > < language to make decisions - For example if income is <X then the probablity of Y is Z - A raondom forest classifier uses ML to generate multiple iterations of decision trees and converge the results <img src="https://marksresearch.shinyapps.io/PictureSite/_w_c36be1e2/randomf.png" width="450" height="300" style="display: block; margin: auto;" /> --- # .center[Machine Learning (Continued)] - Random forest reports the most powerful classifications and predictions from the tree iterations - Uses bootstrapping to test hundreds of randomized sampled decision trees - Produces a mean decrease in accuracy if the variable was removed and a decrease in Gini to measure the decline in the tree split if removed - The r package rattle was used to conduct these analyses - <https://journal.r-project.org/archive/2009-2/RJournal_2009-2_Williams.pdf> <img src="https://marksresearch.shinyapps.io/PictureSite/_w_c36be1e2/gini.png" width="400" height="150" style="display: block; margin: auto;" /> --- # .center[Path Model] - A form of regression that also examines direction and order of predictor variables - Path models can consider mediation (alcohol and body weight to blood alcohol example) - The lavaan package was used to conduct the path analysis - <https://www.jstatsoft.org/article/view/v048i02> <img src="https://marksresearch.shinyapps.io/PictureSite/_w_c36be1e2/path.png" width="450" height="300" style="display: block; margin: auto;" /> --- ## .center[Methodology- Data] - After querying all the IPEDS and Census data, we wrote a custom data set - We used this dataset for our Random Forest model in Rattle - We saved it as a .csv as well ```r graduationrattle<- select(graduationmodel, RET_PCF, UNITID, INSTNM, L4GR150, InStateOffCampus, totcampuscrimeprop, Percent_Unemployed, WithHealth, med_income, PercentWhite, percentpopchange, HousePercent, percentmarried, percentdivorced, percentseperated, PercentSomeorASS, PercentBach, percenttribe, SingleParPercent, PercentNotCitizen, PercentImigrant, PercentRent, Urban_15, ENRFT_15, PCTENRWH_15, PCTENRBK_15, PCTENRHS_15, PCTENRAP_15, PCTENRAS_15, PCTENRNH_15, PCTENRAN_15, PCTENRW_15, PCTFT_15, internet) write.csv(graduationrattle,"graduationrattle.csv") ``` --- ## .center[Results- Descriptives] <div id="htmlwidget-f86f86e00939c67bcd4a" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-f86f86e00939c67bcd4a">{"x":{"filter":"none","vertical":false,"data":[["2018 150% Graduation Rate","2016 Full-Time Retention Rate","% In Same House County","In State Off Campus Cost","Median County Household Income","% Non-Resident in College*","% Asian in College*","% American Indian in College*","% Black at College*","% Hispanic at College*","% Unknown Ethnicity in College*","% Women at College","% White at college","% Students Full-Time in College","County Unemployment","% Bachelor Degrees in County","% Divorced in County","% Immigrant in County","% Married in County","% Not Citizens in County","5 Year County Population Change*","% Who Rent in County","% Separated in County","% With Associates or Some College","% Tribal in County*","% White in County","% Single Parent in County","% In County with Health Insurance","Distant Rural","Distant Town","Fringe Rural","Fringe Town","Large city","Large Suburb","Midsized City","Midsized Suburb","Remote Rural","Remote Town","Small City","Small Suburb"],["Dependent","Dependent/ Mediator","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent","Independent"],[863,863,920,906,924,920,920,920,920,920,920,920,920,920,924,920,920,924,920,924,924,920,920,920,924,924,924,924,46,98,166,16,90,140,77,30,21,102,120,19],["27.71%","59.18%","86.10%","$18,524.70 ","$57,121.01 ","2.82%","4.23%","3.59%","13.16%","16.04%","0.53%","57.30%","55.90%","40.54%","4.95%","26.82%","11.37%","9.69%","48.33%","5.18%","2.69%","32.97%","2.03%","57.96%","1.47%","74.73%","36.28%","86.68%","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A"],["12.31%","9.54%","3.17%","$37,88.89","$16,660.73 ","12.29%","8.67%","5.86%","15.32%","18.88%","5.95%","7.37%","24.72%","13.98%","1.57%","10.38%","2.03%","9.52%","5.54%","4.90%","4.59%","9.14%","0.85%","9.21%","5.96%","17.60%","9.66%","6.13%","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A","N/A"]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th>Variable.Name<\/th>\n <th>Type<\/th>\n <th>n<\/th>\n <th>Mean<\/th>\n <th>SD<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"initComplete":"function(settings, json) {\n$(this.api().table().header()).css({'background-color': ' #686A6C', 'color': 'white'});\n}","pageLength":7,"autoWidth":false,"scrollY":"300PX","columnDefs":[{"className":"dt-right","targets":2}],"order":[],"orderClasses":false,"lengthMenu":[7,10,25,50,100],"rowCallback":"function(row, data, displayNum, displayIndex, dataIndex) {\nvar value=data[0]; $(this.api().cell(row, 0).node()).css({'font-size':'75%'});\nvar value=data[1]; $(this.api().cell(row, 1).node()).css({'font-size':'75%'});\nvar value=data[2]; $(this.api().cell(row, 2).node()).css({'font-size':'75%'});\nvar value=data[3]; $(this.api().cell(row, 3).node()).css({'font-size':'75%'});\nvar value=data[4]; $(this.api().cell(row, 4).node()).css({'font-size':'75%'});\n}"}},"evals":["options.initComplete","options.rowCallback"],"jsHooks":[]}</script> --- ## .center[Results- Random Forest] <div id="htmlwidget-c51a99d240f37a0274aa" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-c51a99d240f37a0274aa">{"x":{"filter":"none","vertical":false,"data":[["% American Indian in College","% Asian in College","% Bachelor Degrees in County","% Black at College","% Divorced in County","% Hispanic at College","% Immigrant in County","% With Health Insurance","% In Same House County","% Married in County","% Non-Resident in College","% Not Citizens in County","% Separated in County","% Single Parent in County","% Students Full-Time in College","% Students Full-Time in College","% Tribal in County","% Unknown Ethnicity in College","% White at college","% White in County","% Who Rent in County","% With Associates or Some","% Women at College","County Population Change","County Unemployment","Distant Rural","Distant Town","Fringe Rural","Fringe Town","In State Off Campus Cost","Large City","Large Suburb","Median Household Income","Midsize City","Midsize Suburb","Remote Rural","Remote Town","Small City","Small Suburb"],[8.19,6.84,7.76,15.61,11.15,12.64,15.04,7.16,5.44,5.85,9.78,13.85,5.67,9.91,9.58,6.14,8.55,0.54,12.3,6.01,8.55,5.98,9.58,5.75,5,-0.13,3.45,0.9,-2.05,7.27,5.03,3.32,11.52,-0.58,8.35,1.2,1.63,-0.93,2.11],[0.17,0.12,0.12,0.21,0.18,0.24,0.36,0.13,0.12,0.1,0.17,0.29,0.13,0.18,0.25,0.22,0.18,0.01,0.18,0.11,0.11,0.1,0.28,0.12,0.11,0.01,0.01,0.01,0.01,0.18,0.01,0.01,0.23,0.01,0.04,0.02,0.01,0.01,0.01],[1.39,0.82,0.93,3.28,2.01,3.03,5.41,0.93,0.65,0.59,1.66,4.02,0.74,1.78,2.4,1.35,1.54,0.01,2.21,0.66,0.94,0.6,2.68,0.69,0.55,0,0.03,0.01,-0.02,1.31,0.05,0.03,2.65,-0.01,0.33,0.02,0.02,-0.01,0.02],[5.57,6.14,10.09,13.49,6.21,5.83,8.72,6.79,2.36,5.02,5.15,7.88,6.8,7.97,10.02,10.02,6.28,2.08,17.24,10.14,4.22,6.49,18.43,6.6,9.41,-0.45,-0.01,0.24,-0.78,5.42,3.47,0.22,10.98,-0.31,3.54,0.06,11.76,1.68,-0.79],[0.08,0.09,0.16,0.28,0.15,0.21,0.22,0.13,0.13,0.15,0.09,0.2,0.19,0.22,0.33,0.33,0.18,0.01,0.42,0.28,0.12,0.12,0.59,0.15,0.24,0.01,0.01,0.01,0,0.18,0.02,0.01,0.21,0.01,0.04,0.02,0.17,0.01,0],[0.45,0.55,1.61,3.78,0.93,1.22,1.92,0.88,0.31,0.75,0.46,1.58,1.29,1.75,3.31,3.31,1.13,0.02,7.24,2.84,0.51,0.78,10.87,0.99,2.26,0,0,0,0,0.98,0.07,0,2.31,0,0.14,0,2,0.02,0]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th>Variable<\/th>\n <th>D..AccG<\/th>\n <th>D..GiniG<\/th>\n <th>A.Gg<\/th>\n <th>D.AccR<\/th>\n <th>D.GiniR<\/th>\n <th>A.Gr<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"initComplete":"function(settings, json) {\n$(this.api().table().header()).css({'background-color': ' #686A6C', 'color': 'white'});\n}","pageLength":7,"autoWidth":false,"scrollY":"300PX","columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6]}],"order":[],"orderClasses":false,"lengthMenu":[7,10,25,50,100],"rowCallback":"function(row, data, displayNum, displayIndex, dataIndex) {\nvar value=data[0]; $(this.api().cell(row, 0).node()).css({'font-size':'75%'});\nvar value=data[1]; $(this.api().cell(row, 1).node()).css({'font-size':'75%'});\nvar value=data[2]; $(this.api().cell(row, 2).node()).css({'font-size':'75%'});\nvar value=data[3]; $(this.api().cell(row, 3).node()).css({'font-size':'75%'});\nvar value=data[4]; $(this.api().cell(row, 4).node()).css({'font-size':'75%'});\nvar value=data[5]; $(this.api().cell(row, 5).node()).css({'font-size':'75%'});\nvar value=data[6]; $(this.api().cell(row, 6).node()).css({'font-size':'75%'});\n}"}},"evals":["options.initComplete","options.rowCallback"],"jsHooks":[]}</script> --- # .center[Path Model code] ```r library(lavaan) library(semPlot) SEMData<- read.csv("graduationrattlePaperCleaned.csv") mediationmodel <- with(SEMData, "# direct effects GradRate ~ a*PCTBK + b*PCTHS + c*PercentImigrant + d*PercentNotCitizen + e*PCTFT + f*PCTWH + g*PercentWhite + h*PCTW + i*med_income + j*Retention Retention~ k*PCTBK + l*PCTHS + m*PercentImigrant + n*PercentNotCitizen + o*PCTFT + p*PCTWH + q*PercentWhite + r*PCTW + s*med_income #indirect effects indirectBlack := k*j indirectLatinX := l*j indirectImigrant := m*j indirectCitizen := n*j indirectFullTime := o*j indirectWhiteCol := p*j indirectWhiteCnty := q*j indirectWomCol := r*j indirectincome := s*j #total effects totalBlack := a + (k*j) totalLatinX := b + (l*j) totalImigrant := c + (m*j) totalCitizen := d + (n*j) totalFullTime := e + (o*j) totalWhiteCol := f + (p*j) totalWhiteCnty := g + (q*j) totalWomCol := h + (r*j) totalincome := i + (s*j)") ``` --- # .center[Model Fit] - This bootstraps to calculate the standardized and unstandardized coefficients - We write the standarsizied coefficients to a .csv to make tables ```r mediationmodel <- sem(mediationmodel, data = SEMData, se = "bootstrap", bootstrap = 500) summary(mediationmodel, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) stdsolution<-standardizedsolution(mediationmodel, type = "std.all") stdsolution write.csv(stdsolution, "persistperameterspaper.csv") ``` --- # .center[Path Model Results] <div id="htmlwidget-16cd6988853c7b7711eb" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-16cd6988853c7b7711eb">{"x":{"filter":"none","vertical":false,"data":[["Graduation Rate","Graduation Rate","Graduation Rate","Graduation Rate","Graduation Rate","Graduation Rate","Graduation Rate","Graduation Rate","Graduation Rate","Graduation Rate","Retention","Retention","Retention","Retention","Retention","Retention","Retention","Retention","Retention","Indirect Effects","Indirect Effects","Indirect Effects","Indirect Effects","Indirect Effects","Indirect Effects","Indirect Effects","Indirect Effects","Indirect Effects","Total Effects","Total Effects","Total Effects","Total Effects","Total Effects","Total Effects","Total Effects","Total Effects","Total Effects"],["% Black in College","% Latinx in College","% Immigrant in County","% Citizens in County","% Full-Time in College","% White in College","% White in County","% Women in College","Median Income","Retention","% Black in College","% Latinx in College","% Immigrant in County","% Citizens in County","% Full-Time in College","% White in College","% White in County","% Women in College","Median Income","indirectBlack","indirectLatinx","indirectImigrant","indirectCitizen","indirectFullTime","indirectWhiteCol","indirectWhiteCnty","indirectWomCol","indirectIncome","totalBlack","totalLatinx","totalImigrant","totalCitizen","totalFullTime","totalWhiteCol","totalWhiteCnty","totalWomCol","totalIncome"],[-0.01,0,0.09,-0.16,0.1,0.18,0.03,-0.24,-0.23,0.52,-0.03,0.49,0.28,-0.12,0.05,0.59,-0.28,-0.18,0.2,-0.02,0.25,0.14,-0.06,0.03,0.31,-0.14,-0.09,0.1,-0.03,0.26,0.23,-0.22,0.13,0.48,-0.11,-0.33,-0.12],[0.05,0.08,0.16,0.16,0.05,0.08,0.06,0.05,0.04,0.05,0.06,0.1,0.19,0.18,0.06,0.1,0.08,0.07,0.04,0.03,0.06,0.1,0.1,0.03,0.05,0.04,0.04,0.03,0.06,0.1,0.16,0.15,0.06,0.1,0.08,0.06,0.05],[-0.17,0.03,0.56,-1.01,2.17,2.29,0.54,-5.26,-5.26,11.22,-0.56,4.96,1.43,-0.68,0.81,6.14,-3.28,-2.63,4.42,-0.56,4.3,1.41,-0.67,0.81,5.59,-3.21,-2.55,3.94,-0.46,2.45,1.47,-1.52,1.98,4.97,-1.47,-5.36,-2.68],["0.862","0.979","0.576","0.311","0.03","0.022","0.587","<0.001","<0.001","<0.001","0.575","<0.001","0.152","0.495","0.416","<0.001","<0.001","0.008","<0.001","0.575","<0.001","0.157","0.5","0.418","<0.001","<0.001","0.011","<0.001","0.643","0.014","0.14","0.129","0.048","<0.001","0.143","<0.001","0.007"],[-0.11,-0.16,-0.23,-0.47,0.01,0.03,-0.09,-0.33,-0.31,0.43,-0.16,0.3,-0.1,-0.48,-0.07,0.4,-0.44,-0.31,0.11,-0.08,0.14,-0.06,-0.25,-0.04,0.2,-0.23,-0.16,0.05,-0.14,0.05,-0.08,-0.51,0,0.29,-0.26,-0.45,-0.21],[0.09,0.16,0.41,0.15,0.19,0.33,0.15,-0.15,-0.14,0.61,0.09,0.68,0.66,0.23,0.17,0.78,-0.11,-0.05,0.29,0.04,0.37,0.34,0.12,0.09,0.41,-0.06,-0.02,0.15,0.09,0.46,0.55,0.07,0.25,0.68,0.04,-0.21,-0.03]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th>Category<\/th>\n <th>Predictor<\/th>\n <th>Beta<\/th>\n <th>SE<\/th>\n <th>z<\/th>\n <th>p<\/th>\n <th>ci.lower<\/th>\n <th>ci.upper<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"initComplete":"function(settings, json) {\n$(this.api().table().header()).css({'background-color': ' #686A6C', 'color': 'white'});\n}","pageLength":7,"autoWidth":false,"scrollY":"300PX","columnDefs":[{"className":"dt-right","targets":[2,3,4,6,7]}],"order":[],"orderClasses":false,"lengthMenu":[7,10,25,50,100],"rowCallback":"function(row, data, displayNum, displayIndex, dataIndex) {\nvar value=data[0]; $(this.api().cell(row, 0).node()).css({'font-size':'75%'});\nvar value=data[1]; $(this.api().cell(row, 1).node()).css({'font-size':'75%'});\nvar value=data[2]; $(this.api().cell(row, 2).node()).css({'font-size':'75%'});\nvar value=data[3]; $(this.api().cell(row, 3).node()).css({'font-size':'75%'});\nvar value=data[4]; $(this.api().cell(row, 4).node()).css({'font-size':'75%'});\nvar value=data[5]; $(this.api().cell(row, 5).node()).css({'font-size':'75%'});\nvar value=data[6]; $(this.api().cell(row, 6).node()).css({'font-size':'75%'});\nvar value=data[7]; $(this.api().cell(row, 7).node()).css({'font-size':'75%'});\n}"}},"evals":["options.initComplete","options.rowCallback"],"jsHooks":[]}</script> --- # .center[Path Model] semPaths(mediationmodel, whatLabels = "std.all", structural = FALSE, edge.label.cex = 1, node.label.cex = .8, label.prop=0.9, edge.label.color = "black", rotation = 4, equalizeManifests = FALSE, optimizeLatRes = TRUE, node.width = 1.5, edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse", shapeInt = "graph", sizeMan = 4, sizeInt = 2, sizeLat = 2, curve=2, title = FALSE, intercepts = FALSE, residuals = FALSE, edge.label.position = .1, exoCov = FALSE) <img src="https://marksresearch.shinyapps.io/PictureSite/_w_c36be1e2/pathmodel.png" width="600" height="400" style="display: block; margin: auto;" /> --- # .center[Overall Findings] - Community colleges fail to graduate women within 150% time at the same rate as men - White students are more likely to graduate even when retention is used as a mediator - Institutions with higher proportions of white and male have higher graduation rates - Communities with higher proportions of white people have CCs with higher persistence rates - Community factors did not show effects on graduation - Wealthier counties graduate more student but retain less, possibly explained by transfer rates --- # .center[Discussion] - This is a very high level analysis of internal and external factors' correlations with graduation and persistence - The results suggest that there is a correlation between institutions and their communities - To echo Dr. Benjamin, we must ask ourselves who and what shapes the experiences of students and of community members? - Who produces the risk to community colleges that happen to be in communities of less privilege? - What actions can we take to address community and community college needs? - What are the interactions between communities and their colleges and how do they relate to empowering all students and community members? --- # .center[Limitations] ### This used aggregated institution and county level data ### IPEDS has retention rates reported at the 150% level ### We are working on seeing if we can apply these to our model ### We are also working on partnerships with multiple institutions to conduct large sample person level analyses --- ###Get this presentation online (write this down) - It has links to all the code - <https://rpubs.com/MarkRules/> ## Thank You! ### .center[Questions] --- ## .center[References from Paper] - 2020 community college snapshot. (2020, April 20). Higher Education Today. https://www.higheredtoday.org/2020/04/20/2020-community-college-snapshot/ - Allaire, J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2020). rmarkdown: Dynamic documents for R. https://rmarkdown.rstudio.com - Astin, A. W. (1999). Student involvement: A developmental theory for higher education. Journal of College Student Development, 40(5), 12. - Atchison, E. S., & Hosch, B. J. (2016). Benchmarking: Current availability, possible new national alternatives, and making a contribution to the discussion. New Directions for Institutional Research, 2015(166), 73–87. - Aulck, L., & West, J. (2017). Attrition and performance of community college transfers. PLoS ONE, 12(4), 1–23. - Bahr, P. R., Fagioli, L. P., Hetts, J., Hayward, C., Willett, T., Lamoree, D., Newell, M. A., Sorey, K., & Baker, R. B. (2019). Improving placement accuracy in California’s community colleges using multiple measures of high school achievement. Community College Review, 47(2), 178–211. https://doi.org/10.1177/0091552119840705 - Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. --- # .center[References (Continued)] - Boggs, G. R. (2011). Community colleges in the spotlight and under the microscope. New Directions for Community Colleges, 2011(156), 3–22. https://doi.org/10.1002/cc.462 - Breiman, L., & Cutler, A. (n.d.). Random forests: Classification description. Retrieved June 24, 2021, from https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm - Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Chapman & Hall/CRC. - Broton, K. M., Goldrick-Rab, S., & Benson, J. (2016). Working for college: The causal impacts of financial grants on undergraduate employment. Educational Evaluation and Policy Analysis, 38(3), 477–494. https://doi.org/10.3102/0162373716638440 - Broton, K. M., Miller, G. N. S., & Goldrick-Rab, S. (2020). College on the margins: Higher education professionals’ perspectives on campus basic needs insecurity. Teachers College Record, 122(3), 1–32. https://doi.org/10.1177/016146812012200307 - Brownlee, J. (2016, February 2). Your first machine learning project in r step-by-step. Machine Learning Mastery. https://machinelearningmastery.com/machine-learning-in-r-step-by-step/ --- # .center[References (Continued)] - Bryer, J., Tibert, B., & Fahey, S. (2019). ipeds: Integrated Postsecondary Education Data System (IPEDS) access. https://github.com/jbryer/ipeds - Bureau, U. C. (2021). Data. Census.Gov. https://www.census.gov/data - Campbell, S. L. (2020). Ratings in black and white: A Quantcrit examination of race and gender in teacher evaluation reform. Race, Ethnicity & Education, 1–19. - Capps, R. (2012). Supporting adult-student persistence in community colleges. Change, 44(2), 38–44. - Card, P., & Wood, J. L. (2019). The impact of faculty validation on male students of color at community college. Community College Journal of Research and Practice, 43(5), 386–389. https://doi.org/10.1080/10668926.2018.1487890 - Carnevale, A. P., & Smith, N. (2018). Balancing work and learning. Georgetown University Center on Education and the Workforce. https://repository.library.georgetown.edu/bitstream/handle/10822/1052638/Low-Income-Working-Learners-FR.pdf?sequence=1 - Clasemann, C., & Boon, R. D. (2019). Seeking patterns in swirl and drift: Retention, persistence, and transfer. New Directions for Institutional Research, 2019(184), 21–32. --- # .center[References (Continued)] - Cohen, R., & Kelly, A. M. (2019). The impact of community college science and mathematics course taking on graduation, transfer, and non-completion. Review of Higher Education, 42(2), 595–617. - Crellin, M., Kelly, P., & Prince, H. (2012). Increasing college attainment in the United States: Variations in returns to states and their residents. Change, 44(4), 35–41. - Crisp, G., & Delgado, C. (2014). The impact of developmental education on community college persistence and vertical transfer. Community College Review, 42(2), 99–117. - Davis, L. P., & Museus, S. D. (2019). What is deficit thinking? An analysis of conceptualizations of deficit thinking and implications for scholarly research. NCID Currents, 1(1). http://dx.doi.org/10.3998/currents.17387731.0001.110 - Ewert, S. (2012). Fewer diplomas for men: The influence of college experiences on the gender gap in college graduation. Journal of Higher Education, 83(6), 824–850. https://doi.org/10.1080/00221546.2012.11777271 --- # .center[References (Continued)] - Fairchild, S. D. (2017). Breaking the silence, reimagining justice: Voices of returning women students in community college. Feminist Teacher, 28(1), 1–16. - Fairlie, R. W., Hoffmann, F., & Oreopoulos, P. (2014). A community college instructor like me: Race and ethnicity interactions in the classroom. The American Economic Review, 104(8), 2567–2591. - Fike, D. S., & Fike, R. (2008). Predictors of first-year student retention in the community college. Community College Review, 36(2), 68–88. - Fong, C. J., Davis, C. W., Kim, Y., Kim, Y. W., Marriott, L., & Kim, S. (2017). Psychosocial factors and community college student success. Review of Educational Research, 87(2), 388–424. - Gantt, A. J. (2010). Graduation rates of students in technical programs at an urban community college. Community College Journal of Research and Practice, 34(3), 227–239. - Garcia, N. M., López, N., & Vélez, V. N. (2018). QuantCrit: Rectifying quantitative methods through critical race theory. Race Ethnicity and Education, 21(2), 149–157. https://doi.org/10.1080/13613324.2017.1377675 --- # .center[References (Continued)] - Garza, T., Huerta, M., García, H. A., & Lau, J. (2021). Exploring sense of belonging, socioacademic integrative moments, and learning communities related to ELS’ persistence based on reenrollment decisions in community colleges. Community College Review, 49(1), 30–51. - Gillborn, D., Warmington, P., & Demack, S. (2018). QuantCrit: Education, policy, ‘Big Data’ and principles for a critical race theory of statistics. Race Ethnicity and Education, 21(2), 158–179. https://doi.org/10.1080/13613324.2017.1377417 - Goldrick-Rab, S. (2017). The real crisis facing community colleges. Trustee Quarterly, 3. - González Canché, M. S. (2014). Localized competition in the non-resident student market. Economics of Education Review, 43, 21–35. https://doi.org/10.1016/j.econedurev.2014.09.001 - González Canché, M. S. (2017). The heterogeneous non-resident student body: Measuring the effect of out-of-state students’ home-state wealth on tuition and fee price variations. Research in Higher Education, 58(2), 141–183. https://doi.org/10.1007/s11162-016-9422-2 - González Canché, M. S. (2018). Geographical network analysis and spatial econometrics as tools to enhance our understanding of student migration patterns and benefits in the U.S. higher education network. The Review of Higher Education, 41(2), 169–216. --- # .center[References (Continued)] - González, L. M., Stein, G. L., & Huq, N. (2013). The influence of cultural identity and perceived barriers on college-going beliefs and aspirations of Latino youth in emerging immigrant communities. LatinX Journal of Behavioral Sciences, 35(1), 103–120. - Hachey, A. C., Wladis, C. W., & Conway, K. M. (2014). Do prior online course outcomes provide more information than G.P.A. alone in predicting subsequent online course grades and retention? An observational study at an urban community college. Computers & Education, 72, 59–67. - Harrell, I. L., & Bower, B. L. (2011). Student characteristics that predict persistence in community college online courses. American Journal of Distance Education, 25(3), 178–191. - Hernandez, E., Suárez-Orozco, C., Cerda, J., Osei-Twumasi, O., Corral, M., Garcia, Y., Katsiaficas, D., & Ruedas-Gracia, N. (2019). Immigrant-origin students in community college: How do they use their time on campus? Teachers College Record, 121(7), 1–48. - Hess, C., Krohn, S., Reichlin, L., Román, S., & Gault, B. (2014). Women, community college, and child care (Securing a Better Future:, pp. 22–25). Institute for Women’s Policy Research; http://www.jstor.org.libproxy.uwyo.edu/stable/resrep34545.8 --- # .center[References (Continued)] - Huntington-Klein, N., Cowan, J., & Goldhaber, D. (2017). Selection into online community college courses and their effects on persistence. Research in Higher Education, 58(3), 244–269. https://doi.org/10.1007/s11162-016-9425-z - Jackson, D. L., & Laanan, F. S. (2011). The role of community colleges in educating women in science and engineering. New Directions for Institutional Research, 2011(152), 39–49. - Jacobs, S., Atack, L., Ng, S., Haghiri-Vijeh, R., & Dell’Elce, C. (2015). Learning curve. A peer mentorship program boosts student retention. Nursing, 45(9), 19–22. - Juszkiewicz, J. (2015). Trends in Community College Enrollment and Completion Data, 2015. American Association of Community Colleges. https://www.grcc.edu/sites/default/files/docs/irp/AACC%20-%20Community%20College%20Enrollment_2015.pdf - Kim, E., & Díaz., J. (2013). Immigrant students and community colleges. ASHE Higher Education Report, 38(6), 91–107. --- # .center[References (Continued)] - Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press. - Kobrin, J. L., & Patterson, B. F. (2011). Contextual factors associated with the validity of SAT scores and high school GPA for predicting first-year college grades. Educational Assessment, 16(4), 207–226. https://doi.org/10.1080/10627197.2011.635956 - Landau, S., & Barthel, S. (2010). Recursive partitioning. In P. Peterson, E. Baker, & B. McGaw (Eds.), International Encyclopedia of Education (Third Edition) (pp. 383–389). Elsevier. https://doi.org/10.1016/B978-0-08-044894-7.01314-2 - Leo, A. (2021). Aspiration and opportunity: First-generation immigrants and refugees at community college. Community College Review, 49(4), 435–456. - Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22. - Linderman, D., & Kolenovic, Z. (2013). Moving the completion needle at community colleges: CUNY’s accelerated study in associate programs (ASAP). Change, 45(5), 43–50. https://doi.org/10.1080/00091383.2013.824350 --- # .center[References (Continued)] - Loh, W.-Y. (2011). Classification and regression trees. WIREs Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8 - López, N., Erwin, C., Binder, M., & Chavez, M. J. (2018). Making the invisible visible: Advancing quantitative methods in higher education using critical race theory and intersectionality. Race Ethnicity and Education, 21(2), 180–207. https://doi.org/10.1080/13613324.2017.1375185 - McArthur, R. C. (2005). Faculty-based advising: An important factor in community college retention. Community College Review, 32(4), 1–19. - McKinney, L., & Burridge, A. (2015). Helping or hindering? The effects of loans on community college student persistence. Research in Higher Education, 56(4), 299–324. - McKinney, L., & Novak, H. (2013). The relationship between FAFSA filing and persistence among first-year community college students. Community College Review, 41(1), 63–85. - Museus, S. D., & Chang, T.-H. (2021). The impact of campus environments on sense of belonging for first-generation college students. Journal of College Student Development, 62(3), 367–372. https://doi.org/10.1353/csd.2021.0039 --- # .center[References (Continued)] - Museus, S. D., & Quaye, S. J. (2009). Toward an intercultural perspective of racial and ethnic minority college student persistence. Review of Higher Education, 33(1), 67–94. - On-campus housing (Data Points). (2016). American Association of Community Colleges. https://www.aacc.nche.edu/wp-content/uploads/2017/09/DataPoints_No23.pdf - Ozaki, C. C., Olson, A. B., Johnston-Guerrero, M. P., & Pizzolato, J. E. (2020). Understanding persistence using a phenomenological variant of ecological systems theory. Community College Review, 48(3), 252–276. - Payne-Sturges, D. C., Tjaden, A., Caldeira, K. M., Vincent, K. B., & Arria, A. M. (2018). Student hunger on campus: Food insecurity among college students and implications for academic institutions. American Journal of Health Promotion, 32(2), 349–354. https://doi.org/10.1177/0890117117719620 - Pérez Huber, L., Vélez, V. N., & Solórzano, D. (2018). More than ‘papelitos:’ a QuantCrit counterstory to critique Latina/o degree value and occupational prestige. Race, Ethnicity & Education, 21(2), 208–230. --- # .center[References (Continued)] - Revelle, W. (2020). psych: Procedures for personality and psychological research. Northwestern University. https://CRAN.R-project.org/package=psych Version = 2.0.9 - Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. - Sandoval-Lucero, E., Maes, J. B., & Chopra, R. V. (2011). Examining the retention of nontraditional Latino(a) students in a career-based learning community. Journal of LatinX Higher Education, 10(4), 299–316. - Schneider, M., & Lu Michelle, Y. (2012). Completion matters: The high cost of low community college graduation rates. American Enterprise Institute for Public Policy Research, 2, 1–10. - Shea, P., & Bidjerano, T. (2014). Does online learning impede degree completion? A national study of community college students. Computers & Education, 75, 103–111. https://doi.org/10.1016/j.compedu.2014.02.009 - Stage, F. K., & Wells, R. S. (2014). Critical quantitative inquiry in context. New Directions for Institutional Research, 2013(158), 1–7. --- # .center[References (Continued)] - Stagg, D. R. (2017). Differences in persistence and graduation rates of LatinX students in Texas community colleges: A Texas statewide study [Dissertation, Sam Houston State University]. In Department of Educational Leadership: Vol. Doctor of Education. https://shsu-ir.tdl.org/bitstream/handle/20.500.11875/2174/STAGG-DISSERTATION-2017.pdf?sequence=1&isAllowed=y - Strayhorn, T. (2018). College students’ sense of belonging (2nd ed.). Routledge. https://doi.org/10.4324/9781315297293 - Suárez-Orozco, C., Katsiaficas, D., Birchall, O., Alcantar, C. M., Hernandez, E., Garcia, Y., Michikyan, M., Cerda, J., & Teranishi, R. T. (2015). Undocumented undergraduates on college campuses: Understanding their challenges and assets and what it takes to make an undocufriendly campus. Harvard Educational Review, 85(3), 427-463,526-530. - Suzuki, S., Morris, S. L., & Johnson, S. K. (2021). Using QuantCrit to advance an anti-racist developmental science: Applications to mixture modeling. Journal of Adolescent Research, 36(5), 535–560. - Teranishi, R. T., Suárez-Orozco, C., & Suárez-Orozco, M. (2011). Immigrants in community colleges. Future of Children, 21(1), 153–169. - The Integrated Postsecondary Education Data System. (2021). https://nces.ed.gov/ipeds/use-the-data/download-access-database --- # .center[References (Continued)] - Thernau, T., & Atkinson, B. (2019). rpart: Recursive partitioning and regression trees. https://CRAN.R-project.org/package=rpart - Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.2307/1170024 - Townsend, B. K., Donaldson, J., & Wilson, T. (2005). Marginal or monumental? Visibility of community colleges in selected higher-education journals. Community College Journal of Research and Practice, 29(2), 123–135. https://doi.org/10.1080/10668920590524265 - TRIO home page. (2021, November 3). [Programs; Offices; Reference Materials]. US Department of Education (ED). https://www2.ed.gov/about/offices/list/ope/trio/index.html - Turk, J. M., & González Canché, M. S. (2019). On-Campus housing’s impact on degree completion and upward transfer in the community college sector: A comprehensive quasi-experimental analysis. Journal of Higher Education, 90(2), 244–271. - Undergraduate Retention and Graduation Rates. (2021, May). National center for education statistics. https://nces.ed.gov/programs/coe/indicator/ctr --- # .center[References (Continued)] - Walker, K. (2020). tidycensus: Load U.S. Census boundary and attribute data as “tidyverse” and ’sf’-ready data frames. https://CRAN.R-project.org/package=tidycensus - Wickham, H., & Bryan, J. (2019). readxl: Read excel files. https://CRAN.R-project.org/package=readxl - Williams, G. J. (2009). Rattle: A data mining GUI for R. The R Journal, 1/2, 11. - Wood, J. L. (2012). Leaving the 2-year college: Predictors of black male collegian departure. Journal of Black Studies, 43(3), 303–326. - Wood, J. L. (2014). Examining academic variables affecting the persistence and attainment of black male collegians: A focus on academic performance and integration in the two-year college. Race, Ethnicity & Education, 17(5), 601–622. --- # .center[References (Continued)] - Wood, J. L., & Harris, F. (2020). Differences in self-reported GPA for students experiencing insecurities. Community College Journal of Research and Practice, 1–4. https://doi.org/10.1080/10668926.2020.1835746 - Wood, J. L., Harris III, F., & Delgado, N. (2017). ‪Struggling to survive–striving to succeed food and housing insecurities in the community college‬. Community College Equity Assessment Lab. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=niBCFGQAAAAJ&sortby=pubdate&citation_for_view=niBCFGQAAAAJ:WbkHhVStYXYC‬‬‬‬‬‬‬‬‬‬ - Work-Study Jobs Federal Student Aid. (2021). https://studentaid.gov/understand-aid/types/work-study - Xu, D., Jaggars, S. S., Fletcher, J., & Fink, J. E. (2018). Are community college transfer students “a good bet” for 4-year admissions? Comparing academic and labor-market outcomes between transfer and native 4-year college students. The Journal of Higher Education, 89(4), 478–502. https://doi.org/10.1080/00221546.2018.1434280 - Yiu, T. (2019, August 14). Understanding Random Forest. Medium. https://towardsdatascience.com/understanding-random-forest-58381e0602d2 - Yu, H., McKinney, L., & Carales, V. D. (2020). Do community college students benefit from federal work study participation? Teachers College Record, 122(1), 1–16.