Examining Beginning Teacher Retention and Mobility in Washington State
Final Report
Prepared for the Office of Superintendent of Public Instruction
Margaret L. Plecki Ana M. Elfers
Anna Van Windekens
University of Washington College of Education
Center for the Study of Teaching and Policy
May 2017
Table of Contents
ExecutiveSummary
I.BackgroundA. StudyPurposeB. RelevantLiterature
II.ResearchApproachandMethodsA. ResearchQuestionsB. MethodologyandDataSourcesC. DefinitionofTermsD. StudyLimitations
III.FindingsA. GrowthintheNumber ofNewTeachers 1)Beginning Teachers 2)FirstandSecondYearTeachers 3)SchoolsWhereBeginningTeachersWork
B. SupportsforNewTeachers–The BESTProgram 1) OverviewoftheBEST Program 2) CharacteristicsofBESTDistricts 3)BeginningTeachersinBESTDistricts 4)SchoolsWhereBESTTeachersWork
C. Retention andMobility ofBeginningTeachers Statewide andinBESTDistricts1) Retention andMobility TrendsAcrossFive‐YearTimePeriods2) Year‐by‐YearRetention andMobilityTrends
D. Statistical ModelsofBeginningTeacherRetentionandMobility StatewideandinBESTDistricts1) Introduction toAnalyses,ModelsandDatasets2) Beginning TeachersStatewideandinBESTdistricts3) Retention in2013 and 2014BEST DistrictsMeetingBEST Induction Standards
IV.ConclusionsandImplications
References
Appendices
Executive Summary
Purpose of the Study
Thepurposeofthisreportisto provideeducatorsandpolicymakersinWashingtonstatewithinformationandanalysesaboutstatewidebeginningteacherretentionandmobilityandtoinformandenhancedecisionmaking regardingteacherqualitypolicies,particularlywithrespectto supportingbeginning teachers. Weexaminethe characteristicsofbeginning teachersandlook atfactorsassociated withtheirretentionandmobility. Wealso examineaspecificsetofbeginningteacherswhobegantheir firstyearofteachingindistrictsthatreceived BEST(BeginningEducatorSupportTeam)grantsfromthestatetosupportbeginningteacherinduction.Thisreportonbeginningteachersservesasacompanionpiece toareportissuedinJanuary2017regardingretention andmobilityofallteachersinWashingtonstate.1
Methodology and Data Sources
Theprimarydatasourceisthepersonneldata fromthestate’s S‐275dataset.Thisdatasetcontainsindividualteacherleveldemographicandassignmentinformationaboutalleducatorsin Washingtonstate.WelinktheS‐275datatootherstatedatabases,includingschooldemographicdata,toformaportraitofteacherretentionandmobility. Wehave accesstomultipleyearsofdata, enablingustoconductlongitudinalanalysesthat arecomparableovertime.
Afterprovidingaportraitof thedemographiccharacteristicsofbeginning teachers, weexamine theiryear‐by‐yearand five‐year retentionandmobilityrates forthetimeperiod from2009‐10to2015‐16.Ouranalysisislimited toexamining firstyearteachersonly.Specificcomparisonsare madeatthedistrictand schoollevelforBEST‐fundeddistricts.Both thefive‐year andyear‐by‐year analyses are cohort‐based.Weusefourcategoriesto analyzebeginningteacherretentionandmobility:stayersinthesameschool,movers within district,moversout ofdistrictandexiters fromtheWashingtoneducationsystem.
Tohelpexplainbeginningteacherretentionandmobilitypatterns,we constructedmultinomiallogisticregressionmodels,asthisapproachenablesustoinvestigatetherelationshipbetweenseveraloutcomesofinterest(retentionand mobilitystatus)and anumberofdistrict,school,andindividualteachervariables.Thefocalquestionfor thisworkis“What variablesconsistentlyexplain beginningteachers’ retentionandmobility outcomesinWashingtonstate?”Thetwomainpopulations
1 SeeElfers,A.,Plecki,M.,& VanWindekens,A.(2017).ExaminingTeacherRetentionandMobilityin Washington State.Areport prepared for the Office ofthe SuperintendentofPublicInstruction bytheCenter for theStudy of Teaching and Policy,CollegeofEducation,UniversityofWashington,Seattle.
investigatedincludeallbeginning teachersstatewideand beginning teachers locatedindistrictsthat receivedBESTfundinginrecentyears.
OuranalysisalsoincludesasubsetofBEST‐fundeddistrictsin 2013and2014 that metasetof sevencriteriafor full‐fledged inductionprograms.Thefocalquestionforthisanalysisis“Howdothe retentionratesofbeginningteachers whowere locatedinBEST‐funded districtsthatmetaset ofcriteriafor full‐fledgedinductionprogramscomparetootherbeginning teachersinthestate?”
Selected Findings
Growth in the number of new teachers
Thenumberofbeginningteachers (lessthanoneyearofexperience),hasincreased steadilyfromnearly2,000in 2010‐11toover3,600 in2015‐16. NationallyandinWashington state,new teacherscomprisealargersegment ofthe populationthaninpreviousyears.Nationally,12%ofallpublicschoolteachers werein their first orsecondyearofteachingin2014‐15.InWashingtonstatein2014‐15,firstandsecondyearteacherscomprised10.7%oftheworkforce,butthe percentageroseto11.6%in2015‐16.The numberoffirstandsecondyear teachers morethandoubled inthepastsixyears,from3,387in 2010‐11to 6,918in2015‐16.
Characteristics of all beginning teachers and the schools in which they work
From2010‐11to2015‐16,thestatewide percentageofstudentsofcolorincreasedfrom39%to44%,whilethepercentageofbeginning teachersof colorincreasedfrom12%to15%.Proportionately,beginningHispanicteachers haveexperiencedthegreatest increasesince2010, representing 6.3%ofallbeginning teachersin2015‐16. TheproportionofWhiteteachersdeclinedslightly,asmostotherracialandethnic groupsincreasedor fluctuatedslightlyoverthistime.
Duringthe periodfrom2009‐10 to 2015‐16, closetohalfofall beginningteachersinWashington worked inelementary schools.Justunderhalfoftheseteachers wereworking in highpovertyschools (50%ormoreFRPL).Acrossall yearsexamined,themajority ofallbeginningteachersworked inschoolswhere Whitestudents comprisedthemajority ofstudents (50%ormore).
Variation in the number of BEST districts and the years of BEST funding
Sincethe inceptionof theBEST program,therehasbeensignificant variation inthenumberofparticipatingdistricts.Inthefirstyearof theprogram,therewere30 participatingdistricts. Thenumberofparticipatingdistricts hasrangedfromalowof7districtstoanumber10times greater(71)inagiven year.Districtsalsovariedinthenumberofyearsinwhich theyparticipatedintheBESTprogram,rangingfrom1to6years.Duringthe periodfrom2009‐10to2015‐16, morethanhalfofBEST‐fundeddistricts(53%)have receivedonlyoneyearoffunding. These
important variations in programimplementationandlevelsoffundingmakeitparticularlychallengingtoconductclearanalysesof retention andmobilityofbeginning teachersin BEST‐fundeddistricts.
Mostbeginning teachersinWashingtonstate havenotparticipated inBEST‐fundedinduction andsupport.Duringthetimeperiodfrom2009‐10to 2014‐15,thepercentof allbeginning teachers locatedinBESTdistricts rangedfrom 7%to32%ofallbeginning teachersstatewide.In2015‐16,theproportion ofbeginningteachersservedbytheBEST programincreasedto54%.
Characteristics of BEST beginning teachers
Nolargedifferenceswerenoted intheproportionofBESTteacherscomparedto allbeginning teacherswithrespectto race/ethnicityorage distribution.Noconsistentpatternofdifferences existedbetween thetwo groupswhenexaminingeducation level.However, in eachyearexamined,there wereslightlyhigherproportionsof BESTteacherswhowerefull‐time.
Characteristics of schools where BEST teachers worked
WhileonlyaboutathirdofBEST teachersworkedinhigh povertyschoolsduringthetwoearliestyears examined(2009‐10and 2010‐11),therewas a dramaticshiftbeginning in2011‐12,whenmore thanhalfanduptothree‐fourthsofBESTteachersworkedinschoolswith povertyratesof50%ormore.
Retention and mobility across five‐year time periods
Weexaminedretentionandmobilityintwo5‐yeartime periods: 2010‐11to2014‐15and 2011‐12to2015‐16.Thepercentageofstayers in BESTdistrictsishigher(50%forbothperiods)thanbeginningteachersinnon‐BESTdistricts(40%in oneperiodand43%inthe other).Alowerproportionof teachersinBEST districts movedwithintheirdistrictsfor bothperiods,andalowerproportionofteachersinBESTdistrictsmovedoutofdistrict foroneperiod,butnottheother.Finally,the proportionofexiterswasnearly identicalforBESTandnon‐BESTteachersforoneperiod(2010to2014), butsomewhatdifferentinthelaterperiod,with18%ofBESTteachersexiting, comparedto21%ofallteachersstatewide.
Year‐by‐year retention and mobility trends
Themajority ofbeginningteachers(onaverage70%)stayintheirschoolfromoneyear tothe next, 11% movewithinthedistrictand7%moveout ofdistrict.Onaverage,12% exitthe workforce inthefollowingyear.
Onaverage, beginning teachersin BEST‐fundeddistrictsareretained intheirschoolatsomewhathigher ratesthanbeginning teachersstatewide(77% vs73%).
Mobilityandexitingpatternsfor teachersinBESTdistrictsare,on average,slightlylower.
Statistical models of beginning teacher retention and mobility statewide and in BEST districts
Weconductedstatistical analysesusingmultinomiallogisticregressionswhich comparedretention and mobility outcomestoareferencegroup. Stayinginone’ssameschoolfiveyearslaterwas selectedas thereferencegroup,sincethisoutcomerepresents themajorityofbeginningteachers inourdatasets.
Thefollowingstatisticallysignificantresultsfromthemodels examiningretentionandmobility areconsistent forboth five‐year timeperiods:
Exiters. Full‐timebeginningteachersarehalfaslikelytoexit,but highschoolteachersaretwice aslikelyto exit(ascomparedtostayingin thesameschool).
Movers out of district.Highschoolbeginningteachersare morelikely tomoveoutofdistrictascomparedtoelementarybeginningteachers. Beginning teachers in districtswithlargerstudent enrollmentare slightlylesslikelytomoveoutofdistrict.AsthepercentofWhitestudentsenrolledintheschoolincreases, thereis aslightdecreasein thelikelihoodthata beginningteacherwillmoveoutofdistrict.
Movers in district.Beginning teachersinlarger enrollment districtsareslightlymorelikelytomovewithindistrict,whilebeginningteachers inWesternWashington outsideESD 121aremorelikely tomoveindistrict,ascomparedtobeginning teachers inESD121.
Statistically significant findingsfrom theanalysisoftherelationships betweenBESTparticipationforbeginning teachersandtheirsubsequentretentionandmobilityoutcomesafterfiveyearsare asfollows:
Movers out of district.Inthefive‐yeartimeperiodfor2010‐11to2014‐14,therewasa significant effectofBESTparticipationonabeginningteachers’ likelihoodofmovingtoanewdistrict.BESTparticipation was associatedwithapproximatelyhalfthelikelihoodofbeginningteachersmoving outofdistrict,suggestingthat BESTmayhaveencouraged new teachers toremainintheiroriginalschools.
Movers in district.BESTparticipationapproachedsignificanceat the p<.05levelinbothfive‐yeartimeperiods forbeginningteachersmovingwithintheiroriginaldistricts. BESTparticipationwasassociatedwithadecreasedlikelihoodofmovementwithinteachers’originalschooldistricts,suggesting
thatthesebeginningteachersweremorelikely toremainintheiroriginalschoolsascomparedto beginning teacherswhowerenot inBEST‐funded districtsin 2010‐11or 2011‐12.
Afterrunningseparatemodelsforeachofthe sixyearsof data (2009‐10to2014‐15),thesix multinomiallogisticregressionsresultedin thefollowingsignificantfindings:
In2009‐10, BESTwasfoundtobe asignificantandnegativepredictor ofbeginning teachersexitingandmoving toanewdistrictoneyearlater.Specifically, beginning teachersin BESTdistrictswereless likelytoexittheworkforce oneyearlater,ascomparedto theirpeers innon‐BESTdistricts.BESTbeginning teacherswere,on average, less thanhalfaslikelytoleave the districtone yearlater, ascomparedtotheir non‐BESTcounterparts.Inbothcases,this indicatesthatBEST beginningteachersweresignificantlymore likelytoremainintheiroriginalschools.
In2013‐14,2 BESTwasfoundtobeasignificantandpositivepredictor ofbeginning teachersmoving toadifferentschoolwithintheirdistrict.Specifically, beginning teachersin BESTdistrictsweremorethan twiceaslikelyastheirpeersinnon‐BESTdistrictstomovewithinthe districtascomparedtoremaininginone’soriginalschooloneyear later. AlthoughthissuggeststhatBESTbeginningteacherswereleavingtheir originalschools,italsodemonstratesthat theywere remainingwithin theiroriginalBEST‐fundeddistricts.GiventhatBESTwasconceptualizedasadistrict‐levelintervention fornewteachers,onecouldarguethatthisoutcomeprovidesevidenceoftheeffectivenessof theBESTprogram.
Identifying BEST districts with full‐fledged induction programs
Given thepotentialfor variationin thequality ofinductionprograms amongBESTdistricts,weconductedanadditionalsetof statisticalanalysesusingasubsetofBEST‐fundeddistrictsthatreceivedgrantsin 2013and2014.Eachdistrictthatreceiveda grant inthesetwoyears wasaskedto respondtosevenquestionsdeveloped byOSPIabouttheirteacherinductionprogram.These questionsservedasaproxyfordeterminingwhetheraBESTdistrictwasengaging in full‐fledged implementationofateacherinductionprogram.
FourteenBEST‐funded districtsverifiedthatallsevencriteria hadbeenmet.Beginningteachersinthese14districtswerecombinedtocreate“BESTsubsetdistricts,”andwerecomparedto allremainingbeginningteachers statewide in2014‐15.
2 It shouldbenotedthat 2013‐14 represents theyearwiththefewest numberof BESTdistricts.
Statistical models of BEST districts with full‐fledged induction programs
Beginningteachersin BEST‐fundeddistricts withfull‐fledgedinductionprogramshadstatisticallysignificantlylowerratesofexitingtheWashington teachingworkforce oneyearlaterthanbeginningteachersinallotherdistricts.Onaverage,approximately10percentofbeginning teachersworking inallother districtsarepredictedtoexit theteachingworkforceoneyearlater,comparedto approximately6percentoftheirpeers working in BEST‐fundeddistricts with full‐fledgedinduction programs.
Conclusions and Implications
Thisstudyfocusedonunderstandingtheretention and mobility ofbeginningteachers in Washington state.Wefoundthat there isarelationshipbetween full‐timestatusandretention,asfull‐timebeginningteachers are halfaslikelytoexitascomparedtopart‐time beginning teachers.Beginninghighschoolteachersaremorelikelytomoveoutofdistrictascomparedto beginning elementaryteachers.Asthepercentof Whitestudents enrolledintheschoolincreases,there is aslightdecreaseinthelikelihoodthatabeginningteacherwillmoveoutofdistrict.It isimportanttonotethat,contrarytothefindingsfromthemajorityof other studies intheresearch literature,thepovertylevelof theschoolwasnotaconsistentlysignificantpredictor ofbeginning teacher turnover.Furtherinvestigation intothereasonswhyfull‐timestatus,high schoolteaching,andstudentrace/ethnicityarerelatedto teacherretentionandmobility wouldbeaworthyendeavor.
Thisstudy alsoexaminedteacher retention andmobilityforall beginning teachers locatedinBEST‐funded districts.FindingsindicatethattheBESTprogramhashadsomepositiveimpactonteacherretentionand mobility.Whenlookingattwofive‐year timeperiods forteacherswho werelocatedinBEST‐funded districts(2010‐11to2014‐15 and2011‐12to2015‐16), wefindthatforthe earliertimeperiod,beginning teachersin BEST‐fundeddistrictsarestatisticallylesslikelytomoveoutofdistrictafterfiveyears.
Perhapsmoreimportantly,whenexaminingoutcomesfor beginning teachersinasubsetofBEST‐fundeddistricts thatmetstandardsfor a full‐fledgedinductionprogram,wefindthatbeginningteachersinsuchdistrictshad alower rateofexitingtheWashingtonworkforceafteroneyearthanotherbeginningteachers.Thisresultwasstatisticallysignificant.These findingssuggestthatcontinuingefforts aimedathigh‐quality,comprehensivementoring andsupportofteachersnewtotheprofession canbeeffective inreducingbeginningteacher attrition.
WhileitislikelythatsomedistrictsnotreceivinganyBESTfundinghavequalityinductionprogramsin place,currentlydatais notavailableto identifythosedistrictsstatewide.Italsoshouldbenoted that53%ofallBEST‐funded districtsreceived onlyoneyearoffunding,andmany BEST‐fundeddistricts havejustreceivedBESTfunding for thefirsttimein2015‐16.Thus,itisnotpossibleyetto assessthelong‐
termimpactofBESTfundingon a sizeableportionofteachersinBEST‐funded districts.Additionalinquiryisneededtoexaminethe impact ofhighqualityteacherinduction inWashingtonstate,perhapsincludingalldistricts thatmeetstandardsforhighqualityteacherinduction programs,irrespective ofBESTfunding.
Animportantpotentialimplication toconsider basedonthisworkisthefollowing:OnlyaboutathirdofBEST‐funded districtsin 2013‐14and2014‐15 metthestandardsforfull‐fledgedinductionprograms.Furtherinquiry isneededinordertounderstandwhythemajorityof BEST‐funded districtswere notabletoimplementallfeaturesofafully‐fledgedinductionprogram.Factorswhichmayinfluencethecapacityof districtsto provide comprehensiveinductionsupportincludethelackofstableorsufficientfundingtosupportnewteachers,alackof experiencedmentorswhocanbringtheprogramtolife forthosenewtotheprofession,andaneedtodevelopdistrict‐widecapacity tosupportnewteacher induction,evenwhenthe numbersofnewteachersfluctuate fromyear to year.
Asstatedinthisreport,thenumberoffirstandsecondyearteachers hasmorethandoubledsince2010‐11. Thisrapid increase in thenumberofteachersnewtothe profession indicatesthattheneed forefficient andeffective teacherinduction,mentoring andsupportprograms ismorepronouncedthanhasbeen inthepast.
Whilethisstudyprovidesacomprehensiveandlongitudinalanalysis ofteacherretentionandmobility, includingfactorsthat mayimpactturnover rates,wedo notexaminesomerelatedissues.Furtherinquiry isneeded intomatters suchasreasons whyteachers makeparticularcareer decisions,theimpactofschoolworkingconditionsand leadership,andtheadequacyandquality oftheteacherpreparationpipeline.
A. Study Purpose
Thepurposeofthisreportisto provideeducatorsandpolicymakersinWashingtonstatewithinformationandanalysesaboutstatewidebeginningteacherretentionandmobility,andtoinformand enhancedecisionmaking regarding teacherquality policies,particularlywithrespectto supportingbeginning teachers. Weexaminethe characteristicsofbeginning teachersandlook atfactorsassociated withtheirretentionandmobility.
Inrecentyears,Washingtonstatehasprovided somesupportfor districtstocreateandimplementprogramsthatattract,induct, andretain newteachersthroughtheBeginningEducatorSupportTeam (BEST)grantprogram.Aspart ofafocusoninductionsupportsfornewteachers,wecompareallbeginningteachersstatewidewiththoselocatedindistricts thatwerefundedthroughtheBESTprograminrecentyears.We alsoinvestigateretention outcomesforaspecificsetofdistrictsthatreceivedBESTgrantsin2013and 2014andthatmetaset ofstandardsforfull‐fledged inductionprograms.
Thisreportservesasa companionpiecetoa reportissuedinJanuary2017regardingretentionandmobility ofallteachersinWashington state.1
B. Relevant Literature
Nationalstudiesoftheteacherworkforcehaveconcludedthatwhilethenumberofteachershasgrownwithincreasesinthestudentpopulation,overallteacherretentionandmobility rateshave remainedrelativelystableovertime(Goldring,Taie,&Riddles,2014; Luekens, Lyter, &Fox,2004;Marvel,et. al.,2006;NCES,2005).The earliestSchoolsandStaffingSurvey(SASS)wasadministeredbytheNationalCenterforEducationStatistics in1987‐88,and themostrecentTeacherFollow‐upSurvey(TFS)in2012‐13.Ofpublicschoolteacherswhowereteaching inthe2011‐12schoolyear,84%remained inthesameschool,8%movedtoadifferentschool,and8%left the profession duringthe followingyear(Goldring,Taie,&Riddles,2014).Arecentstudyexaminingtenyearsof dataon teacherretentionand mobilityinWashington staterevealsfindings similartonationalstatistics.In Washington state,fromoneyear tothenext,onaverage84%of teachersareretainedintheirsame school,7% movetoanotherschoolwithin thedistrict,andonaverage,2%changedistricts.The percentageofteacherswholeavetheworkforcefromoneyeartothenextisapproximately7% (Elfers,Plecki& VanWindekens,2017).
1 SeeElfers,A.,Plecki,M.,& VanWindekens,A.(2017).ExaminingTeacherRetentionandMobilityin Washington State.Areport prepared for the Office ofthe SuperintendentofPublicInstruction bytheCenter for theStudy of Teaching and Policy,CollegeofEducation,UniversityofWashington,Seattle.
1
Fewstudiespointtowidespread nationalteachershortages.However,ithasbeenmoredifficultforschoolstofindfullyqualifiedteachersin somefieldsthaninothers,suchasmathematics,science,English learners,andspecialeducation(Cowan,Goldhaber,Hayes& Theobald,2016; Henke,etal.,1997; Podgursky,Ehlert,Lindsay, & Wan,2016).Researchershavealso noteddifficultyinfindingfullyqualifiedteachersinschoolsservinglargerproportionsofstudentsinpoverty(Engel,Jacob&Curran, 2014;Henke,etal.,1997).TheLearningPolicy Instituterecentlyreleased areportinwhichtheysuggestthat toomany teachersareleavingtheworkforce,andthis couldresult inafuture shortage(Sutcher,Darling‐Hammond,&Carver‐Thomas,2016).
Evidencesuggeststhatwhenteachersmove,theyoftentransfer tootherschoolswithin theirdistrict.Between theschoolyears2011‐2012, ananalysisofTFSdata foundthat ofamong thosewhotransferred,59%movedtoanotherschoolwithintheirdistrict,and38%movedtoaschoolinanother district (Goldring, Taie,&Riddles,2014). Thisintra‐districtmovement indicates thatcertainschoolcharacteristics(suchasworking conditionsofschools,the socio‐economicstatusandethnicityofstudents)maymotivate teacherstomove orleave,inadditiontothecommonly‐perceivedreasonsof retirement and child‐rearing(Ingersoll,2001; Luekens,Lyter & Fox,2004).
Inparticular,thecompositionofaschool’sstudentbodywith regard torace,ethnicity,andpoverty,hasbeenshowntoinfluenceteacherattritionandmobility(Guin,2004;Hanushek,Kain, &Rivkin,2001;Kelly,2004; Lankford,Loeb&Wyckoff,2002;NCES,2005; Podgursky,Ehlert,Lindsay, &Wan,2016;Shen,1997).Whilethesefactorsmayposeparticularchallenges,otherstudies havefound thattheinfluenceofstudentdemographicsonreportedturnoverandhiring problemsmaybereduced whenfactoringincertainpositive workingconditions(Loeb&Darling‐Hammond,2005).Othershavenotedadeclineintheproportionofminorityteachers in somecases, suggesting thatminorityteachers’ careershavebeenlessstablethan thoseofWhiteteachers (AlbertShankerInstitute, 2015;Ingersoll&May,2011).
Teacher turnovercannegatively affectthecohesivenessandeffectivenessofschoolcommunitiesbydisruptingeducationalprogramsandprofessional relationshipsintendedto improvestudentlearning(Borman&Dowling,2008;Bryk,Lee&Smith,1990;Ingersoll,2001; Ronfeldt,Loeb,&Wyckoff,2013).Mostagree thatsomeattritionis normaland thathealthy turnover canpromoteinnovationinschools(Macdonald,1999).HarrisandAdams(2007)foundthatteachers leavetheprofessionataboutthesameratesassimilarprofessionssuchassocialworkandnursing,andthatteachersactuallyhadalowerturnoverratethantheaveragecollegegraduate.
Oftenteachersleavefor personalreasons—thedesire for career changeorfamily pressures—butorganizationalconditionsarepotentiallypartof the story.Accordingtoaseriesofnationalstudies,lackofcollegialandadministrative
2
support,studentmisbehavior and disinterest, insufficientsalary,lack ofteacherautonomy,unreasonableteaching assignment, lackofprofessionaldevelopmentopportunities,and inadequateallocationoftime,allcontributetothe departureofteachers(Boyd,etal.,2011;Burkhauser,2016;Ingersoll,2003;Johnson,Kraft,&Papay,2012;Kelly,2004;Luekens,Lyter&Fox,2004;NCES,2003).
Teacher attrition ishigherintheearlyyearsof teaching when comparedwithmidcareerteachers(Goldring,Taie, &Riddles, 2014;Murnane,Singer&Willet, 1988,Lortie,1975;Shen,1997).In examiningtheTFAdatafrom 2011‐12,Goldring,TaieandRiddles(2014),foundthat7%ofteacherswithoneto three yearsof experience leftthe followingyear. Inthe1993 Baccalaureate andBeyondLongitudinalStudy,Henke,Zahn&Carroll(2001)foundthat82% ofnoviceteachers werestillteachingthreeyearslaterandnotethatnoneof the otheroccupationalcategories examinedprovedmore stablethanteachers.Inastudyofnoviceteacherturnover in fourMidweststates,Theobaldand Laine(2003)foundthatthepercentage ofthosewholeftteachingduringthefirstfiveyearsvariedfrom20%to32%, depending on the state.
Novicesalsoareconsiderablymorelikely tomovethan otherteachers(Goldring,Taie,&Riddles,2014; NCES,2005).Inalongitudinalstudyof new teachersinMassachusetts,JohnsonandBirkeland(2003)foundthatexperiencesattheschoolsitewerecentralininfluencing newteachers’ decisionsto stayintheirschoolsandinteaching. Theyarguethatnoviceteachers’professional successandsatisfaction istied tothe particularschoolsiteandthatworkingconditions foundtosupporttheir teachingincludecollegialinteraction,opportunities forgrowth,appropriateassignments,adequate resources andschool‐widestructurestosupportstudentlearning. Theseissues maybeparticularlyacutefor new teachersinlow‐incomeschools(Johnsonetal., 2004).Othershave foundthatthe participationin a combinationofmentoringandgroupinductionprogramsmayreducebeginningteacher turnover(Ingersoll&Strong,2011;Smith&Ingersoll, 2004),thoughthequalitative distinctions amongtheseprogramsandtheir relativecost‐effectivenessarenotalwaysclear(Ingersoll& Kralik,2004).
II. Research Approach and Methods
A. Research Questions
Theresearch questionsaddressed inthisstudyofWashington’s beginningteacherworkforce includethefollowing:
1. Whatarethe demographiccharacteristicsofbeginningteachers in Washington state?Howdothedemographiccharacteristicsofbeginning teacherswhoworkedinBEST‐fundeddistricts comparetoallbeginningteachersstatewide?
3
2. Whatdifferences, ifany,existintheretention andmobilityratesofbeginningteachers fromBEST‐fundeddistrictscomparedtothoselocatedindistrictsthatdid not receiveBESTgrants?
3. Inwhatwaysdodifferences inbeginning teacherretentionand mobilityrates exist by:(a)demographiccharacteristicsofteachers,(b)regionofthestate,(c)district andschooldemographics(e.g.,size,poverty,studentdiversity),and(d)districtsthat receivedBESTgrantscomparedtothosethat didnot?
4. Howdotheretentionratesofbeginning teacherswhowerelocatedin BEST‐fundeddistrictsthatmetaset ofcriteriafor full‐fledgedinductionprogramscomparetootherbeginning teachersinthe state?
B. Methodology and Data Sources
Weuseseveraldatasourcestoconductastatewideanalysisof theretentionandmobilitypatternsofbeginning teachers.Theprimarydatasourceisthepersonneldata fromthestate’sS‐275dataset.Thisdatasetcontains individualteacherlevel demographicandassignment informationaboutalleducatorsinWashingtonstate.Welinkthe S‐275data tootherstatedatabases,including schooldemographicdata,toformaportraitofteacherretentionandmobility.Wehave accesstomultipleyearsof data,enabling ustoconductlongitudinalanalysesthatarecomparableovertime.After providinga portrait ofthedemographiccharacteristicsof beginningteachers,weexaminetheiryear‐by‐yearandfive‐yearretention and mobilityrates forthetimeperiodfrom2009‐10 to2015‐16. Specificcomparisons aremade atthedistrictandschoollevelforBESTdistricts.Boththefive‐yearandyear‐by‐yearanalysesarecohort‐based.That is, weidentify allbeginningteachers inagivenyear,andthenexaminetheirindividual assignmentsintheworkforce inthe subsequentyear.
Wealsoconstructmultinomiallogisticregressionmodelsusing STATA14.1software to helpexplainbeginning teacher retentionand mobility,asthisapproachenablesustoinvestigatetherelationshipbetweenourdependentoutcomevariablesofinterest(retentionandmobility status)and anumberofcontinuousandcategoricalindependentvariables(e.g.,district, schoolandindividualteachercharacteristics).Thefocalquestionforthisworkis“Whatvariables consistently explainbeginningteachers’retentionandmobilityoutcomesin Washingtonstate?”
Thetwomainpopulationsinvestigated includeallbeginningteachersstatewide and beginning teacherslocated indistrictsthat receivedBESTfundinginrecentyears.Whilewewere interestedin identifyingwhich variableshelpto explainretentionandmobilityoutcomesmoregenerally,wealsohadaspecialfocusonwhether theBESTprogram,meant asaninductionsupportfornewteachers,hadasignificanteffectontheobserved outcomes.Afteranalyzingretentionand mobilityoutcomesforallbeginning teachersenrolledintheBESTprogram,wefocusedourattention
4
onaspecificsubsetofBESTdistricts.ThissubsetconsistsofBESTdistricts thatreceived funding in2013and 2014.Allfundeddistricts were assessedtodetermineiftheymet asetofsevencriteria forfull‐fledgedinduction programs,andonlythoseBESTdistrictswhoself‐reported thattheymeteachofthe sevencriteriawere includedin thesubsetforanalysis.
C. Definition of Terms
Asnotedabove,weprovideanalysesofbothfive‐yearandyear‐by‐yearretentionandmobility ratesfor allbeginning teachersstatewideandfor beginning teachersin districtsservedbythe BESTprogram. We describe the criteria for the teachers included in these analyses as follows:
Beginning Teachers were defined as those public school teachers with less than one year of experience as reported in the S-275 whose assignment is the instruction of pupils in a classroom situation and who have a designation as an elementary teacher, secondary teacher, other classroom teacher, or elementary specialist teacher.2 Other teachers serving in specialist roles (e.g., reading resource specialist, library media specialist) were not included.
BEST Teachers were defined as those public school teachers with less than one year of experience as reported in the S-275 who worked in a district that received BEST funding in particular years of interest.
To examine retention and mobility patterns, teachers are placed in one of four categories:
“Stayers” – teachers assigned to the same school(s) in the initial school year and also in the subsequent year.
“Movers in” – teachers who moved to other schools in the same district, or changed assignment (other than a classroom teacher) within the same district.
“Movers out” – teachers who moved to other districts, either as a classroom teacher or in some other role.
“Exiters” – teachers who exited the Washington education system, either temporarily or permanently.3
2 Asreported bytheOfficeoftheSuperintendentofPublicInstruction,classroomteachersare certificated instructionalstaffwith a duty root designationof 31,32,33or34.Teacherswhosefull‐time equivalent(FTE)designation waszerofor theinitialyear wereexcludedfromtheanalysis.3 Exiters mayhaveretired,re‐enteredthesysteminsubsequent years, leftWashingtonto teachin anotherstate,orcompletelyleft the profession.Itis not possibletodistinguishvoluntaryand involuntary departures. Itisnot possibleto determinewhetherteacherswholeft the state continued to beemployed asteacherselsewhere.
5
D. Study Limitations
While this study provides an analysis of beginning teacher retention and mobility, including factors that may impact turnover rates, we do not examine some related issues. First, we do not address the reasons why teachers choose to move to other schools or districts, or why they decide to leave the profession, either temporarily or permanently. Issues such as increased workload, quality of school and district leadership, support from parents and community, and personal and family factors are all known to influence teacher’s views about their careers. We also do not distinguish between teachers who choose to make a change in their assignment or location, and those who have been involuntarily transferred or did not have their contracts renewed. Additionally, we make no claims about the quality of the performance of teachers who stay in their schools, move to another school or district, or leave the profession.
This report also does not examine the extent to which the current supply of teachers is adequate to meet future staffing needs. Inquiry about the adequacy of the teacher “pipeline,” including the number, endorsements, and quality of prospective teachers, while beyond the scope of this report, is another important aspect of understanding workforce dynamics. Based on the findings in this study, inquiry into these questions is likely to yield further insight into policies than may enhance the retention and support of new teachers.
III. Findings
A. Growth in the Number of New Teachers
1) Beginning Teachers
AsseeninTable1,thenumberofbeginning teachers(less than oneyearofexperience),hasincreasedsteadily fromnearly2,000 in2010‐11toover3,600in2015‐16.Overthecourseofthe timeperiodexamined,between68%and82%ofbeginning teachersworkedfull‐time,andbetween 54% and63%heldabachelor’sdegreeonly.Asonemightexpect, onaverage, themajorityof teachers enteringthe profession (63%)are betweentheagesof20 and30,withanadditional16%over theageof 40.Duringthistimeperiod,thestatewidepercentageofstudentsofcolorincreased from39%to44%,while thepercentageofbeginning teachersofcolorincreased from12%to15%.Proportionately, beginning Hispanic teachershaveexperiencedthegreatestincrease since2010, representing6.3% ofallbeginning teachers in 2015‐16. TheproportionofWhiteteachersdeclined slightly,asmostotherracialandethnicgroupsincreasedor fluctuatedslightly overthistimeperiod. Table1providesdetails aboutbeginningteachercharacteristics.
6
Table 1: Characte ristics of All Beginning Teache rs* Statewide : from 2009‐10 to 2015‐16
2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16**
# Te ache rs ( He adcount) Teacher Gender
1,344 1,959 1,883 2,412 2,914 3, 372 3,675
Fe mal e 72% 72% 72% 73% 76% 75% 77% Mal e Full‐time/Part‐time Status
28% 28% 28% 27% 24% 25% 23%
Ful l ‐Ti me ( Te ache r FTE > .9) 68% 75% 72% 76% 77% 82% NA Not Ful l ‐Ti me ( Te ache r FTE < .9) Education
32% 25% 28% 25% 23% 18% NA
Bache l or 63% 57% 54% 54% 59% 61% 63% Maste rs and above 34% 40% 42% 43% 38% 36% 37% Uni de nti f i e d Teacher Age (in given year)
3% 3% 4% 3% 2% 3% 0%
19‐30 61% 66% 60% 63% 62% 64% 63% 31‐40 22% 19% 22% 21% 22% 21% 22% 41‐50 12% 11% 12% 12% 12% 11% 11% 51‐60 4% 4% 5% 4% 4% 4% 4% 61+ Teacher Race/Ethnicity A si an/Paci f i c Isl ande r/Nati ve
1% 1% 1% 0% 1% 1% 0%
Haw ai i an 4% 4% 4% 4% 4% 4% 4% Bl ack/Af ri can A me ri can 2% 1% 2% 2% 2% 2% 2% Hi spani c Nati ve Ame ri can/Al askan
5% 5% 6% 5% 4% 6% 6%
Nati ve 1% 1% 1% 1% 1% 1% 1% Whi te ( non‐Hi spani c) 89% 88% 85% 86% 88% 86% 85% More than one race NA*** 2% 2% 2% 2% 2% 3% Notes: *Duty root 31, 32, 33 or 34 with FTE designation >0. Beginning teachers are teachers with less than one year of experience. **Based on preliminary data which does not include some programmed fields. ***"More than one race" category was added in 2010‐11. Percentages may not add up to 100% due to rounding.
2) First and Second Year Teachers
NationallyandinWashingtonstate,newteacherscomprisea largersegmentofthepopulationthaninpreviousyears.Nationally,12%ofallpublicschoolteacherswere intheirfirst orsecondyearof teachingin2014‐15(DOE, CivilRights,2016). InWashingtonstatein 2014‐15, firstandsecondyearteachers comprised 10.7%oftheworkforce,butthepercentage roseto11.6%in2015‐16.Thenumberoffirstandsecond year teachersmorethandoubledinthepastsixyears,from3,387in2010‐11,to 6,918in2015‐16(see Table2).
7
Table 2: Trend Data for First and Second Year Teachers
Year Total Number Teachers
Number 1st and 2nd y ear Teac hers Statewide
Percent Teachers Statewide
2010-11 School Year
2011-12 School Year
2012-13 School Year
2013-14 School Year
2014-15 School Year
2015-16 School Year
56,222
55,279
55,772
56,761
58,246
59,809
3,387
3,668
4,314
5,336
6,261
6,918
6.0%
6.6%
7.7%
9.4%
10.7%
11.6%
*Teachers with less than 2.0 years of experienc e
Theinfluxofnew teachersmaybe morepronouncedinsomedistricts ascomparedtoothers,dependingonfactors suchasincreasesinstudentenrollment,changes inclasssize,andretirementsorother formsofteacherturnover. Italsoraisesquestionsregarding a district’sabilitytoprovideadequate supporttoincreasing numbersofnewteachers.Without adequatesupport,newteacherscanbecome partofthe turnovercycle.
3) Schools Where Beginning Teachers Work
Table3provides informationaboutthecharacteristicsoftheschoolswherebeginning teachersworkedduring thetimeperiodfrom2009‐10through2015‐16.Ingeneral,closetohalfofall beginning teachersinWashingtonworkedinelementary schools.Thisnumberhasincreasedslightlyinthe mostrecentthreeyears,whenmorethanhalfofallbeginning teachersworkedin elementary schools.Whenconsideringthepovertyleveloftheschoolswhereallbeginning teachers worked,weseearelativelystabletrendovertime,withjustunderhalfofthese teachersworking inthehighestpovertyschools(50%ormoreFree orReducedPriceLunchProgram(FRPL)participation).Between34%and43% ofbeginningteacherswereassignedtoschoolswherestudentsofcolorrepresentedmorethanhalfofthestudentbody.Conversely,across allyearsexamined,themajorityofallbeginning teachersworkedinschoolswhereWhitestudentscomprisedthemajorityofstudents(50%ormore).
8
Table 3: District and School Characteristics of All Beginning Teachers* Statewide: from 2009‐10 to 2015‐16
2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16**
# Te ache rs ( He adcount) 1,344 Region of the State Ce ntral Puge t Sound
1,959 1,883 2,412 2,914 3,372 3,675
(ESD 121) 44% We ste rn WA (ESDs 112,
44% 50% 49% 45% 46% 44%
113, 114, 189) 31% Easte rn WA ( ESDs 101,
31% 28% 28% 32% 30% 31%
105, 123, 171) 25% District Total Student Enrollment
25% 22% 23% 23% 24% 24%
Fe we r than 999 6% 6% 6% 6% 6% 6% 6% 1,000‐4,999 20% 20% 18% 17% 18% 18% 18% 5,000‐9,999 14% 16% 14% 15% 16% 15% 15% 10, 000‐19,999 30% 28% 29% 29% 27% 30% 26% 20, 000+ 29% School Level
30% 33% 33% 32% 32% 34%
El e me ntary 47% 44% 45% 47% 52% 54% 55% Mi ddl e School 16% 19% 19% 18% 17% 17% 15% Hi gh School 30% 30% 30% 28% 24% 24% 21% Other (e.g., PK‐8, 1‐8, 6‐1 7% Poverty of School
7% 6% 8% 6% 5% 6%
0‐25% FRPL 25% 22% 22% 20% 18% 18% 20% 26‐49% FRPL 32% 33% 31% 31% 30% 30% 30% 50‐74% FRPL 27% 27% 26% 28% 29% 29% 29% 75+% FRPL 14% 17% 20% 20% 21% 20% 19% Unidentified 2% Student Race/Ethnicity
2% 1% 1% 2% 3% 2%
0‐25% Whi te stude nts 17% 17% 19% 18% 18% 20% 20% 26‐49% Whi te stude nts 17% 18% 21% 20% 19% 22% 23% 50‐74% Whi te stude nts 34% 38% 38% 39% 41% 36% 37% 75+% Whi te stude nts 31% 25% 21% 21% 21% 19% 18% Unidentified 2% 2% 1% 1% 2% 3% 2% Notes: *Duty root 31, 32, 33 or 34 with FTE designation >0. Beginning teachers are teachers with less than one year of experience. **Based on preliminary data which does not include some programmed fields. Percentages may not add up to 100% due to rounding.
B. Supports for New Teachers – BEST Program
Attritionis commonintheearlystagesofmostoccupationsas individualslearnabouttheworkplaceanddeterminewhether ornotthejobisa goodfit.However,induction intotheteachingprofessionisparticularlyimportantbecauseteachingrequiresasignificant acquisitionofskillsin thefirstfewyearsanda highturnoverofbeginning teacherscanimpact thequalityofinstructionthatstudentsreceive.
Teacherswhoarenewertotheprofession changeschoolsatahigherratethan more experiencedteachers, often toanotherschoolwithin the district.Manythingsmaycausenewteachersto movemorethanother teachers.For some, teachingasa
9
whole(orteachingatthisschool)is notwhattheythoughtit wouldbe.Butotherforcesbeyondpersonalpreferencemaycomeintoplay.Asthestaffmemberswiththeleastseniority,theyaremorelikelytobeimpactedbyareduction inforce, changesin enrollment, orschool ordistrictorganizationalchanges.
1) Overview of the BEST program
Providinghigh‐qualityinduction andmentoringsupportisseen asa viableapproachtoimprovetheretention andperformanceofbeginning teachers.In Washington state,the Beginning EducatorSupportTeam (BEST)programpromotesstrategiesforimprovingdistrictandregionalcapacity toretainandsupportbeginning teachers.Washington hasprovidedsomestate support forbeginningteacherssince1987,initiallythroughtheTeacherAssistanceProgram(TAP).ThetotalamountoffundingforTAPremainedconstantover theyears,whilethenumberofbeginning teachersincreased,therebyreducing theamountoffundingavailableperteacher.In2009‐10,theWashingtonstatelegislatureauthorizedthedevelopmentand fundingof theBESTprogram.Accordingtothe Officeof theSuperintendentof PublicInstruction,thegoalsoftheBESTprogramareto1)closelearninggapsexperiencedbynoviceteachers whenthey entera newsystemsotheycanclosetheirstudents’ learninggaps,2)attractandretain skillfulnoviceteachersinWashington’spublicschools,and3)buildcomprehensive,coordinatedsystems ofsupportwithinschooldistrictsto sustain inductionwork.BESTprovidescompetitivegrants todistricts and regionalconsortia,and alsofundsprofessionaldevelopmentfor instructionalmentorsthroughoutthestate.Initially,theBESTprogramaimedtoprovidesupport forteachersintheirfirstthreeyears,butlaterthiswasreducedtosupportforfirstandsecondyearteachers. Inthisreport, we lookspecificallyatfirstyearteachers.
2) Characteristics of BEST districts
Sincethe inceptionof theBEST program,therehasbeensignificant variation inthenumberofparticipatingdistricts.Inthefirstyearof theprogram,therewere30 participatingdistricts. Thenumberofparticipatingdistricts hasrangedfromalowof7districtstoanumbertentimes greater(71)inthemostrecentyearofthe analysis.Figure1displaysthe variation inthenumberofdistictswithBESTgrants since2009‐10.
10
Number
of Districts
Figure 1: Number of Districts with BEST Grants Per Year: 2009‐10 to 2015‐16
80
70
60
50
40
30
20
10
0 20 09 ‐10 2010‐11 20 11 ‐12 2012‐13 20 13 ‐14 2014‐15 20 15 ‐16
Year
Districts alsovariedin thenumberofyearsin whichtheyparticipatedintheBESTprogram,rangingfrom1to6years.Thereare4districtsthat havereceived 6 yearsofBESTfunding:BattleGround, Evergreen(in ClarkCounty),Federal Way,and Grandview. Sevendistrictshave received 5 yearsofBEST funding:Cheney,Hockinson, Kalama,Toppenish,Wapato,Washougal,andZillah.Noneofthedistrictsthatreceived5or6yearsofBESTfundingreceivedanystatesupportinthe2013‐14schoolyear.Duringtheperiodfrom2009‐10 to2015‐16, morethanhalf(53%)of BEST‐fundeddistrictshavereceivedonlyoneyearof funding. WhenexamingthecharacteristicsofallBEST‐funded districts,irrespectiveofthenumberofyearsoffunding, themajorityofdistrictswereconcentrated inEastern Washington(57%)andhadenrollmentsoflessthan5,000students(68%).Only9% ofBEST‐fundeddistrictshadenrollmentsofmore than20,000students. Morethan half(52%)ofallBESTfunded‐districts weredistrictswhere 50% ormoreofstudentswerelow‐income(as measuredbyFRPLparticipation).Table4providesdetailsregarding thecharacteristicsofBEST‐fundeddistrictsbythe numberofyears ofBESTfunding,andfor allBESTdistrictsovertheperiodfrom2009‐10 to2015‐16.
11
Table 4: Characte ristics of BEST‐Funded Districts by Ye ars of BEST Funding: 2009‐10 to 2015‐16
Years of BEST Funding 6 ye ars 5 ye ars 4 ye ars 3 years 2 years 1 year TOTALS # % # % # % # % # % # % # %
# of Districts Region of the State Central Puget Sound
4 7 7 13 15 51 97
( ESD 121) Western WA (ESDs
1 25% 0 0 0 0 4 31% 5 33% 6 12% 16 16%
112, 113, 114, 189) Eastern WA ( ESDs
2 50% 3 43% 2 29% 3 23% 2 13% 14 27% 26 27%
101, 105, 123, 171) District Enrollment
1 25% 4 57% 5 71% 6 46% 8 53% 31 61% 55 57%
Fe we r than 999 0 0 1 14% 1 14% 2 15% 5 33% 22 43% 31 32% 1,000‐4,999 1 25% 6 86% 4 57% 5 38% 3 20% 16 31% 35 36% 5,000‐9,999 0 0 0 0 1 14% 2 15% 2 13% 5 10% 10 10% 10,000‐19,999 1 25% 0 0 1 14% 3 23% 2 13% 5 10% 12 12% 20,000+ District Poverty
2 50% 0 0 0 0 1 8% 3 20% 3 6% 9 9%
0‐25% FRPL 0 0 1 14% 1 14% 1 8% 2 13% 4 8% 9 9% 26‐49% FRPL 2 50% 3 43% 4 57% 4 31% 8 53% 21 41% 42 43% 50‐74% FRPL 1 25% 2 29% 2 29% 8 62% 5 33% 18 35% 36 37% 75+% FRPL Student Race/Ethnicity
1 25% 1 14% 0 0 0 0 0 0 8 16% 10 10%
0‐25% Whi te 1 25% 2 29% 0 0 1 8% 1 7% 9 18% 14 14% 26‐49% Whi te 1 25% 1 14% 0 0 4 31% 3 20% 1 2% 10 10% 50‐74% Whi te 1 25% 0 0 2 29% 2 15% 4 27% 16 31% 25 26% 75+% Whi te 1 25% 4 57% 5 71% 6 46% 7 47% 25 49% 48 49%
3) Beginning Teachers in BEST Districts
Inaddition tothesignificantvariationinthe numberandcharacteristicsofdistrictswithBESTgrants,thereisalso variationintheproportionof teacherswhowereservedbytheBESTprogramover time.The vastmajorityofbeginningteachershavenotbeenlocatedindistricts withBESTfunding,meaningthat mostbeginningteachers in Washington havenotparticipatedinBEST‐fundedteacherinductionandsupport.Theonlyexceptionisfoundin2015‐16,when slightly morethanhalfofallbeginning teachers(54%)werelocatedinBEST‐funded districts. Duringthe time periodfrom2009‐10 to2015‐16, thepercentofallbeginning teacherslocatedin BESTdistrictsrangedfrom7%to 54%ofallbeginningteachers statewide.SeeFigure2foradisplayoftheproportionofbeginningteachers locatedinBESTdistrictsfrom2009‐10 to2015‐16.
12
Table 5: Characteristics of Beginning Teachers* in BEST Districts: from 2009‐10 to 2015‐16
2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16**
Numbe r of BEST di stri cts 30 14 28 21 7 36 71 # Te ache rs ( He adcount) Teacher Gender
275 316 194 225 206 1,093 1,981
Female 68% 73% 65% 70% 77% 73% 75% Male Full‐time/Part‐time Status
32% 27% 35% 30% 23% 28% 25%
Ful l ‐Ti me (FTE > .9) 70% 80% 76% 83% 85% 85% NA N ot Ful l ‐Ti me (FTE < .9) Education
30% 20% 24% 17% 15% 16% NA
Bachel or 58% 47% 53% 54% 70% 55% 60% Maste rs and above 40% 51% 42% 42% 28% 42% 40% Uni de nti f i e d Teacher Age (in given year)
2% 2% 5% 4% 2% 3% 1%
19‐30 68% 70% 60% 63% 70% 65% 64% 31‐40 15% 17% 23% 21% 16% 22% 22% 41‐50 12% 9% 13% 12% 11% 10% 11% 51‐60 5% 4% 4% 4% 3% 3% 3% 61+ Teacher Race/Ethnicity Asian/Pacific
0% 0% 1% 0% 0% 0% 1%
Islander/Native Hawaiian 4% 4% 3% 3% 3% 5% 4% Bl ack/Afri can Ame ri can 2% 1% 2% 2% 2% 3% 2% Hi spani c Native American/Alaskan
6% 7% 5% 4% 4% 6% 7%
Native 0% 0% 1% 0% 1% 0% 1% White (non‐Hispanic) 88% 85% 87% 89% 87% 85% 83% More than one race NA*** 4% 3% 2% 2% 2% 3% Notes: *Duty root 31, 32, 33 or 34 with FTE designation >0. Beginning teachers are teachers with less than one year of experience. **Based on preliminary data which does not include some programmed fields. ***"More than one race" category was added in 2010‐11. Percentages may not add up to 100% due to rounding.
4) Schools Where BEST Teachers Work
Sincethere isgreatvariation fromoneyearto thenextintermsofthenumberandtypeofdistrictsthatreceivedBESTgrants,itisnotsurprisingtosee variationacrosstimeinthe characteristicsofschoolsinwhichBESTteacherswork.Ingeneral,closetohalfofallbeginningteachers inWashington workedinelementaryschoolsbetween2009‐10and 2015‐16. Thisnumberhasincreasedslightlyinthemostrecentthreeyears,whenmorethanhalfofall beginningteachersworkedinelementary schools.Overthissameseven‐year timespan, theschoollevelassignmentsofBESTbeginningteachershaveshownslightlymore variability,withslightlylowerproportionsworking inelementaryschoolsinthe mostrecentyearsinthedata set.However,BESTbeginning teachersmirroredthe recenttrend(since2013‐14)ofrisingproportionsof allbeginning teachersworking inelementaryschools.
14
Whenconsideringthepovertyleveloftheschoolswhereallbeginning teachers worked,weseearelativelystabletrendovertime,withjustunderhalfofthese teachersworking inthehighestpovertyschools(50%ormoreFRPLparticipation).BESTbeginning teachers,however,exhibited adifferent pattern.WhileonlyaboutathirdofBESTbeginningteachers workedinthehighestpoverty schoolsduringthetwoearliestyearsofthedata set(2009‐10and2010‐11), wesee adramaticshiftbeginning in2011‐12,whenmore thanhalfanduptothree‐fourthsofBESTbeginning teachersworkedinthehighestpovertyschools.
There isalsomorevariation inthestudentcompositionof the schoolswhereBEST beginning teachersworkedduring thesesamesevenyears—ranging froma low of32%workinginschoolswherea majorityof studentswerestudentsof colorin2009‐10to a highof74%in2013‐14.Beginningin 2013‐14and continuingthroughthe2015‐16year,the majorityof beginningBESTteachersworkedinschoolswith50%ormorestudentsofcolor,comparedtolessthanhalf(between 37‐43%)of allbeginningteachersstatewide.Table6providesdetailsabout school characteristicsforbeginningteachersinBESTdistricts(seeTable3foracomparisonwithallbeginningteachersstatewide).
15
Table 6: District and School Characteristics of Beginning Teachers* in BEST Districts: from 2009‐10 to 2015‐16
2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16**
N umbe r of BEST di stri cts 30 14 28 21 7 36 71 # Te ache rs ( He adcount) Region of the State
275 316 194 225 206 1,093 1,981
Ce ntral Puget Sound We ste rn WA ( ESDs
54% 53% 23% 32% 68% 66% 51%
112, 113, 114, 189) Easte rn WA ( ESDs 101,
24% 37% 42% 38% 6% 17% 24%
105, 123, 171) District Enrollment
22% 10% 36% 29% 26% 17% 26%
Fe we r than 999 0% 0% 8% 4% 0% 1% 2% 1,000‐4,999 20% 16% 34% 26% 11% 13% 14% 5,000‐9,999 6% 4% 0% 5% 10% 3% 7% 10,000‐19,999 17% 18% 8% 16% 41% 32% 30% 20,000+ School Level
57% 62% 50% 50% 38% 52% 47%
El e me ntary 47% 43% 39% 44% 59% 55% 55% Mi ddl e School 15% 24% 19% 26% 13% 18% 14% Hi gh School Other (e.g., PK‐8, 1‐8,
32% 31% 36% 22% 23% 22% 23%
6‐12) Poverty of School
6% 3% 6% 8% 5% 6% 6%
0‐25% FRPL 31% 23% 9% 2% 2% 14% 13% 26‐49% FRPL 42% 42% 29% 33% 20% 27% 29% 50‐74% FRPL 21% 23% 42% 48% 40% 32% 32% 75+% FRPL 7% 13% 19% 17% 36% 25% 24% Unidentified Student Race/Ethnicity
0% 0% 1% 0% 2% 3% 3%
0‐25% Whi te 12% 19% 19% 20% 37% 31% 29% 26‐49% White 20% 21% 20% 25% 37% 25% 23% 50‐74% White 30% 38% 35% 21% 19% 26% 30% 75+% Whi te 38% 22% 25% 34% 4% 15% 16% Unidentified 0% 0% 1% 0% 2% 3% 3% Notes: *Duty root 31, 32, 33 or 34 with FTE designation >0. Beginning teachers are teachers with less than one year of experience. **Based on preliminary data which does not include some programmed fields. Percentages may not add up to 100% due to rounding.
Inthenext section,we examinetheissueof theretention and mobilityofallbeginningteachersandforteacherswhoworkedinBEST districtsduringthe time periodfrom2009‐10 to2015‐16.
16
C. Retention and Mobility of Beginning Teachers Statewide and in BEST Districts
1) Retention and Mobility Trends Across Five‐Year Time Periods
Trenddata overfourtimeperiods verifiesthattherateofbeginning teacher retentionandmobility isrelativelystable,withbetween 42%and 47%retainedinthesameschool,comparedto59% ofallteachersafterafive‐year period.As canbeseen in Table7,ahigherproportion ofbeginningteachers move bothwithindistrict(16‐18%)ortoanotherdistrict(13‐19%).Thiscanbecompared to14%ofallteachersstatewide whomovewithindistrict,and7%whomoveoutofdistrict.However,therateof beginningteachersexitingtheWashington workforcehas beendecliningslightlyover time,to alow of21%inthemostrecentfive‐yearperiod,arate thatis similartoallteachersstatewide.4
Table 7: Statew ide Be ginning Teacher Rete ntion - Five Year Trend Data
5 Y ear Period
Total Beginning Teachers
Beginning Stayers in Sc hool
Beginning Movers in Dis trict
Beginning Movers out district
Beginning Exiters from WA System
Number Percent Number Perc ent Number Percent Number Percent
2003 to 2007
2005 to 2009
2010 to 2014
2011 to 2015
2,344
2,849
1,960
1,882
991 42.3%
1,331 46.7%
809 41.3%
822 43.7%
399 17.0%
463 16.3%
350 17.9%
316 16.8%
347 14.8%
361 12.7%
371 18.9%
352 18.7%
607 25.9%
694 24.4%
430 21.9%
392 20.8%
Wecomparedthefive‐year retentionandmobilityratesofbeginning teacherswho werelocatedinBEST‐fundeddistrictswiththoselocated innon‐BESTdistricts. Todrawthese comparisons,weidentified thosebeginningteachers whowerelocatedinBESTdistricts in2010‐11and 2011‐12andcalculatedtheirretentionandmobilitystatusafterfive years.Consequently, weexaminedtwo5‐yeartimeperiods:2010‐11to2014‐15and 2011‐12to 2015‐16. Whenexaminingthedescriptive statistics in Table8,we seethat thepercentageofstayersinBESTdistrictsis higher(50%forbothtimeperiods)thantherateofstayersinnon‐BESTdistricts(40%inonetimeperiod and43% in theother).Wealsonotethatalower proportion ofteachers inBESTdistrictsmovedwithin theirdistricts forbothtimeperiods,andalowerproportion of teachersinBESTdistrictsmovedoutofdistrict foronetime period,butnotthe other.Finally,theproportionof exiterswasnearlyidenticalforBESTand non‐BESTteachersinonetimeperiod(2010to2014),but somewhatdifferent in thelater timeperiod, with18%ofBESTteachersexiting,comparedto21%ofallteachersstatewide.
4 SeeElfers,A.,Plecki,M., &VanWindekens,A.(2017) Examining Teacher Retention and Mobility in Washington State for additional information about theretentionandmobilityrates of all teachers statewide.
17
Table 8: Five‐Year Retention and Mobility Rates for Beginning Teachers in BEST and Non‐BEST Districts
2010‐11 to 2014‐15 2011‐12 to 2015‐16 Non‐BEST teachers BEST teachers Non‐BEST teachers BEST teachers
# Teachers 1,644 316 1, 686 196 Numbe r Pe rce nt Numbe r Pe rce nt Numbe r Pe rce nt Numbe r Pe rce nt
Staye rs Move rs In Move rs Out Ex i te rs
649 39.5% 300 18.3% 333 20.3% 360 21.9%
158 50.0% 50 15.8% 38 12.0% 70 22.2%
724 42.9% 291 17.3% 315 18.7% 356 21.1%
98 50.0% 25 12.8% 37 18.9% 36 18.4%
Itisimportantto note thatthesearedescriptivestatistics, whichdonotcontrolforimportant variablesassociatedwithteacherretentionand mobilityoutcomes.Itisalsopossiblethatthe variations noteddonotrepresentstatisticallysignificantdifferences. InSection Dofthesefindings,we developstatisticalmodelsthatcontrolforsuchimportantpredictorswhiletestingfor statistically significant differences inretentionandmobility rates forbeginning teacherslocatedin BEST‐fundeddistricts.
WhileouranalysesofbeginningWashington teachersindicate thatmostare retainedintheirsame schoolor district after a five‐year period,thereis considerablevariation byregion.Inordertoexaminethismoreclosely,weusedtheEducational ServiceDistrict(ESD)asaproxy forregion. The nineESDsinthestatevaryconsiderablyinsizeand numberofdistricts,teachers,andstudentsserved.Table9presentsbeginning teacherretention andmobilityduringthe2010‐11to2014‐15period,and revealsregionalvariation.Duringthistimeperiod,ESDs112 and123hadthehighestratesof beginningstayers inschool,whileESDs171,105, and114hadthehighestratesof exitersfromtheWashingtoneducationsystem.
Table 9: Beginning Teacher* Retention by ESD (Five Year Trend Data: 2010‐11 to 2014‐15)
Total Pe rce nt Staye rs i n Move rs i n Mov e rs Ex i te rs f rom
ESD Total # Te ache rs
Be gi nni ng Te ache rs
Be gi nni ng Te ache rs
School Di stri ct out district WA Sys te m # % # % # % # %
101 5, 236 145 2. 8% 55 37. 9% 34 23. 4% 29 20.0% 27 18. 6%
105 3, 305 135 4.1% 57 42. 2% 12 8.9% 32 23.7% 34 25.2%
112 5, 267 174 3.3% 80 46. 0% 34 19. 5% 22 12.6% 38 21.8%
113 4, 004 134 3.3% 55 41. 0% 19 14. 2% 32 23.9% 28 20.9%
114 2, 646 72 2.7% 26 36. 1% 5 6.9% 23 31.9% 18 25.0%
121 21,273 865 4.1% 355 41. 0% 169 19. 5% 146 16.9% 193 22.3%
123 3, 582 154 4. 3% 69 44. 8% 27 17. 5% 24 15.6% 34 22. 1%
171 2, 350 58 2.5% 24 41. 4% 5 8. 6% 13 22.4% 16 27. 6%
189 8, 557 223 2. 6% 86 38. 6% 45 20. 2% 50 22.4% 42 18. 8% *Duty root 31, 32, 33 or 34 with FTE designation >0. Beginning teachers is based on an unduplicated count of teachers with less than one year of experience.
18
2) Year‐by‐Year Retention and Mobility Trends
Themajority ofbeginningteachers(onaverage70%)stayintheirschoolfromoneyear tothe next, 11% movewithinthedistrictand7%moveout ofdistrict.Onaverage,12% exitthe workforce inthefollowingyear.Inthis data,onecanseehow thenumberofbeginningteachers intheworkforcedroppedduring theeconomicrecessionperiodof 2008‐09through2011‐12.Ahigher proportion ofbeginningteachersmovedfrom oneschooltoanotherwithin theirdistrict duringtheseyears,andin2008‐09,wesee aspike inthepercentageofbeginning teacherswhoexited(18%),whichcorrespondswiththe timingofReductioninForce (RIF)noticesstatewideinthespring of2009(seeTable10).
Table 10: Statewide Beginning Teacher Year by Year Retention and Mobility Trend Data
# Be gi nni ng Te ache rs
Staye rs in School
Move rs i n Di stri ct
Move rs out Di stri ct
Ex i te rs from WA syste m
2005/06 to 2006/07
2006/07 to 2007/08
2007/08 to 2008/09
2008/09 to 2009/10
2009/10 to 2010/11
2010/11 to 2011/12
2011/12 to 2012/13
2012/13 to 2013/14
2013/14 to 2014/15
2014/15 to 2015/16
2,841
2,835
2,725
2,460
1,309
1,959
1,883
2,411
2,914
3,372
72.2%
69.6%
67.2%
64.6%
67.8%
67.4%
72.3%
76.3%
73.3%
74.9%
9.0%
9.5%
10.7%
13.7%
13.9%
12.4%
11.0%
8.0%
9.4%
7.4%
6.8%
6.7%
5.7%
3.9%
7.0%
7.2%
6.5%
7.4%
9.0%
8.7%
11.9%
14.1%
16.5%
17.8%
11.4%
13.0%
10.2%
8.3%
8.3%
9.0%
Te n Ye ar Ave rage 2,471 70.5% 10.5% 6.9% 12.1%
Weprovidealookatbeginningteacherretentionandmobility inBESTdistrictsbycomparing six‐yearaverages forthetimeperiod2009‐10to2014‐15(usingyear‐by‐yeardatasets).Onaverage,beginning teachersinBEST‐funded districtsareretainedintheirschoolatsomewhathigher ratesthan beginning teachersstatewide (77%vs73%).Mobilityandexitingpatterns forteachers inBEST‐fundeddistricts are,on average,slightly lower(see Table11).
19
Table 11: Average Retention and Mobility Rates for Beginning Teachers Six‐Year Averages (2009‐10 to 2014‐15)
Average # Average % Statewide All BEST districts
Average # Average % # Te ache rs ( He adcount) 2,154 FTE Te ache rs 1,904
Retention and Mobility (from 1 yr to next) Stayers in Sc hool 1569 72.8% Movers in Distric t 216 10.0% Movers out Distric t 162 7.5% Exiters from WA system 207 9.6%
175 160
135 77.0% 14 7.8% 11 6.2% 16 9.0%
*Duty root 31, 32, 33 or 34 with FTE designation >0. Beginning teachers are teachers with less than one year of experience.
Inordertotestthe statistical significanceof ourdescriptivefindingsaboutbeginning teacherretention and mobility,wedevelopstatisticalmodelsthatarediscussedinthenextsection.We usethedescriptivestatisticsaboutthecharacteristicsofbeginning teachers,andthe schoolsanddistrictsinwhichtheyarelocated,toinformourselection ofvariablesto includeinour statisticalanalysis.
D. Statistical Models of Beginning Teacher Retention and Mobility Statewide and in BEST Districts
Theanalysespresented inthissectionaimtoidentifyvariablessignificantly associated withthefourmutually exclusiveoutcomesofteacher retentionand mobilitydescribedearlier inthis report:stayers,moversindistrict,moversoutofdistrictandexiters.Thefocal questionis,“Whatvariables consistentlyexplainbeginning teachers’retentionand mobilityin Washington state?”
Inthisportionofthereport,wefirstprovide an introduction toouranalyses,modelsanddatasets(section1).Next,wepresenttheresults fromourmodelswhichcompareretentionandmobilityoutcomesforallBESTdistrictswithoutcomesforallbeginningteachersstatewide(section2).SinceimplementationwasvariableacrossBESTdistricts duringthe timeperiod examined, wealsofocus onasubsetofBESTdistrictsmeetingspecified criteria regardingthefeaturesoftheirinductionprograms(section3).Indoingso,wefindthatthesubsetofdistrictswhichmetcriteria for full‐fledgedinductionprogramsshowafavorableandstatistically significant difference inexitrates forbeginningBESTteachers. However,evidencefromcomparing beginning teacher retentionandmobilityinall BESTdistrictstonon‐BESTdistricts waslessclear.Weprovideasummaryatthe conclusionofsection.
20
1) Introduction to analyses, models and datasets
Weconstructedmultinomiallogisticregression modelsusingSTATA 14.1softwaretoinvestigatetherelationshipbetweenourdependent nominaloutcomevariablesofinterest(e.g.,exiting, movingoutofdistrict, movingwithindistrict, orstaying)andanumberofcontinuousandcategoricalindependent variablesthoughttoinfluenceteacherretentionand mobilityoutcomes(e.g.,district,school,andindividuallevelcharacteristics,suchasthetotalstudentenrollment atthedistrictlevel,thepercentageof students in povertyattheschoollevel,andfull‐timeteachingstatusattheindividualteacherlevel).
Inthemodels,weincludedavariableindicatingwhether ornot abeginningteacherwasworkinginaBEST‐fundeddistrict.Thisvariablewasincludedin ordertoconductpreliminary explorationintothepotential impactoftheBESTprogram.However,itisimportanttorecognize thatsignificant variationexistsinoursample. First,aspreviouslymentioned,thenumberofdistrictsfunded inagivenyearvaried from7to71districts.Second, theamountoffundinginanygivenyearfortheBESTprogramalsovaried.Consequently,districts experienceddifferentlevelsofsupportdepending ontheyear inwhichtheyparticipatedintheprogram.Third,theBEST programincludedsupportforteachersintheirfirst3 yearsat onepoint,butlatertheprogramonlyincludedteachersinthefirst2years. Becauseof thesevariations,ouranalysesarelimitedonlyto firstyearteachers.Theseimportant variationsin programimplementationandlevelsoffundingmakeitparticularlychallengingtoconductclearandmeaningfulanalysesofretention and mobility ofbeginningteachers in BESTdistricts,since theeffectscalculatedare basedonaveragesofwidelyvaryingnumbersandtypes ofdistricts.
Sincedistrictswere not randomlyselectedtoreceiveBEST fundingandthese districtsalso werenot representative of alldistrictsstatewide,webuiltandfitted regressionmodelstocontrolfor district,school,andindividuallevel characteristicsthoughttohaverelationshipsto teachers’retention and mobilityoutcomes,includingtheBESTstatusofthedistrictwhere theteacher worked,usingbothfive‐year andyear‐by‐year datasets.
Webeginwithananalysisofthe five‐yearcohort‐baseddataset for2010‐11to2014‐15. Thisdatasetincludesallteachersstatewide whowere intheirfirstyearof teachingin 2010‐11(N=1,960).Next,weconductananalysisofthe five‐yeardatasetfor 2011‐12to 2015‐16(N=1,882).Whereapplicableandappropriate, supporting evidence is provided fromayear‐by‐yeardatasetthat includessixyears ofcohortdatafor teachers in2009‐10,2010‐11,2011‐12,2012‐13, 2013‐14,and2014‐15. Theyear‐by‐yeardataset includesbeginningteachers in eachyear for atotalof13,884records. Weran separatemodelsforeachof thesixyearsofdatatoavoidissuesrelatedto duplicateteacherrecordsandtoprovideamore precise understandingofBEST effectbyyear.
21
Thecompletebeginning teachers’ multinomiallogisticregressionSTATAoutputbasedthe2010‐11to2014‐15datasetcanbe foundinAppendix A,whileoutputforthe2011‐12to2015‐16datasetcanbefoundinAppendix B.The BESTbeginningteachers’multinomiallogisticregressionSTATAoutputfortwo illustrativeyearsoftheyear‐by‐yeardatasetcanbe foundinAppendicesCandD.
2) Beginning Teachers Statewide and in BEST Districts
Beginning exiters from the WA teacher workforce
Thefirstoutcomediscussedisthe exitofbeginningteachersfromtheWashingtonworkforce. Weconductedanalysesinthe form ofmultinomiallogisticregressions,requiringeachoutcometobecomparedtoareference group.Stayinginone’ssameschoolfiveyearslater wasselectedasthereferencegroup,sincethis outcomerepresentsthemajorityofbeginningteachers inourdatasets. AsseeninTable12,lessthanhalfofthe12independentvariablesincludedinthe modelforbeginningteacherswere identifiedassignificantpredictorsoftheexitingoutcome(p<.05)inthefirstfive‐year time period (2010‐11to2014‐15).Inthemorerecentfive‐yeartimeperiod (2011‐12to2015‐16), onlyonevariable—teachingat ahighschool— wasfoundto beasignificantpredictorofexiting.Althoughdistrictlevelstudentenrollment andschoollevelproportionsofbothstudentpoverty and White students werenotfoundtobesignificant predictorsof theexitingoutcome,weretainedthesevariablesinthemodeltocontrolforvariationinthesemeasuresacrossthestate.
Table 12: Significant Predictors of Beginning Teacher Exit Outcome (as compare d to Staye rs)
Predictor significant at p <.05 More likely (>1) = + Less likely (<1) = ‐
2010‐11 to 2014‐15 (N =1,869)
2011‐12 to 2015‐16 (N =1,747)
School Enrollment Full‐time Teacher
Middle School Grade Level High School Grade Level Other School Grade Level
− − (0.55) − (1.51) + (1.67) + (2.05)
Not significant Not significant Not significant + (2.03)
Not significant In this table, coefficients are not listed if they are within plus or minus 0.02 of 1.0. Coefficients are in relative risk ratios (RRR).
Coefficientsarepresentedasrelativeriskratios(RRR),which provideameasureoftheexpectedchangein thelikelihoodofthefocaloutcomerelative to thereference groupforeveryunitchange inthe predictorvariable,holding allothervariablesconstant.Negativepredictors,orthoselessthan1.0,suggest adecreasedlikelihoodintherelativeriskofteacherswith thatcharacteristic intheoutcomegroupratherthanthereferencegroup.Forexample,ascomparedto part‐timebeginning
22
teachers, full‐timebeginningteachersinthe2010‐11to2014‐15datasetdemonstrated,onaverage,approximatelyhalftherelativerisk of exitingfromtheteacherworkforce five yearslater ascomparedtostaying intheiroriginalschools(coefficientof0.55),holdingallothervariablesconstant.Moregenerally,itcouldbesaidthatif a beginning teacherwerefull‐time, theindividual wouldbeexpectedto beastayerrather than an exiter.
Conversely, positivepredictors, or thosegreaterthan 1.0, suggestan increasedlikelihoodintherelativerisk ofteacherswith thatcharacteristicintheoutcomegroupratherthanthe referencegroup.Inthecaseofthe 2010‐11to 2014‐15dataset,eachofthethreeschool gradelevelcategorieslisted (middleschool,highschool,“other”school) wasfoundtobeassociatedwithan increasedlikelihoodofexitforbeginningteachers,as comparedtothereference categoryof teachingin anelementary school,indicating that beginning elementaryschool teachersweremorelikelytostayintheiroriginal schoolsfiveyearslaterthan theirsecondaryand“other”counterparts.Inthe more recent2011‐12to2015‐16dataset,teachinginahighschool,rather than elementaryschool,asabeginningteacherwas associatedwithtwotimestheriskofexitingtheworkforcefiveyearslater(coefficientof 2.03). Withthelatercohortofbeginning teachers,therewas nosignificant difference in likelihoodofexitfive yearslaterfor thoseworkingatthemiddleschoolor“other”schoollevels,ascomparedtothoseworking at theelementarylevel.
Themajority ofvariablesincluded inthebeginning teacherexitermodelswerenotfoundtobestatistically significant, regardless ofthetime periodexamined.Forinstance,highestdegreeheldby theteacherwasnotasignificantpredictor,and neitherwas theregionallocationoftheschoolwheretheteacherworked.Intheseexitermodels,participationin BESTwasnotfoundtobeasignificantpredictor ofwhetherabeginningteacherexitedtheteacherworkforceorremainedintheschool fiveyearslater.
Beginning movers from one district to another
Thesecond outcomediscussedisbeginningteachersmovingfrom onedistricttoanother. As withtheexiter analysisdiscussed above,staying asa teacherinone’ssameschoolfiveyearslaterwasthereference group.Beinglocatedinadifferentdistrictwasthethirdmostfrequentoutcomeobservedforbeginning teachers, representing approximately19%of teachersinboththefive‐yeartimeperiodsexamined.
AsseeninTable13,in thefirstfive‐yeardataset(2010‐11to 2014‐15),schoollevelstudentpovertywasasignificant andpositive predictorofabeginning teachermovingtoa newdistrict fiveyears later.Thisindicatesthat asschoollevelstudentpovertyrises10percent,beginning teachersare,onaverage,1.11 timesmorelikelytomovetoanewdistrict(ratherthanremain intheiroriginal school),holdingallothervariablesconstant.This effectofschoollevelpoverty onbeginningteachers’ movement tonewdistrictswas notevident inthemore recent five‐year dataset
23
(2011‐12to2015‐16). Alsointheearlierdataset,BEST participationwasassociated withapproximatelyhalfthelikelihoodofmovingout ofdistrict ascomparedtoremaininginone’ssameschool,suggesting thatBEST mayhaveencouraged newteacherstoremainintheiroriginaldistrict.
Inthe2010‐11to2014‐15dataset, twoadditionalvariableswere foundtobesignificant predictors ofbeginningteachers’movement tonewdistricts:1)theschool‐levelproportion ofWhite students,and2)teachingina highschool.AstheproportionofWhitestudentsin a schoolincreasedby10 percent, thelikelihood thatabeginning teacherwouldmovetoanewdistrictdecreasedslightly(coefficientof0.92),holdingallothervariablesconstant.Comparedtotheir elementaryschoolcounterparts,beginningteachers inhighschoolsaremorelikelyto movetoanew district,byafactorof 1.71,holdingallothervariablesconstant.Itisworthpointingoutthatthisincreased likelihoodofbeginning highschoolteachersmovingoutofdistrictwasechoedinouranalysesofallteachersstatewide(seeElfers,Plecki&VanWindekens, 2017).This higherlikelihoodofout‐of‐districtmovement forhighschoolteachers,regardlessofyearsofteachingexperience,perhaps pointstostructuralorcontextualfeaturesofhighschoolsthatpromptteachers tofind workinnewdistricts.
Onlyonevariable—districtlevel studentenrollment—wasfoundtobeasignificant negative predictor acrossbothfive‐year time periods.As enrollment increases,teachersarelesslikelytomove outofthedistrict.Thisis to beexpected,sincelargerdistrictsoftenprovidemore opportunitiesforteachers tochangeschoolswithin the district.
Table 13: Significant Predictors of Beginning Teacher Mobility Out of Distri ct Outcome (as compare d to Stayers)
Predictor significant at p <.05 More like ly (>1) = + Less likely (<1) = ‐
2010‐11 to 2014‐15 (N =1,869)
2011‐12 to 2015‐16 (N =1,747)
Total District Enroll ment School % Poverty %White Students
BEST Distri ct Hi gh School Grade Level
− + (1.11)
Not significant − (0.51)
Not significant
−
Not significant − (0.92)
Not significant + (1.71)
In this table, coefficients are not listed if they are within plus or minus 0.02 of 1.0. Coefficients are in relative risk ratios (RRR).
Beginning movers within district
Thefinaloutcomediscussedismovingas a beginningteacherto anotherschoolwithinone’soriginalschooldistrict,ascomparedtothe reference outcomeofstayingwithinone’soriginalschool.Thiswas theleast frequentlyobserved
24
outcomeforbeginningteachers, representing about17%ofallbeginning teachersstatewide.AsseeninTable14,onlytwoofthe12independent variablesincludedinthemodelforbeginningteachers were identified assignificant predictorsofthemovers‐within‐districtoutcome(p<.05)acrossbothfive‐year timeperiods: 1)district‐levelstudent enrollment,and2)regionallocation,in particular,teachingin WesternWashington outsidetheCentralPugetSound.
Althoughnotsignificantatthe p<.05level,BESTparticipation approached significanceatthe p<.05 levelinbothfive‐year timeperiods(p=.085and p=.091,depending ontheyear).BESTparticipationwasassociatedwith adecreasedlikelihoodofteachersmovingwithintheiroriginaldistrict,suggestingthatbeginning teachersin BESTdistrictsweremorelikelyto remain in theiroriginalschools,ascomparedtobeginningteacherswhowerenotinBEST‐fundeddistricts.
AsseeninTable14,differences emerged when examining results forthetwofive‐year timeperiods.Inthefirst five‐yeardataset,full‐timeteacherstatuswasassociated withlessthanhalfthelikelihoodofabeginningteachermovingto adifferentschoolwithinthesamedistrict five yearslater, as comparedtoremaininginone’soriginalschool(coefficientof0.48),holdingallothervariables constant. Inotherwords,full‐timebeginning teachersweremorelikelytostayintheiroriginal schoolsthan tomove withindistrict.Inaddition,teachingat thehighschoollevelwasassociatedwitha0.58decreasedlikelihoodofabeginning teachermovingwithin the districtascompared to stayinginone’soriginal school.Inthiscase, beginninghighschoolteachersweremorelikelytoremainintheiroriginalschool thantomovewithindistrict.
Inthelater five‐year time period(2011‐12to 2015‐16),other significantvariables were found for predicting within‐districtmovers.Twoschoollevelvariableswerefoundtobesignificant andnegativepredictorsofbeginningteacherswithin‐districtmovement:theproportionofstudentsinpovertyandtotalschoolenrollment.Inbothcases,aspoverty(ortotal schoolenrollment)increases, thelikelihoodofa beginning teachermovingwithin districtasopposedtoremainingintheiroriginalschooldecreases. This isinteresting,aswemightexpect higherlevelsofschoolpovertyto havetheoppositeeffect,whichwouldbetodrivebeginningteachersawayfromsuchaschool,perhaps toadifferentschoolwithinthesamedistrict. Itcouldbethatschoolswithhigherlevelsofpovertyalsohavemoredevelopedstructurestosupportteachersorstudents,making itmorelikelyfor teachersinsuchschoolstostay.
Highestdegreeheldand teachingin“other”schoolgradelevel configurationswerealsosignificantandnegativepredictorsofthe mover‐in‐districtoutcome.Ontheotherhand,teachinginEastern Washington asopposedtotheCentralPugetSound region was associated withapproximately twicethelikelihoodofbeginning teachersmovingwithinthedistrictfive yearslater(coefficientof 1.99).
25
Table 14: Signif i cant Pre dictors of Be ginning Te ache r Mobility Within District Outcome (as compared to Stayers)
Predictor significant at p <.05 More l i ke l y ( >1) = + Le ss l i ke l y ( <1) = ‐
2010‐11 to 2014‐15 2011‐12 to 2015‐16 (N =1,869) (N =1,747)
Total District Enrollment School % Poverty School Enrol l ment Ful l ‐ti me Te ache r
Master's or Higher Degree We stern WA (outsi de ESD 121)
Easte rn WA Re gi on Hi gh School Grade Level Other School Grade Leve l
+ Not significant Not significant − ( 0.48)
Not significant + ( 1.48)
Not significant − ( 0.58)
Not significant
+ − ( 0.92) − (0.96)
Not significant − (0.69) + ( 1.91) + ( 1.99)
Not significant − (0.50)
In this table, coefficients are not listed if they are within plus or minus 0.02 of 1.0. Coefficients are in relative risk ratios(RRR).
Tosummarize,resultsfromthestatisticalmodelsexaminingretention andmobilityindicatethefollowingaboutall beginningteachersstatewide. Theseresultsareconsistent forbothfive‐yeartime periods:
Exiters. Full‐timebeginningteachersarehalfaslikelytoexit,but highschoolteachersaretwice aslikelyto exit(ascomparedtostayingin thesameschool).
Movers out of district.Highschoolbeginning teachersaremorelikely to moveoutofdistrictascomparedto elementary beginning teachers. Beginningteachersin districts with largerstudent enrollment areslightlylesslikelytomoveoutofdistrict. AsthepercentofWhite studentsenrolled intheschoolincreases, thereisaslightdecreasein thelikelihoodthata beginning teacherwillmoveoutofdistrict.
Movers in district.Beginningteachersinlargerenrollmentdistrictsareslightlymorelikelytomovewithindistrict,whilebeginningteachers inWesternWashington outsideESD 121aremorelikely tomoveindistrict ascomparedtobeginning teachers inESD121.
Thefollowingpointssummarizethe findingsfromanalysisofthe five‐yeardatasets regardingtherelationshipsbetweenBESTparticipation forbeginningteachersandtheirsubsequentretentionandmobilityoutcomesafterfiveyears:
Movers out of district.Inthefive‐yeardataset for2010‐11to2014‐14,therewasasignificant effect ofBESTparticipationonabeginningteachers’
26
likelihoodofmovingtoanewdistrict.BESTparticipation was associatedwithapproximatelyhalfthelikelihoodofbeginningteachersmoving outofdistrict,suggestingthat BESTmayhaveencouraged new teachers toremainintheiroriginalschools.
Movers in district.Althoughnotsignificantat the p<.05level,BESTparticipationapproachedsignificanceinboth five‐yeardatasets(p=.085and p=.091)inregard tobeginning teachersmovingwithintheiroriginaldistricts.BESTparticipationwas associated withadecreased likelihoodofmovement within teachers’originalschooldistricts,suggesting that these beginning teachersweremorelikelytoremainintheiroriginal schoolsascomparedtobeginning teacherswhowerenot inBEST‐fundeddistrictsin2010‐11or 2011‐12.
Year‐by‐year analyses
WeexamineBEST‐relatedretentionandmobilityoutcomesofbeginningteachersinamorein‐depthwayusingthe year‐by‐yeardatasetfor eachof theyears from2009‐10to2014‐15.Across thesesix years,theyear‐by‐yearanalyses involved2,309beginning teachers,whowerelocatedinBEST‐fundeddistricts, and11,575whowerelocatedinnon‐BEST‐funded districts.
Afterrunningseparatemodelsforeachofthe sixyearsof data (2009‐10to2014‐15),thesix multinomiallogisticregressionsresultedin thefollowingsignificantfindings:
In2009‐10, BESTwasfoundtobe asignificantandnegativepredictor ofbeginning teachersexiting(p=.037),andalsoofmovingtoanewdistrict(p=.027)oneyearlater. Specifically,beginningteachersinBESTdistrictswerelesslikelytoexit theworkforceoneyearlater,ascomparedtotheirpeersinnon‐BESTdistricts(coefficientof 0.60).Regardingmovingtoanew district,BESTbeginning teacherswere,on average,lessthanhalfaslikelyto leavethedistrictone yearlater,ascomparedtotheirnon‐BESTcounterparts.Inbothcases,thisindicatesthatBESTbeginningteacherswere significantlymorelikely toremainintheiroriginalschools. Themultinomiallogisticregression STATAoutputonwhichthisfindingisbased canbefoundinAppendixG.
In2013‐14,5 BESTwasfoundtobeasignificantandpositivepredictor ofbeginning teachersmoving toadifferentschoolwithintheirdistrict(p=.001).Specifically, beginning teachersin BESTdistrictsweremorethan twiceaslikelyastheirpeersinnon‐BESTdistrictstomovewithinthe districtascomparedtoremaininginone’soriginalschooloneyear later(coefficientof2.16).AlthoughthissuggeststhatBESTbeginningteacherswereleaving
5 It shouldbenotedthat 2013‐14 represents theyearwiththefewest numberof BESTdistricts.
27
theiroriginalschools,italsodemonstrates thattheywere remaining withintheiroriginalBEST‐fundeddistricts.GiventhatBESTwasconceptualizedasadistrict‐levelinterventionfornewteachers, onecouldargue thatthis outcomeprovidesevidenceof the effectiveness oftheBESTprogram. ThemultinomiallogisticregressionSTATAoutputonwhichthisfinding isbasedcanbefoundinAppendixH.
Model limitations
Whilethemodelsalreadypresentedincludea variableof whetherornotteacherswerelocatedinaBEST‐fundeddistrict,theanalysesdonotaddress thecriticalquestionof thequality ofBEST programimplementation, whichwoulddirectly address theissueofvariability inbeginningteachersupportand induction programsacrossdistricts.Itis reasonabletoassumethatsuchvariationexists;thatis,someBESTdistricts mayhave a morerigorous,comprehensive,or otherwisehigherqualitysetofinduction supportsinplacethanotherBESTdistricts. Thissuggeststhatthestatisticalmodelspresented abovemaynotbe abletoconsistentlydetectsignificantvariationin retention and mobilityoutcomes.Itispossiblethatvariationmaybepresent,butmightbemaskedbydifferencesin thequalityofteacher inductionprogramimplementation acrossBESTdistricts. Inthenextsection,we provideadditionalanalysesaimed atspecificallyaddressingvariation inoutcomesofbeginningteacherslocated indistrictswithBESTinductionprogramsthatmetstandards for afull‐fledgedinduction program.
3) Retention in 2013 and 2014 BEST Districts that Met BEST Induction Standards
Analytic approach
Given thepotentialfor variationin thequality ofinductionprograms amongBESTdistricts,weconductedanadditionalsetof statisticalanalysesusingasubsetofBEST‐fundeddistrictsthatreceivedgrantsin 2013and2014.Eachdistrictthatreceiveda grant inthesetwoyears wasaskedto respondtosevenquestionsabout theirteacherinduction program. ThesequestionsweredevelopedbyOSPIas proxies for determining whethera BESTdistrictwasengaging in full‐fledgedimplementationofateacherinductionprogram.Thequestionsare informedbyBESTstandardsfor inductionand areprovidedbelow:
1. Haveyoubeendoinginductionworkfortwoormoreyears?2. Duringthis time,did you haveastakeholderteam?3. Duringthis time,did youholdanorientationfor newteachers duringthesummerthathadatleastoneday relatedtoinstruction?
4. Duringthis time,did youoffer on‐goingprofessionaldevelopmentfor newteachers?
5. Duringthis time,did yousendyourmentorsfortrainingat the Mentor
28
Academy?6. Duringthis time,did youoffer on‐goingprofessionaldevelopmentfor mentors(roundtables,in‐districttraining, etc.)?
7. Duringthis time,did youhavementorsobserve new teachersand givethem verbaland/orwritten feedback?
Districts thatresponded“yes”toallsevenquestionswereidentified as having a full‐fledged inductionprogram.Inotherwords,districtsmeetingthesecriteria aresaidtohavemetBESTinductionstandards.Atotalof14districts verifiedthatallsevencriteriaweremet.Ofthese14 districts,fourdistrictsreceivedBESTfundingin2013andwerealso fundedin 2014. Tenof the14districts received funding beginning withthe2014‐15year. Beginning teachersinthese14districtswerecombinedintoonegroupnamed“BESTsubset.”Theteachersin the BESTsubsetwerecomparedto allremainingbeginningteachersstatewide.
Model specification
Therewere771beginningteachers inthe14 districtsselected forfurtheranalysis.Becausethesamplesizeismuchsmallerthan thatforall BEST districtsexaminedinthepriorsectionofthis report,therewerelimitationsto the typesofanalysesthat werepossibleforthis subsetof BESTdistricts.Weconducted tests ofstatisticalpowertodeterminethemostappropriatemodelingapproach.The statisticalpowercalculationsindicated thattheappropriateanalysiswas tocomparetheexitrate ofbeginning teacherstotherateofstayingasa teacherinWashington, eitherin thesameoradifferentschoolordistrict.Whileotheroutcomesareofinterest(i.e.,moversin andmovers out),statisticalpower constraints limitedustoinvestigating theexiteroutcomeatthistime. Consequently, weuselogistic regressionsratherthanmultinomiallogisticregression forthis analysis.
Thefocalquestionfor thisanalysisis:“Didbeginning teachersinBEST‐fundeddistrictsthat metaset ofcriteriaforfull‐fledgedinduction programs exit the Washington education systematstatisticallysignificantly lowerrates, comparedtoallotherbeginning teachersin thestate?”We wantto emphasizethat thisis not acomparisonofBESTversusnon‐BESTdistricts,butratherananalysisthatcomparesbeginning teachersin BESTdistrictsmeeting thesevencriteria forBESTinduction standardstoteachers experiencingallotheroptions.The comparisongroupforourreferenceoutcomeofinterest(exiter)combinesthethreeremainingpotentialoutcomes mentionedaboveintoonegroup—stayers, moversin, and moversoutofdistrict.
Table15providesdescriptive,comparativeretentionand mobilitystatisticsontheoverallnumbersand proportionsofbeginningteachers statewide working in BESTsubsetdistrictsand allremaining districtsin 2014‐15. This tableprovidesevidencethatasmallerproportionofbeginningteacherswhoworkedintheBESTsubsetdistrictsexitedtheWashington teachingworkforceoneyearlater(6.9%)ascomparedtotheirpeersworking inotherdistricts(9.7%).
29
Table 15: Beginning Teacher Exiters in BEST Subset Compared to All Other Beginning Teachers: 2014‐15 to 2015‐16
Exiters only Stayers, Movers in and Movers out Combined
Total
Number Percent Number Percent Number Percent Teachers in BEST subset districts 53 6.9% 718 93.1% 771 100.0%
Teachers in all other districts 251 9.7% 2,350 90.3% 2,601 100.0%
Total teachers 304 9.0% 3,068 91.0% 3,372 100.0%
Weincludedsevenvariablesinour specifiedmodel.Thefirst three variablesare continuousdistrict‐and school‐levelvariables,whilethe remaining fourvariablecategories arebinary. TheTotalDistrictEnrollmentvariable referstothetotalnumberofstudents enrolledinthe teacher’soriginaldistrict. TheSchool%Povertyvariablereferstotheproportionof studentsenrolledinFRPL. The%WhiteStudentsvariablerefers totheproportionof Whitestudents enrolledintheteacher’soriginalschool.Ourvariableofinterest,BESTSubsetDistricts,isbinary andnoteswhetherteachers wereworking in2014‐15inasubsetdistrictornot.Thenextbinary variable,FullTimeTeacher,indicateswhether ornottheteacherhada reported teacherFTEof0.90or above.Regionindicates inwhichof threeregions theteacher workedduring the2014‐15schoolyear(PugetSound region,wherethemajorityofteacherswork,isourreferencecategory).Finally,SchoolGradeLevelindicates thetypeofschoolwheretheteachertaughtthatyear (elementary schoollevel,wherethemajorityofteacherswork,is ourreferencecategory).Thegradelevelcategorynamed“other”referstoschools thatarenot exclusively eitherelementary orsecondary(e.g., K‐12 schools).Table16provides thedefinitionsweusedtocategorize thegradelevels ofschoolswhereteachersworked.
Table 16: School Grade Level Categories and Definitions
Elementary Schools se rving any of grades K‐6 and none of grades 7‐12.
Middle Schools serving primarily any of grades 6‐9.
High Schools serving any of grades 9‐12 and none of grades K‐8.
Other Schools serving one or more of grades K‐6 AND one or more of grades 7‐12.
Findings and interpretation
Resultsfrom thelogisticregression modelarepresentedinTable17. Informationaboutmodelcoefficientsandconfidence intervalsareprovided inAppendixE.
30
Table 17: Odds Ratio Results and Significant Predictors of “Exiter” Outcome (as compared to remaining 3 outcomes combined)
2014‐15 to 2015‐16 (N =3,278)
Independent variables Odds ratio *Predictor significant
at p <.05? Total District Enrollment
School % Poverty % White Students BESTSubsetDistricts Full Time Teacher
Region (Weste rn WA) Regi on (Eastern WA)
School Grade Leve l (Middl e ) School Grade Level (High) School Grade Le vel (Other)
1 0.95 0.88 0.57 0.52 1.09 0.84 1.15 1.23 2.01
No No
Yes (p =.002) Yes (p =.005) Yes (p <.001) No No No No
Ye s (p =.004)
Fourofthevariablesincludedinourmodelwerefoundtobestatisticallysignificantpredictors atthe p<.01 levelofbeginningteachersrepresentedin the“exiter” outcomecategory.Thesefourvariablesare: theproportionof Whitestudents at theschool,thefull‐timestatusoftheteacher,if theteacher taughtinoneofthe“other” schoolgradelevelconfigurations,and,ofmostinteresttothe focusofthisanalysis,whetherornotthebeginningteacherworkedinaBESTsubsetdistrict.Wenotethatalthoughthreevariableswerenotfoundtobestatisticallysignificant(thetotalnumberofstudents enrolledinthe district,the school‐levelproportionofstudentsliving inpoverty,and theregioninwhichtheteacherworked), weretainedthesevariablesinthemodelbecausetheycontrolforimportantcontextual factorswhichvaryacross thestateandcould shapeteachers’decisions toremain in the workforce.
AscomparedtotheirpeerswhowerenotworkinginoneoftheBESTsubsetdistrictsin 2014‐15,the oddsofbeginning teachersintheBESTsubsetdistricts exitingthe Washington state workforceone yearlaterdecrease byafactorof 0.57(p=.005),holdingconstant allothervariablesinthemodel.In otherwords,beginning teachersintheBESTsubsetofdistrictsmeetingthe criteriaforBESTinductionstandardsweresignificantlymorelikelytoremain in theteachingprofession inthestate ofWashingtonthantheirpeerswhowere not insuchdistricts,controlling for otherimportant characteristics.
Toprovideamoreconcreteunderstandingof howworkinginone ofthe14BESTsubsetdistrictswaspredictedto impactthelikelihoodofexit fromtheWashingtonstateteachingworkforceoneyear later,weexploredtwotypes ofmargins:1)theaveragemarginaleffect (AME),and2)themarginaleffect atthemeans(MEM).Ingeneral,marginsprovidethepredictedchangeinlikelihoodof ourvariableof interest (exiter)when onlyone variableinthemodelischanged.Inourcase,thevariablewechangedis whetherthe teacherworkedinaBESTsubsetdistrictornot.
31
TheAMEapproachdrawsonthe empirical,recordedcovariatesof allobservationswithin the datasettopredictwhat wouldhappenifteacherswereor were notinBESTsubsetdistricts,andthen averages these probabilities. TheMEMapproachdrawsonthe meanvaluesofeachofthecovariatestopredictwhatwouldhappenifteacherswereorwerenotinsubsetdistricts. Althoughthese twoapproachesusesomewhatdifferentmethodstoapproximatetheoutcomeofinterest (exiter)—or theprobabilityofexiting based ontheinputsto thespecified model—theresultspresentedbelowarequitesimilar.
According to ourspecifiedmodel andutilizingtheaveragemarginaleffects(AME) approach,thereis a difference of 4percent(3.99)inthepredictedlikelihoodofexitbetweenbeginningteachersworkinginthe14BESTsubsetdistricts andtheirpeersworking in non‐BESTsubsetdistricts.Onaverage, approximately10percentofbeginning teachersworking in non‐BESTsubsetdistricts arepredictedtoexittheteachingworkforceoneyearlater,comparedtoapproximately6 percentoftheirpeersworkinginBEST subsetdistricts.Thisdifferenceisstatisticallysignificant at the p=.001level(see AppendixF).
Similarly,accordingtoourspecifiedmodelandutilizingthemarginaleffectatthemeans(MEM)approach,thereis a differenceof approximately3.8percent inthe predictedlikelihoodofexitbetweenbeginning teachersworking inthe14BEST subsetdistrictsand theirpeersworking in non‐BESTsubsetdistricts.Onaverage,approximately9.4percentofbeginning teachersworking innon‐BESTsubsetdistrictsarepredictedtoexit theteachingworkforceone year later,comparedtoapproximately5.6percentof theirpeersworkinginBEST subset districts.Thesepredictedvaluesarestatisticallysignificant at the p=.001level(see AppendixG).
Tosummarizeourspecifiedmodel andcalculationsoftwotypes ofmargins,we foundthat beginning teachersin BEST‐fundeddistrictsthatmet standards for afull‐fledged inductionprogramhadstatisticallysignificantlylower ratesofexiting the Washington teaching workforce oneyearlaterthanbeginningteachersinother districts.
IV. Conclusions and Implications
Thisstudyfocusedonunderstandingtheretention and mobility ofbeginningteachersinWashington state.Wefoundthat forallbeginning teachers,there is a relationshipbetween full‐timestatusandretention, asfull‐timebeginningteachersarehalfaslikelytoexitascomparedtopart‐timebeginningteachers.Wealsofoundthathighschoolbeginningteachersaremorelikelytomoveout ofdistrict as comparedtoelementary beginning teachers.Beginningteachers indistrictswithlargerstudent enrollmentareslightlylesslikelytomoveout ofdistrict.Asthepercentof Whitestudents enrolledintheschoolincreases,there is aslightdecreaseinthelikelihoodthatabeginningteacherwillmoveoutofdistrict.It isimportanttonotethat,contrarytothefindingsfromthemajorityof other studies intheresearch
32
literature,thepovertylevelof theschoolwasnotaconsistentlysignificantpredictor ofbeginning teacher turnover.Furtherinvestigation intothereasonswhyfull‐timestatus,high schoolteaching,andstudentrace/ethnicityarerelatedto teacherretentionandmobility wouldbeaworthyendeavor.
Thisstudy alsoexaminedteacher retention andmobilityforall beginning teachers locatedinBEST‐funded districts.FindingsindicatethattheBESTprogramhashadsomepositiveimpactonteacherretentionand mobility.Whenlookingattwofive‐year timeperiods forteacherswho werelocatedinBEST‐funded districts(2010‐11to2014‐15 and2011‐12to2015‐16), wefindthatforthe earliertimeperiod,beginning teachersin BEST‐fundeddistrictsarestatisticallylesslikelytomoveoutofdistrictafterfiveyears.
Perhapsmoreimportantly,whenexaminingoutcomesfor beginning teachersinasubsetofBEST‐fundeddistricts thatmetstandardsfor a full‐fledgedinductionprogram,wefindthatbeginningteachersinsuchdistrictshad alower rateofexitingtheWashingtonworkforceafteroneyearthanotherbeginningteachers.Thisresultwasstatisticallysignificant.These findingssuggestthatcontinuingefforts aimedathigh‐quality,comprehensivementoring andsupportofteachersnewtotheprofession canbeeffective inreducingbeginningteacher attrition.
WhileitislikelythatsomedistrictsnotreceivinganyBESTfundinghavequalityinductionprogramsin place,currentlydatais notavailableto identifythosedistrictsstatewide.Italsoshouldbenoted that53%ofallBEST‐funded districtsreceived onlyoneyearoffunding,andmany BEST‐fundeddistricts havejustreceivedBESTfunding for thefirsttimein2015‐16.Thus,itisnotpossibleyetto assessthelong‐termimpactofBESTfundingon a sizeableportionofteachersinBEST‐funded districts.Additionalinquiryisneededtoexaminethe impact ofhighqualityteacherinduction inWashingtonstate,perhapsincludingalldistricts thatmeetstandardsforhighqualityteacherinduction programs,irrespective ofBESTfunding.
Animportantpotentialimplication toconsider basedonthisworkisthefollowing:OnlyaboutathirdofBEST‐funded districtsin 2013‐14and2014‐15 metthestandardsforfull‐fledgedinductionprograms described earlier.Furtherinquiry isneededinordertounderstandwhythemajorityofBEST‐fundeddistrictswere notabletoimplement allfeaturesofafully‐fledgedinductionprogram.Factorswhichmayinfluencethecapacityofdistrictstoprovidecomprehensiveinductionsupportincludethe lackofstableorsufficientfundingto supportnew teachers,alackofexperiencedmentors whocanbringtheprogramtolife forthose newtotheprofession, andaneed todevelopdistrict‐widecapacitytosupportnewteacherinduction, evenwhenthenumbers ofnewteachersfluctuatefrom yeartoyear.
Asstatedinthisreport,thenumberoffirstandsecondyearteachers hasmorethandoubledsince2010‐11. Thisrapid increase in thenumberofteachersnewtothe profession indicatesthattheneed forefficient andeffective teacherinduction,mentoring andsupportprograms ismorepronouncedthanhasbeen inthepast.
33
Whilethisstudyprovidesacomprehensiveandlongitudinalanalysis ofteacherretentionandmobility, includingfactorsthat mayimpactturnover rates,wedo notexaminesomerelatedissues.Furtherinquiry isneeded intomatters suchasreasons whyteachers makeparticularcareer decisions,theimpactofschoolworkingconditionsand leadership,andtheadequacyandquality oftheteacherpreparationpipeline.
34
References
AlbertShankerInstitute(2015). The state of teacher diversity in American education.Washington,DC:Author.
Borman,G.,&Dowling, N.(2008). Teacherattritionandretention:Ameta‐analyticandnarrativereviewoftheresearch. Review of Educational Research, 78(3),367‐409.
Boyd,D.J.,Grossman,P.L.,Ing,M.,Lankford, H.,Loeb,S., &Wyckoff, J.(2011).Theinfluenceofschooladministrators onteacherretentiondecisions. American Educational Research Journal, 48,303–333.
Burkhauser,S.(2016).Howmuchdoschoolprincipalsmatterwhen itcomestoteacherworkingconditions. Educational Evaluation and Policy Analysis,online first, DOI:10.3102/0162373716668028,0:1‐21.
Byrk,A.S.,Lee,V.E.,& Smith,J.B.(1990).Highschoolorganization anditseffecton teachers andstudents: Aninterpretive summaryoftheresearch.InW.J.Clune &J.F.Witte(Eds.), Choice and control in American education: Volume 1. The Theory of choice and control in American education.Philadelphia:FalmerPress.
Cowan,J.,Goldhaber,D.,Hayes,K., &Theobald,R.(2016).Missing elementsinthediscussion ofteachershortages. Educational Researcher, 45(8),460‐462.
Elfers,A., Plecki,M.,&VanWindekens,A.(2017). Examining Teacher Retention and Mobility in Washington State.AreportpreparedfortheOffice oftheSuperintendentof PublicInstructionbytheCenterfortheStudyofTeachingandPolicy, CollegeofEducation,Universityof Washington,Seattle
Engel,M.,Jacob,B.,andCurran, C.(2014).New evidenceonteacherlaborsupply.American Educational Research Journal,51(1),36‐72.
Goldring,R.,Taie,S.,& Riddles,M.(2014). Teacher Attrition and Mobility: Results from the 2012‐13 Teacher Follow‐up Survey (NCES2014‐077).U.S.DepartmentofEducation.Washington,DC:NationalCenterforEducationStatistics.
Guin,K.(2004).Chronicteacher turnover inurbanelementaryschools. Education Policy Analysis Archives, 12(24).RetrievedFebruary13,2005,fromhttp://epaa.asu.edu/epaa/v12n42/
Hanushek,E.A.,Kain,J.F.,&Rivkin,S.G.(2001). Why Public Schools Lose Teachers.Cambridge, MA:NationalBureauofEconomicResearch.
35
Harris.D.N. &Adams,S. J.(2007).Understandingthelevelandcausesofteacherturnover:Acomparisonwithotherprofessions. Economics of Education Review,26,325‐337.
Henke,R.R.,Choy,S.P.,Chen, X.,Geis,S.,Alt,M.N.,&Broughman,S.P.(1997).America’s Teachers: Profile of a Profession.WashingtonDC:U.S.DepartmentofEducation, NationalCenter forEducationStatistics.
Henke,R.R.,Zahn,L.& Carroll,C.D. (2001). Attrition of new teachers among recent college graduates: Comparing occupational stability among 1992‐93 graduates who taught and those who worked in other occupations (StatisticalAnalysis ReportNCES2001‐189).Washington, DC:U.S. DepartmentofEducation,NationalCenter forEducationStatistics.
Ingersoll,R.M.(2001).Teacher turnoverandteachershortages:Anorganizationalanalysis. American Educational Research Journal, 38(3),499‐534.
Ingersoll,R.M.(2003). Who controls teachers’ work?: Power and accountability in America’s schools. Cambridge,MA:Harvard UniversityPress.
Ingersoll,R.M.&May, H.(2011). Recruitment, retention, and the minority teacher shortage.Philadelphia,PA:Consortium forPolicyResearch,University ofPennsylvaniaandCenterforEducationalResearchin theInterestofUnderservedStudents, University ofCalifornia,Santa Cruz.
Ingersoll,R.,&Strong, M.(2011).Theimpact ofinductionand mentoringprogramsforbeginningteachers:Acriticalreviewoftheresearch. Review of Educational Research, 81(2),201‐233.
Ingersoll,R.,&Kralik,J. M. (2004).Theimpact ofmentoring onteacherretention:Whattheresearchsays. Research Review, Education Commission of the States,1‐23.RetrievedonFebruary13, 2005,from www.ecs.org/clearinghouse/50/36/5036.htm
Johnson,S.,&Birkeland, S.(2003).Pursuinga “senseof success”:Newteachers explain theircareerdecisions. American Educational Research Journal, 40(3),581‐617.
Johnson,S.M.,Kraft,M. A.,&Papay, J.P.(2012).Howcontextmatters inhigh‐needschools:Theeffectsofteachers’workingconditionson theirprofessionalsatisfaction andtheirstudents’achievement. Teachers College Record, 114(10),1‐39.
Johnson,S.M.,Kardos, S.M.,Kauffman,D.,Liu,E.,&Donaldson,M.L.(2004).Thesupportgap:newteachers’early experiences inhigh‐incomeandlow‐income
36
schools. Education Policy Analysis Archives, 12(61).RetrievedOctober28,2004fromhttp://epaa.asu.edu/epaa/v12n61/.
Kelly,S.(2004).Aneventhistory analysisof teacherattrition:Salary,teachertracking,andsociallydisadvantagedschools. The Journal of Experimental Education, 72(3),195‐220.
Lankford,H.,Loeb,S.&Wycoff, J.(2002).Teachersorting and theplightofurban schools:Adescriptiveanalysis. Educational Evaluation and Policy Analysis, 24(1),37‐62.
Loeb,S.&Darling‐Hammond,L.(2005).Howteachingconditions predictteacherturnoverinCaliforniaschools. Peabody Journal of Education. 80(3),44‐70.
Lortie,D.C. (1975). Schoolteacher: A Sociological Study.Chicago:UniversityofChicagoPress.
Luekens,M. T.,Lyter,D. M.&Fox,E.E.(2004). Teacher Attrition and Mobility: Results from the Teacher Follow up Survey, 2000‐01 (NCES 2004‐301).U.S.DepartmentofEducation,NationalCenterfor Educational Statistics. Washington,DC:U.S. GovernmentPrintingOffice.
Macdonald,D.(1999).Teacher attrition:A reviewofliterature. Teaching and Teacher Education,15,835‐848.
Marvel,J.,Lyter,D.M., Petrola, P., Strizek,G. A.&Morton,B.A.(2006). Teacher attrition and mobility: Results from the 2004‐05 Teacher Follow up Survey(NCES2007=307).U. S.Department ofEducation,NationalCenterforEducationStatistics.Washington,DC:U.S.GovernmentPrinting Office.
Murnane,R.,Singer,J. D.,&Willett,J.B.(1988).TheCareer PathsofTeachers:Implicationsforteachersupply andmethodologicallessonsfor research.Educational Researcher, 17(6),22‐30.
NationalCenter forEducationStatistics(NCES).(2003). Teacher Attrition and Mobility: Results from the Teacher Follow‐up Survey, 2000‐01.Jessup,MD:U.S.DepartmentofEducation.
NationalCenter forEducationStatistics(NCES).(2005). The Condition of Education 2005,NCES2005‐094, WashingtonDC:U.S.Government PrintingOffice.
Podgursky, M.,Ehlert,M.,Lindsay, J.,&Wan,Y.(2016).Anexaminationofthemovement ofeducatorswithin and acrossthreeMidwest Regionstates.
Ronfeldt,M.,Loeb,S.,&Wyckoff,J.(2013).Howteacher turnoverharmsstudentachievement. American Educational Research Journal,50(4),4–36.
37
Shen,J.(1997).Teacherretention andattrition inpublicschools:Evidence fromSASS91. The Journal of Educational Research, 91(2),81‐88.
Smith,T.M.,&Ingersoll,R. M.(2004).Whataretheeffects ofinductionand mentoring onbeginning teacher turnover? American Educational Research Journal, 41(3),681‐714.
Sutcher,L.,Darling‐Hammond,L.,Carver‐Thomas,D.(2016).Acomingcrisisin thesupply,demand,andshortagesin theUW.Palo Alto,CA: Learning Policy Institute.
Theobald,N.D.,&Laine,S.W.M.(2003).Theimpactof teacher turnover on teacher quality:Findings fromfourstates.InM.L.Plecki&D.H.Monk(Eds.), School Finance and Teacher Quality: Exploring the Connections (pp.33‐54).Larchmont,NY:EyeonEducation.
U.S.DepartmentofEducation(2016).Thestateofracial diversityin theeducatorworkforce. Policyand ProgramStudiesService,Officeof Planning,Evaluation andPolicy Development.Washington,DC:Author.
38
Appendices
39
-----------------------------------------------------------------------------------
High | Other |
| _cons |
Appendix A: Beginning teachers’ multinomial logistic regression STATA output for the Five‐Year Period 2010‐11 to 2014‐15 mlogit ndYearMOB TotalEnroll_by100 stPoverty_by10 stWhite_by10 stYearEnroll_by50 BEST FTteacher i.HighestDegree i.region i.SchlGradeLevel if Exp<1, rr base(4)
Multinomial logistic regression Number of obs = 1,869 LR chi2(36) = 172.75 Prob > chi2 = 0.0000
Log likelihood = -2383.7011 Pseudo R2 = 0.0350
ndYearMOB | RRR Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- Exit TotalEnroll_by100 | 1.000465 .0006094 0.76 0.445 .9992714 1.00166
stPoverty_by10 | 1.014519 .0414194 0.35 0.724 .9365018 1.099036 stWhite_by10 | .9573922 .0379106 -1.10 0.272 .885899 1.034655
stYearEnroll_by50 | .9810246 .0087239 -2.15 0.031 .9640742 .9982731 BEST | .8887739 .1475043 -0.71 0.477 .6419816 1.230439
FTteacher | .5495029 .0797903 -4.12 0.000 .4134011 .7304127 |
HighestDegree | MastersAndAbove | .9714987 .1268649 -0.22 0.825 .7521189 1.254868
| region |
Western WA | 1.026547 .1886748 0.14 0.887 .7160308 1.471722 Eastern WA | 1.031867 .202403 0.16 0.873 .7025198 1.515615
| SchlGradeLevel |
Middle | 1.509534 .260555 2.39 0.017 1.076268 2.117218 1.666237 .327991 2.59 0.009 1.132874 2.45071 2.053513 .5192258 2.85 0.004 1.251046 3.370713
.9259161 .3833946 -0.19 0.853 .4112578 2.084631 ------------------+---------------------------------------------------------------- MOUT TotalEnroll by100 | .9982849 .0006899 -2.48 0.013 .9969337 .999638
stPoverty_by10 | 1.112775 .0490008 2.43 0.015 1.020763 1.213081 stWhite_by10 | 1.064482 .0448433 1.48 0.138 .9801209 1.156103
stYearEnroll_by50 | .9898 .0093515 -1.09 0.278 .97164 1.008299 | .5094516 .1026106 -3.35 0.001 .3432885 .7560432
FTteacher | .8805276 .1404495 -0.80 0.425 .6441265 1.20369 |
HighestDegree | MastersAndAbove | 1.133093 .153885 0.92 0.358 .8682891 1.478656
| region |
Western WA | .9599843 .1788698 -0.22 0.827 .6662901 1.383136 Eastern WA | .7704092 .155849 -1.29 0.197 .5182373 1.145287
| SchlGradeLevel |
Middle | 1.173661 .2128095 0.88 0.377 .8226247 1.674493 High | 1.382724 .2813193 1.59 0.111 .9280177 2.060227 Other | .9557703 .2854044 -0.15 0.880 .5323223 1.71606
| _cons | .3193178 .146347 -2.49 0.013 .1300491 .7840408
------------------+---------------------------------------------------------------- MVIN TotalEnroll_by100 | 1.003683 .0006182 5.97 0.000 1.002472 1.004895
stPoverty_by10 | .997246 .0433431 -0.06 0.949 .9158128 1.08592 stWhite_by10 | .9948869 .0438398 -0.12 0.907 .9125685 1.084631
stYearEnroll_by50 | .9866798 .0109634 -1.21 0.227 .9654243 1.008403 BEST | .7256034 .1351985 -1.72 0.085 .5036151 1.045442
FTteacher | .4784454 .0749579 -4.71 0.000 .3519451 .650414 |
HighestDegree | MastersAndAbove | .90906 .1290005 -0.67 0.502 .6883391 1.200557
|
40
-----------------------------------------------------------------------------------
| region |
| 1.484442 .3035453 1.93 0.053 .9942737 2.21626 Eastern WA | 1.347474 .3010918 1.33 0.182 .8696013 2.087952
| SchlGradeLevel |
Middle | .8584752 .1611692 -0.81 0.416 .5941884 1.240313 High | .5752229 .1398726 -2.27 0.023 .3571543 .9264381 Other | .727954 .2252379 -1.03 0.305 .3969463 1.334984
| _cons | .546887 .244011 -1.35 0.176 .2280904 1.311258
------------------+---------------------------------------------------------------- STAY | (base outcome)
41
-----------------------------------------------------------------------------------
Appendix B: Beginning teachers’ multinomial logistic regression STATA output for the Five‐Year Period 2011‐12 to 2015‐16
mlogit ndYearMOB TotalEnroll_by100 stPoverty_by10 stWhite_by10 stYearEnroll_by50 BEST FTteacher i.HighestDegree i.region i.SchlGradeLevel if Exp<1, rr base(5)
Multinomial logistic regression Number of obs = 1,747 LR chi2(36) = 131.86 Prob > chi2 = 0.0000
Log likelihood = -2217.577 Pseudo R2 = 0.0289
ndYearMOB | RRR Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- Exit TotalEnroll_by100 | .9994915 .0005702 -0.89 0.373 .9983745 1.00061
stPoverty_by10 | .9556934 .0386181 -1.12 0.262 .8829231 1.034462 stWhite_by10 | .9656994 .0410165 -0.82 0.411 .8885638 1.049531
stYearEnroll_by50 | .9855491 .009143 -1.57 0.117 .967791 1.003633 BEST | .7882194 .1731273 -1.08 0.279 .512491 1.212294
FTteacher | .920253 .1343461 -0.57 0.569 .6912607 1.225103 |
HighestDegree | MastersAndAbove | .9931404 .1310122 -0.05 0.958 .7668715 1.286171
| region |
Western WA | 1.016569 .1895871 0.09 0.930 .7053271 1.465155 Eastern WA | 1.027693 .2101955 0.13 0.894 .6882812 1.534479
| SchlGradeLevel |
Middle | 1.376894 .255258 1.73 0.084 .9574119 1.980168 High | 2.029156 .4238936 3.39 0.001 1.347405 3.055854 Other | 1.603549 .4290377 1.76 0.078 .9491586 2.709103
| _cons | .7915287 .3446555 -0.54 0.591 .3371536 1.858256
------------------+---------------------------------------------------------------- MOUT TotalEnroll_by100 | .9978332 .0006529 -3.31 0.001 .9965544 .9991138
stPoverty by10 | .9668588 .0396932 -0.82 0.412 .8921092 1.047872 stWhite_by10 | .9202998 .0392848 -1.95 0.052 .846436 1.000609
stYearEnroll_by50 | .9850877 .0097776 -1.51 0.130 .9661092 1.004439 BEST | .8863008 .1947016 -0.55 0.583 .5762223 1.36324
FTteacher | 1.065278 .1679229 0.40 0.688 .7821415 1.450909 |
HighestDegree | MastersAndAbove | .8508445 .1189069 -1.16 0.248 .6469837 1.118941
| region |
Western WA | 1.057231 .2061817 0.29 0.775 .721387 1.549429 Eastern WA | 1.061496 .2201553 0.29 0.774 .7069335 1.59389
| SchlGradeLevel |
Middle | 1.42895 .2695439 1.89 0.058 .9873113 2.068139 High | 1.713301 .3748545 2.46 0.014 1.115832 2.630684 Other | 1.429511 .4060158 1.26 0.208 .8192671 2.494304
| _cons | 1.088647 .4848232 0.19 0.849 .4547878 2.605944
------------------+---------------------------------------------------------------- MVIN TotalEnroll_by100 | 1.002218 .0005986 3.71 0.000 1.001045 1.003392
stPoverty_by10 | .9227416 .0371501 -2.00 0.046 .8527274 .9985044 stWhite_by10 | .9628644 .0414889 -0.88 0.380 .8848867 1.047714
stYearEnroll_by50 | .9631341 .0115482 -3.13 0.002 .9407639 .9860363 BEST | .6455115 .1669696 -1.69 0.091 .3888034 1.071711
FTteacher | .8695848 .1403713 -0.87 0.387 .6337356 1.193207 |
HighestDegree | | .6917679 .1029993 -2.47 0.013 .5166812 .926186
|
42
-----------------------------------------------------------------------------------
region | Western WA | 1.910023 .4009039 3.08 0.002 1.265831 2.882049 Eastern WA | 1.991034 .4409938 3.11 0.002 1.289871 3.073344
| SchlGradeLevel |
Middle | 1.065703 .2039161 0.33 0.739 .7324257 1.550632 High | .6832319 .1744891 -1.49 0.136 .4141728 1.12708 | .5029484 .16958 -2.04 0.042 .2597301 .9739231
| _cons | .8430706 .375116 -0.38 0.701 .3524766 2.016497
------------------+---------------------------------------------------------------- STAY | (base outcome)
43
-----------------------------------------------------------------------------------
Appendix C: BEST beginning teachers’ multinomial logistic regression STATA output based on the year‐by‐year dataset (2009‐10 to 2010‐11) mlogit ndYearMOB TotalEnroll_by100 stPoverty_by10 stWhite_by10 stYearEnroll_by50 BEST FTteacher i.HighestDegree i.region i.SchlGradeLevel if Exp<1 & yr==2009, rr base(5)
Multinomial logistic regression Number of obs = 1,278 LR chi2(36) = 114.05 Prob > chi2 = 0.0000
Log likelihood = -1178.203 Pseudo R2 = 0.0462
ndYearMOB | RRR Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- Exit TotalEnroll_by100 | 1.000354 .0008754 0.40 0.686 .9986392 1.002071
stPoverty_by10 | .9453755 .0561047 -0.95 0.344 .8415667 1.061989 stWhite_by10 | .9974127 .0566521 -0.05 0.964 .892334 1.114865
stYearEnroll_by50 | .9847835 .0118487 -1.27 0.203 .9618322 1.008283 BEST | .5951737 .1479827 -2.09 0.037 .3655971 .9689127
FTteacher | .330597 .0635511 -5.76 0.000 .2268148 .4818663 |
HighestDegree | MastersAndAbove | 1.078403 .2122527 0.38 0.701 .733241 1.586046
| region |
Western WA | 1.03925 .269919 0.15 0.882 .6246574 1.729013 Eastern WA | 1.243181 .3667913 0.74 0.461 .6972587 2.216537
| SchlGradeLevel |
Middle | .9784626 .2867559 -0.07 0.941 .5509147 1.737817 High | 1.576682 .4185438 1.72 0.086 .9370989 2.65279 Other | 1.264162 .4476801 0.66 0.508 .631488 2.530698
| _cons | .4368115 .2625878 -1.38 0.168 .1344612 1.419029
------------------+---------------------------------------------------------------- MOUT TotalEnroll_by100 | .9972797 .001317 -2.06 0.039 .9947018 .9998643
stPoverty_by10 | .9658094 .0745146 -0.45 0.652 .8302696 1.123476 stWhite_by10 | 1.051455 .0787277 0.67 0.503 .9079399 1.217656
stYearEnroll_by50 | 1.041477 .0193906 2.18 0.029 1.004157 1.080184 | .4562014 .1615439 -2.22 0.027 .227898 .9132142
FTteacher | .428559 .1052832 -3.45 0.001 .2647877 .6936232 |
HighestDegree | MastersAndAbove | .7435143 .197276 -1.12 0.264 .4420188 1.250656
| region |
Western WA | .6733076 .2180642 -1.22 0.222 .3568903 1.270259 Eastern WA | .7758091 .2860082 -0.69 0.491 .3766627 1.597928
| SchlGradeLevel |
Middle | 1.045073 .3643479 0.13 0.899 .5277021 2.069686 High | .5690117 .2461183 -1.30 0.192 .2437505 1.328302 Other | 1.486734 .6545891 0.90 0.368 .627279 3.523757
| _cons | .1942574 .1551315 -2.05 0.040 .0406087 .9292577
44
Appendix D: BEST beginning teachers’multinomial logisticregression STATA outputbased ontheyear‐by‐year dataset(2013‐14 to2014‐15) mlogit ndYearMOB TotalEnroll_by100 stPoverty_by10 stWhite_by10 stYearEnroll_by50 BEST FTteacher i.HighestDegree i.region i.SchlGradeLevel if Exp<1 & yr==2013, rr base(5)
Multinomial logistic regression Number of obs LR chi2(36) Prob > chi2
= = =
2,803 183.25 0.0000
Log likelihood = -2329.9109 Pseudo R2 = 0.0378
MVIN TotalEnroll_by100 |
stPoverty_by10 | stWhite_by10 |
stYearEnroll_by50 | BEST |
FTteacher | |
HighestDegree | MastersAndAbove |
| region |
Western WA | Eastern WA |
|SchlGradeLevel |
Middle | High | Other |
| _cons |
1.001283 .9320766 1.006944 .9664263 2.164353
.3612218
.7854214
1.291673 1.55295
1.33224 .9361138 .5923396
.3783458
.0005715 .0384661 .0441226 .0107701 .5243752
.0530106
.1131827
.2467306 .341578
.2439674 .215501 .1804909
.1747049
2.25 -1.70 0.16 -3.06 3.19 -6.94
-1.68
1.34 2.00
1.57 -0.29 -1.72
-2.10
0.025 0.088 0.875 0.002 0.001 0.000
0.094
0.180 0.045
0.117 0.774 0.086
0.035
1.000163 .8596529 .9240749 .9455461
1.346172 .2709299
.5921624
.8882991 1.009095
.9304771 .5961769 .3259886
.1530517
1.002403 1.010602 1.097245 .9877676
3.47981 .4816049
1.041753
1.878218 2.389917
1.907477 1.469881 1.076314
.9352754
45
Appendix E. Coefficient Results and Accompanying 95 Percent Confidence Intervals of “Exiter” Outcome (as compared to remaining 3
outcomes combined) 2014‐15 to 2015‐16 ( N =3,278)
Independent variables Coefficient 95% confidence interval Total Enrol l me nt School % Poverty %Whi te Stude nts BESTSubse tDi stri cts Ful l Ti me Te ache r
Re gi on ( We ste rn WA ) Re gi on (Easte rn WA)
Mi ddle School Grade Le ve l Hi gh School Grade Le ve l Othe r School Grade Le ve l
<0.01 ‐0.05 ‐0.12 ‐0.56 ‐0.66 0.09 ‐0.18 0.14 0.2 0.7
‐0.0012 – 0.0014 ‐0.1244 – 0.0237 ‐0.1977 – ‐0.0467 ‐0.9540 – ‐0.1714 ‐0.9460 – ‐0.3716 ‐0.2465 – 0.4246 ‐0.5553 – 0.1968 ‐0.2010 – 0.4758 ‐0.0985 – 0.5077 0.2223 – 1.1764
Appendix F: Average Marginal Effects (AME) “Exiter” Results: Delta Method
dy/dx Std. Err. z P>|z| [95% Confi de nce Interval ] BESTSubse tDi stri cts ‐0.0399062 0.0125096 ‐3.19 0.001 ‐0.0644246 ‐0.0153878
Note: dy/dx for factor levels is the discrete change from the base level.
Appendix G: Marginal Effect at the Means (MEM) “Exiter” Results: Delta Method
Margi n Std. Err. z P>|z| [ 95% Conf i de nce Inte rval ] BESTSubse tDi stri cts
0 1 .0943988 .0560579
. 0062705
. 0093289 15. 05 6.01
0 0
. 0821089 .1066888
. 0377737 . 0743421
46
