Friedrich, Flunger, Nagengast, Jonkmann & Trautwein (2015). Teacher Expectancy Effects. ContEdPsych

Pygmalion effects in the classroom: Teacher expectancy effects onstudents math achievementAlena Friedrich *, Barbara Flunger, Benjamin Nagengast, Kathrin Jonkmann,Ulrich TrautweinHector Research Institute of Education Sciences and Psychology, University of Tbingen, Europastr. 6, 72072 Tbingen, Germany

A R T I C L E I N F O

Article history:Available online 29 October 2014

Keywords:Teachers expectanciesPygmalion effectStudents self-conceptMultilevel modelingMath achievement

A B S T R A C T

According to the Pygmalion effect, teachers expectancies affect students academic progress. Many em-pirical studies have supported the predictions of the Pygmalion effect, but the effect sizes have tendedto be small to moderate. Furthermore, almost all existing studies have examined teacher expectancy effectson students achievement at the student level only (does a specic student improve?) rather than at theclassroom level (do classes improve when teachers have generally high expectations of their stu-dents?). The present study scrutinized the Pygmalion effect in a longitudinal study by using a large samplein regular classrooms and by differentiating between two achievement outcomes (grades and an achieve-ment test) and two levels of analyses (the individual and classroom levels). Furthermore, students self-concept was studied as a possible mediator of the teacher expectancy effect on achievement. Data comefrom a study with 73 teachers and their 1289 fth-grade students. Multilevel regression analyses yieldedthree main results. First, Pygmalion effects were found at the individual level for both achievement out-comes. Second, multilevel mediation analyses showed that teacher expectancy effects were partly mediatedby students self-concept. Third, teachers average expectancy effects at the class level were found to benonsignicant when students prior achievement was controlled.

2014 Elsevier Inc. All rights reserved.

1. Introduction

Teachers form expectancies of their students achievements.Teachers expectancies are based on the knowledge they have abouttheir students, such as previous grades and perceptions of in-classperformance, but are also based on teachers prejudices or stereo-types (Good, 1987; Jussim, Eccles, & Madon, 1996; Reyna, 2000,2008). The expectancies teachers form about their students havebeen shown to impact students future achievement, an effect thatis often labeled the Pygmalion effect (Rosenthal, 2010). Pygmalioneffects have high scientic and practical relevance due to their po-tentially positive or negative effects on important student outcomes.Not surprisingly, Pygmalion effects have been the subject of manyempirical studies (meta-analyses and reviews see Jussim & Harber,2005; Rosenthal & Rubin, 1978; Tenenbaum & Ruck, 2007), whichhave documented, by and large, the existence of expectancyeffects.

However, despite the large number of studies, some of the keyquestions concerning expectancy effects have rarely been exam-ined. First, there have been few studies that have examineddifferential effects of different achievement outcomes, namely,between standardized achievement tests and nal grades, whenstudied simultaneously. Most studies concerning the effects of teach-ers expectancies on students achievement have reported only gradesas outcomes (e.g., Freiberger, Steinmayr, & Spinath, 2012; Marsh &Kller, 2004; Marsh & OMara, 2008; Tiedemann, 2000) or only testscores (e.g., Marsh, Parker, & Smith, 1983).

Second, some studies have found small signicant effects of stu-dents self-concept functioning as a mediator between teachersexpectancies and students achievement. However, empirical resultshave not been consistent across studies and have often relied onsmall sample sizes (e.g., Brattesani, Weinstein, & Marshall, 1984;Trouilloud, Sarrazin, Martinek, & Guillet, 2002). Therefore, longi-tudinal studies using large data sets of both teacher and studentreports are needed to examine expectancy effects and possible me-diation effects.

Third, the literature has yet to address whether expectancy effectsare constrained to the individual student level or also affect wholeclasses. In his early review, Good (1987) stated that teachers

* Corresponding author. Fax: 07071 / 295371.E-mail address: [email protected] (A. Friedrich).

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Contemporary Educational Psychology 41 (2015) 112

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expectancies may concern the entire class, groups of students, orspecic individuals. However, almost all studies have been inter-ested in effects operating at the student level (within-class) only:These studies have compared students within a class for whom therespective teachers had either high or low expectations. Only a fewstudies have examined the expectancy effect at the between-classlevel (i.e., do students learn more when their teacher exhibits a highaverage level of expectation toward the classroom?). Smith et al.(1998) studied such teacher expectancy effects for groups of stu-dents and also classrooms that were formed according to studentsability level and showed that expectancy effects could be con-rmed both for individuals and in part for whole groups andclassrooms. They found signicant teacher expectancy effects on stu-dents achievement in classes that used within-class ability groupingbut not for classes that used between-grouping. It is less clearwhether the achievement gains of a natural class with students whoare not grouped are associated with teachers average evaluationof the academic potential of the class.

In the present study, a multilevel design was used to disentan-gle student-level and class-level expectancy effects on two importantachievement outcomes (school grades and a standardized achieve-ment test). Furthermore, we examined students self-concept as apotential mediator of the expected effect of teachers expectancieson students progress. To do so, we were able to take advantage of astudywith a fairly large sample of students in Grade 5 (N = 1289) andtheir teachers, who were examined at three measurement points.

1.1. Teachers expectanciesPygmalion in the classroom

The Pygmalion effect refers to the effects of interpersonal ex-pectancies, that is, the nding that what one person expects ofanother can come to serve as a self-fullling prophecy (Rosenthal,2010, p. 1398). In psychological research, the classic Pygmalion effectstudy dates back to the 1950s. Rosenthal and Jacobson (1968, 1992)told elementary school teachers in an experimental study that certainchildrenwere bloomers based on their test results andwould showgreat improvement in their intellectual competence in the comingmonths. Yet, the bloomers were randomly selected and differedonly in the expectations that teachers were told to have for them.Nevertheless, by the end of the school year, those students hadgained signicantly in their intellectual achievement compared tothe control group. This self-fullling prophecy has been called thePygmalion effect.

In subsequent years, Pygmalion effects received tremendous re-search interest. In their meta-analysis, Rosenthal and Rubin (1978)summarized 345 studies about expectancy effects and found effectsizes of 0.14 to 1.73 (depending on the area of research) betweenexpectancies and achievement. However, the methodology of theseearly studies (e.g., Rosenthal & Jacobson, 1968, 1992) was criti-cized as these studies used small samples, ignored the clusteringof data, and had unknown ecological validity as they were con-ducted mainly as experimental studies in the laboratory setting.Nevertheless, later research in ordinary classrooms usingnonexperimental research designs found smaller but still signi-cant effects of teachers expectations on students academicachievement, accounting for a maximum of 510% of studentsachievement (e.g., Brophy, 1983; Cooper, 1979; Jussim & Eccles, 1992,1995; Madon, Jussim, & Eccles, 1997). For instance, Jussim and Ecclesexamined the effect of math teachers expectancies on the achieve-ment of their sixth-grade students (Jussim & Eccles, 1992). In linewith the self-fullling prophecy hypothesis, teachers expectan-cies predicted changes in student achievement even when effectsof previous achievement and motivation were controlled. However,effects in naturally occurring eld studies are often smaller than instrict laboratory settings with experimental manipulation. The

smaller coecients are not surprising given that teachers expec-tancies were not pervasive and enduring per se, but rather exibleand open to change as soon as more information about individualstudent achievement was available (Brophy, 1983).

Which mechanisms account for teachers expectancy effects?Brophy and Good (1970) described a possible mechanism behindteachers expectancies in a comprehensive model: (a) Teachersform differential expectancies for their students. (b) Teachersbeliefs about those students begin to lead to different treatmentsuch as providing more attention and support (climate), offeringmore challenging learning materials (input), interacting moreoften and longer (output), and being more responsive to the work(feedback) of the students for whom they hold high expectations(Rosenthal, 1974). (c) Students in turn recognize the teachershigh expectancies and react to them: They may work moreand harder and develop higher motivation and interest inschoolwork. (d) This more engaged student behavior will, in thelong run, improve their academic achievement. Those changesmay also affect students self-concept and motivation (Harris &Rosenthal, 1985). (e) The teacher recognizes the positive changesin the students behavior, feels supported in his/her formerexpectancies and the self-fullling cycle is complete and rein-forced. To conclude, there seems to be reasonable theoretical supportfor the effects of teachers expectancies on students achievement.However, longitudinal eld studies concerning teacher expectan-cy effects have thus far rarely taken into account differentachievement outcomes.

1.1.1. The role of different achievement outcomesAccurate evaluations of students achievement and progress in

school are essential for students learning. Grades and standard-ized achievement tests are both common indicators of studentachievement. On the one hand, grades are central in many schoolsystems as they are used for schooling-related decisions such as ac-celeration or remediation or the counseling of parents. Gradesincorporate achievement assessments of several occasions in writtenand verbal form over a whole school year and are therefore less in-uenced by one-time situational events. Moreover, grades assessrather general achievement across different specic topics withinone subject.

On the other hand, standardized tests are common inmany schoolsystems including the American school system, and studies haveconrmed their predictive validity for various student outcomes (e.g.,Kuncel, Hezlett, & Ones, 2001; Kuncel, Wee, Seran, & Hezlett, 2010).In particular, tests have the advantage of allowing comparisons acrossclasses or schools as test results are assumed to be less inuencedby the class as a reference standard than grades (e.g., Kimball, 1989).

Theoretically, the so-called perceptual bias hypothesis claims thatteachers expectancies of students competence should predict theirown judgments of students grades more than an independentachievement test (Jussim & Eccles, 1992; Smith et al., 1998). Indeed,Jussim and Eccles (1992) found those results in their longitudinalstudy of sixth graders. However, some researchers have found theopposite results in which teachers expectancies predicted stu-dents test scoresmore strongly than they predicted nal grades (e.g.,Trouilloud et al., 2002).

So far, most studies concerning expectancy effects on studentsachievement have relied on only test scores (e.g., Marsh et al., 1983)or only grades (e.g., Freiberger et al., 2012; Marsh & Kller, 2004;Marsh & OMara, 2008; Tiedemann, 2000). Just a few studies havereported effects on tests and grades simultaneously (e.g., Jussim &Eccles, 1992). As more andmore researchers recommend using bothtests and grades to prot from the strengths of both methods(Brennan, Kim,Wenz-Gross, & Siperstein, 2001), bothmeasures wereincluded in the present study.

2 A. Friedrich et al./Contemporary Educational Psychology 41 (2015) 112

1.2. Students self-concept as a potential mediator

Studies have shown that the effect of teachers expectancies onstudents achievement can be (partly) mediated by students self-concept (Brattesani et al., 1984; Kuklinski & Weinstein, 2001;Trouilloud et al., 2002). Students who hold higher self-concepts seemto perceive themselves as more academically competent and con-dent and therefore tend to accomplish more than students withmore negative self-perceptions (e.g., Marsh & Craven, 2006; Marsh& Kller, 2004; Marsh & Yeung, 1997; Trautwein, Ldtke, Roberts,Schnyder, & Niggli, 2009). This effect has been shown in particularfor the domain-specic association of students math self-conceptand their math achievement. Previous ndings have shown posi-tive correlations between students math self-concept and studentsmath grades of r = .46 to .50 (Jussim & Eccles, 1992) or even r = .70(Marsh, Trautwein, Ldtke, Kller, & Baumert, 2006).

Regarding math, students self-concept can be positively inu-enced by good prior academic achievement and by ability judg-ments of signicant others, such as teachers (Dickhuser &Stiensmeier-Pelster, 2003; Marsh, Craven, & Debus, 1998; Spinath& Spinath, 2005a) and parents (Frome & Eccles, 1998; Spinath &Spinath, 2005a). More precisely, teachers expectancies were foundto be signicantly related to different self-concept domains of el-ementary children (e.g., math, reading, or school in general; Marshet al., 1998). To this end, teachers can be considered to be impor-tant agents in forming the self-concepts of their students. Althoughstudies have shown that teachers expectancies can inuence stu-dents self-concepts, few studies have examined whether those self-concepts also mediate the effect of teachers expectancies onstudents achievement. After controlling for prior achievement, therewas a small but signicant effect of teachers expectancies on stu-dents achievementmediated by the students self-concept for Grade5 students in those classes in which teachers made their expec-tancies especially salient to the students (Kuklinski & Weinstein,2001). However, results for Grade 1 and Grade 3 students were notsignicant, and only one achievement outcome (i.e., test scores) wasused. A study with eighth and 11th graders and their teachersshowed a small mediation effect of students self-concept for therelation between teachers expectancies and students achieve-ment score (Trouilloud et al., 2002). Yet, the study was conductedfor swimming competence and contained a relatively small samplewith seven teachers and their students. A study with third to sixthgraders provided support for a student mediation model of teacherexpectancy effects (Brattesani et al., 1984). However, again, thesample was relatively small with seven versus 16 teachers and theirstudents and relied on only test scores as the outcome. Till now,studies examining possible mediation effects of students self-concept suffer from certain limitations, which need to be addressedin future research.

1.3. Teachers expectancies at the class level

In his early review, Good (1987) stated that teachers expectan-cies may concern the entire class, groups of students, or specicindividuals. In a model of the Pygmalion effect by Trouilloud andSarrazin (2003), Pygmalion effects were conceptualized as the effectsof teachers expectancies for both individual students and for groupsof students or a whole class. Most empirical studies, however, focuson teachers expectancies about individuals (Spinath & Spinath,2005a; Trouilloud et al., 2002) or specic groups of students, forexample racial minority students or people from a lower class back-ground (Jussim et al., 1996; Jussim & Harber, 2005; Tenenbaum &Ruck, 2007). Only a few studies have analyzed effects of teachersexpectancies for the competences of an entire class on students char-acteristics (e.g., Martin, Veldman, & Anderson, 1980; Smith et al.,1998). Eden (1990) conducted a study with Defense Forces in the

army and found support for the Pygmalion effect for entire workgroups. In their meta-analysis, Kierein and Gold (2000) summa-rized 13 studies about Pygmalion effects in work organization; someof them also had groups as the unit of analysis for which they foundan effect of d = 0.83. Yet, the study by Eden and the meta-analysismanipulated expectancies rather than employing naturally occur-ring expectancies and was conducted in the work organizationalcontext, thus leaving the generalizability to educational settingsunclear.

Smith et al. (1998) analyzed expectancy effects on studentsachievement for students grouped by ability within and betweenclassrooms and for students in heterogeneous classrooms (i.e., inwhich no ability grouping took place). They did not nd teacher ex-pectancy effects (measured as perceptions of performance, talent,and effort) on students achievement at the class level, drawing onaggregated data. Yet, they analyzed whether ability grouping ofclasses moderated the relation between teacher expectations andclass achievement and found evidence for this in classes that usedwithin-class grouping.

Thus, although it is theoretically reasonable to assume wholegroup effects, these effects have seldom been analyzed empirical-ly in the educational setting. In line with the few existing formerstudies, we assumed that teachers might form evaluations not onlyfor a single student or a subgroup of students but also for wholeclasses. Those class-level teachers expectancies could beoperationalized in two ways: First, teachers could be asked direct-ly about their expectancies for the class as a whole (e.g., Hastings& Bham, 2003; Lorenz, 2005). A second way is by aggregating teach-ers expectancies for the individual students in their class. In theliterature onmultilevel analyses, aggregating student or teacher vari-ables, grades, or test scores on the class level is a common methodof separating and analyzing student- and class-level effects (e.g.,Croninger, Rice, Rathbun, & Nishio, 2003; Trautwein, Ldtke, Marsh,Kller, & Baumert, 2006; Trautwein, Ldtke, Marsh, & Nagy, 2009).Previous studies investigating expectancies for groups did not useglobal assessments when exploring expectancy effects for groups(Smith et al., 1998). Inmore detail, prior studies on expectancy effectsfor groups followed the assumption that groups of people consistof different individuals and that their differences account for the per-ception of the whole group (e.g., see Eden, 1990). For example, inthe study by Eden (1990) conducted in a military context, leaderswere not told that this group had high potential on average, butthat the people in the group had high potential on average. Second,with the multi-level analysis, we took into account the nested struc-ture of the data and were able to separate both student- andclassroom-level effects, which can be seen as a strength of the presentmanuscript (e.g., Miller & Murdock, 2007). Predictor and outcomemeasure were assessed on the same (individual) level, aggregatedbefore the analyses, and therefore were more comparable. Indeed,with this approach it was ensured that each student was taken intoaccount to the same extent. Therefore, in particular to increase con-sistency and comparability with prior expectancy research, andfollowing Smith et al.s (1998) considerations, we used the secondmethod and refer to this aggregated teacher measure as teachersaverage expectancies.

When a teacher holds rather low average expectancies for a class,this could result in the selection of less dicult tasks, repeatedproblem talk, and less appreciation by the teacher. In the long term,these actions may result in lower self-concepts or achievements ofthe students in this class. By contrast, if a teacher has rather highaverage expectancies for a class, this teacher might select challeng-ing tasks, focus more on the strengths of the students, and give moreenforcement, and these actions may all have a positive effect on stu-dents self-concept or achievement. This positive effect might beespecially helpful for low-achieving students with poor self-perceptions (which we used in the present study) because a

3A. Friedrich et al./Contemporary Educational Psychology 41 (2015) 112

supportive and encouraging teacher who insists on a general beliefin progress could help those low-achieving students to stay moti-vated (Jussim et al., 1996; Spinath & Spinath, 2005b).

So far, research has not provided sucient insight into class-level effects of teacher expectancies on students class achievement;consequently, these kinds of studies have thus far been missing inthe educational setting. Previous ndings suggest that teacher ex-pectations may be stronger aligned with the class than withindividual students (Rubie-Davies, Hattie, & Hamilton, 2006): teach-ers may hold high or low expectations for their whole class that evencan over- or underrate students actual competencies. Rubie-Davies(2007) found that teachers with high expectancies for a class hadcorrespondingly higher expectations for high-ability, average andbelow-average students. Similarly, teachers with low expectan-cies for a class had correspondingly lower expectations for high-ability, average and below-average students. If class-centered teacherexpectations had effects on students self-concept and achieve-ment, this might indicate that the direction of the teacher-expectancy effect is stronger from the teacher to the students thanthat from the students to the teacher (Rubie-Davies et al., 2006,p. 540). And indeed, in an intervention study in 84 classrooms,teacher expectancies for all students could be raised, this effect wassustained over two school years and inuenced students learningin math (Rubie-Davies, Peterson, Sibley, & Rosenthal, 2014).

1.4. The present study

There has been extensive research on the Pygmalion effect, yetthere are several limitations to the existing researchmentioned above(relying on either test scores or grades, small sample sizes, lack ofmediation analyses in eld studies and lack of analyzing possibleclass-level effects). Therefore, using a multilevel dataset collectedon a sample of N = 1289 fth-grade students from 73 classroomsand their 73 teachers, the present study examined student-level ex-pectancy effects on two important achievement outcomes (schoolgrades and a standardized achievement test). Furthermore, we in-vestigated the role of students self-concept as a potential mediatorof the expected effect of teachers expectancies on students achieve-ment. Third, student- and class-level effects were teased apart toexplore potential class-level effects. We selected low-achieving stu-dents because the Pygmalion effect might be especially salient andimportant in this subpopulation (Jussim et al., 1996; Spinath &Spinath, 2005b). The study was conducted in a real-life setting witha domain-specic focus on math.

In total, we tested the following three research questions. First,do teachers expectancies regarding students competences predictstudents achievement in our sample and will results be signi-cant for both achievement outcomes? Given the overall support forPygmalion effects (e.g., Brophy, 1983; Jussim & Harber, 2005; Madonet al., 1997; Tenenbaum & Ruck, 2007), we expected to nd signif-icant results for students achievement.

Second, we were interested in whether any expectancy effectswould be mediated by students expectancy beliefs. We specu-lated that students self-concept would mediate the associationbetween teachers expectancies of students competences and stu-dents actual achievement.

Third, we probed for a Pygmalion effect at the class level. In moredetail, we explored whether teachers average expectancies of thestudents in their class would be associated with students achieve-ment. Teachers average expectancies might be an effect that existsabove the effect of their expectancies for individual students andmight be even more powerful as many more students would be af-fected at the same time (Smith et al., 1998). However, given thelimited number of articles about Pygmalion effects at the class level,we did not have a clear prediction about the size of the effect of

teachers average expectancies for entire classes on students achieve-ment; thus, we conducted a rather exploratory analysis.

2. Method

2.1. Sample

The participants were math teachers (N = 73) and their fth-grade students (N = 1289) attending the lowest school track inGermany (Haupt andWerkrealschule) who took part in a larger studyon self-regulated learning.1 Teachers and students participated vol-untarily. The core prerequisite for participation was that the teacherstaught a fth-grade math class. The study was conducted in 2012during regular school hours. Data from 73 classes in different schoolswere collected by trained research assistants at three time points.The rst measurement was in February (T1), the second in April (T2),and the third in June (T3). Teachers were asked to report their ex-pectancies concerning their fth graders math competence at T1.Students reported their math self-concept at T2. Students mathachievement was assessed at T3. In addition, control variables suchas students prior self-concept and prior achievement were as-sessed at T1. There were two rationales behind our decision toconduct the rst measurement in the middle of the school year inFebruary. First, as teacher judgments about student performanceare likely to be inuenced by the amount of academic exposure toa student (Begeny, Eckert, Montarello, & Storie, 2008, p. 53; alsosee Jussim, 1989; Kenny, 2004), teachers should have had an ex-tended time period to observe their students (Jussim, 1989).Following this idea, several previous studies on expectancy effectsin the classroom context have also used a long time lag, which allowsteachers to be acquainted with their students before rating them(e.g., Brattesani et al., 1984; Marsh & Craven, 1991; Praetorius, Karst,Dickhuser, & Lipowsky, 2011; Smith et al., 1998). Second, the de-cision to implement a time lag of 6 months was also driven by ourinterest in explaining achievement changes in students. There-fore, we wanted to assess two grades assigned by the same teacherwithin a school year. Consequently, in our studys design, the mea-surement of grades took place at the time points when the half-and end-year grade cards were assigned. In summary, following the-oretical considerations and previous expectancy research, teachersexpectancies were surveyed in February, about 6months after teach-ers had started teaching their students. To reward their participation,students received sweets and teachers received a written report ofthe main ndings; the report was sent to each school after the studywas completed. As studies have shown that most motivational andaffective constructs are domain-specic in nature (e.g., Bong, 2001;Goetz, Frenzel, Pekrun, Hall, & Ldtke, 2007), we decided to focuson one subject (i.e., mathematics). This focus reduced complexity

1 The present data were derived from an intervention study with two experi-mental groups and one control group. In order to test whether it would be appropriateto treat the three groups as one dataset for the present analyses, we tested whetherthe effects of the independent variables on the outcomes differed across the threegroups (two experimental groups vs. one control group). In more detail, we com-pared a model in which the effects of the central independent variables on studentsmath test scores, grades, and self-concept were constrained to be invariant acrossgroups to a model in which the effects of the independent variables were allowedto vary freely. We analyzed separate analyses for each outcome. For model evalu-ation, the common t indices 2, CFI, and RMSEA were used. If the more restrictivemodels exhibited t indices that were similar to those of the unconstrained model,measurement equivalence of the constructs across the groups was assumed (see Little,1997). Fit indices for all models were satisfactory. In addition, to evaluate compar-ativemodel t, SatorraBentler-scaled chi-squares were used for chi-square differencetests. The chi-square difference test indicated that the invariance constraints did notyield a signicantly worse model t. Consequently, the analyses were conducted onthe whole dataset.


in the interpretation of our results. The study procedure was ap-proved by the responsible institutional review board.

On average, teachers were 46 years old (SD = 11.71) with a servicelength of 17.33 years (SD = 11.32); 68% were female. Grade 5 is therst year of secondary school. Seventy-eight percent of the mathteachers were also the principal class teacher who taught stu-dents in other subjects in addition to math.

Students were 10 to 14 years old (M = 10.95, SD = 0.77) andequally distributed with respect to gender (52% boys). Class sizesvaried from nine to 29 students. Only students with active paren-tal consent participated in the study. Nevertheless, the participationrate was high (91.2%).

2.2. Measures

2.2.1. Teacher reportsTeachers were asked to report their expectancies of their fth-

grade students math competence at T1. In general, teachersexpectancies can be assessed by asking teachers about their ex-pectancies of students future success (e.g., How good will thisstudent be in swimming?, Trouilloud et al., 2002) or by asking teach-ers for their present opinions of students competences (e.g., Howtalented is this student?, Jussim & Eccles, 1992), which is also as-sociated with future-directed expectancies about competences andachievement. In line with Jussim and Eccles (1992) as well asFriedrich, Jonkmann, Nagengast, Schmitz, and Trautwein (2013), weused the second approach in the present study. Teachers receivedtwo items to rate on a 4-point Likert-type scale ranging from 1 (com-pletely disagree) to 4 (completely agree) andwere asked to assess eachstudent without reference to the other students in the classroom.The items (The student can solve even dicult mathematical tasksand The student does well in mathematics; = .86; see Table 1)were adapted from the mathematic abilities subscale of the Germanversion (Schwanzer, Trautwein, Ldtke, & Sydow, 2005) of the Self-Description Questionnaire (SDQ; Marsh, 1990). The scale consistsin total of four items, more specically, of two positively and twonegatively worded items. Due to limited space we only assessed twoitems in the teacher questionnaire at the rst measurement. As neg-atively worded itemsmay have undesiredmethod effects (e.g., Marsh,1996), we selected the two positively worded items. In research onteacher expectancies and teachers judgments, single-item mea-sures are commonly used (e.g., Hoge & Butcher, 1984; Kuklinski &Weinstein, 2001; Pohlmann, Mller, & Streblow, 2004; Praetorius,Berner, Zeinz, Scheunpug, & Dresel, 2013; Praetorius, Greb,Lipowsky, & Gollwitzer, 2010; Spinath, 2005). Therefore, we assumedthat a two-item measure was acceptable. The reliability of the re-sulting measure was very satisfying ( = .86).

2.2.2. Student self-reportsStudents math self-concept was assessed by two items at T1

and four items at T2. The items (e.g., I can solve even dicultmathematical tasks and I do well in mathematics; T1: = .61;

T2: = .79; see Table 1) were also adapted from the SDQ with iden-tical wording.

2.2.3. Students achievementStudents scholastic achievement was assessed by students math

grades and students scores on a standardized math achievementtest. The math grade was obtained from school records at the endof the school year (in July). In the German school system, teachersevaluate their students with numerical grades that range from 1 to6. We recoded the grades so that higher values indicate betterachievement. In addition to collecting grades, we conducted a stan-dardized math test. At both measurement points (T1 and T3), thetest consisted of 34 items with varying response formats (e.g., mul-tiple choice items, open questions, drawing tasks, i.e., plot the resultin a coordinate system). The items measured a broad array of stu-dents math competences such as logical inference, division,transformation or use of the rule of three. Students had 35 minutesto complete the test. We applied two parallel tests (Forms A andB) at each time point and used a rotation design with a set of xeditems (anchor items) to be able to compare the test results of Times1 and 3 and a set of items that varied between the time points toreduce possible inuences of training effects. The test content wasbased on the school curriculum of fth graders in Germany.2 Al-though our sample consisted of students from the lowest school trackonly, there was a representative amount of variability in grades andtest scores.

Item response theory (IRT) was used to scale students mathachievement test scores. Model t was checked using conrmato-ry factor analysis models based on polychoric correlations. Ananalysis of dimensionality indicated a good t for a one-dimensionalmodel at each time point (Time 1: RMSEA = .025; Time 3:RMSEA = .018). In addition, combined IRT models also resulted ingoodmodel t, thereby indicatingmeasurement invariance.We usedexpected a posteriori person-parameter estimates (EAPs) calcu-lated with Mplus for further analyses.

2.2.4. Control variablesAs control variables, we assessed students sex, age, their ability

to do gural reasoning, and their prior achievement. For the guralreasoning score, we used the Fig. Analogies subscale of the Cogni-tive Ability Test 412 + R (Heller & Perleth, 2000), a Germanadaptation of the Cognitive Abilities Test developed by Thorndike

2 Germany consists of 16 federal states, each of which has specic educationalsystems and curricula. We focused on one federal state (Baden Wuerttemberg),therefore ensuring that all public schools in this state, i.e., in our sample, followedthe same curriculum. All schools teach the same content in the same order duringthe school year. In addition, we asked the schools in advance to focus on a specicmathematical topic for the timeframe of the study (a topic that complied with theplanned curriculum). By ensuring that similar mathematical content was taught andby focusing on one federal state, the use of a standardized achievement test wasjustied and results can be considered comparable.

Table 1Descriptive statistics of teachers math expectancies of their students math competence and students math self-concept.

T1 T2

Construct No. Item M SD ICC M SD ICC

Teachersexpectancies

1 The student can solve even dicult mathematical tasks. 2.29 0.86 0.19 2 The student does well in mathematics. 2.57 0.83 0.13

Studentsself-concept

1 I can solve even dicult mathematical tasks. 2.49 0.91 0.03 2.67 0.94 0.022 I do well in mathematics. 2.93 0.81 0.03 2.90 0.79 0.053 I always have a problem with mathematical tasks. (r) 3.11 0.88 0.054 I would like the subject math more if it wasnt so dicult. (r) 2.57 1.15 0.04

Note: N = 1145 to 1281. (r) = reverse coded. M = mean, SD = standard deviation, ICC = Intraclass correlation. Measurement time points: T1 = February, T2 = April; M, SD, andICC were calculated on uncentered items.


and Hagen (1971). The subscale is an ecient and often used non-verbal measure of students cognitive abilities, tapping highlyg-loaded ability components for which norm data for fth gradersin Germany exist. The subscale consists of 25 gural items in amultiple-choice format and takes 8 minutes. Again, we had two par-allel tests (Forms A and B).

IRT was used to scale students test scores. For students cogni-tive abilities, the Rasch model was chosen as the measurementmodel. Item- and person-parameters were estimated using ConQuest2.0 (Wu, Adams, Wilson, & Haldane, 2007). The model t statisticswere satisfactory with no signs of bottom or ceiling effects. Weightedmaximum likelihood estimates (WLE) were used as person param-eter estimates in further analyses. Marginal reliabilities for WLEsreached acceptable values (Rel. = .835 for Form A and Rel. = .827 forForm B).

2.3. Statistical analyses

2.3.1. Multilevel structureAs wewere interested in effects of teachers expectancies on out-

comes of individual students and on the class level, we used amultilevel framework with students on the within level (Level 1) andclass on the between level (Level 2). Besides our thematic interest,using multilevel analyses is the appropriate method due to the clus-tering of students in a class that is assessed by one teacher, astructure that violates the assumption of independence of obser-vations (Snijders & Bosker, 2012). A multilevel approach (a) yieldscorrect standard errors and (b) allows the user to separate the vari-ance between the two levels of analysis (Raudenbush & Bryk, 2002;Snijders & Bosker, 2012). The degree of clustering and the amountof between-class variance are usually measured by the intraclass cor-relation (ICC). The ICC is the proportion of total variance explainedby the variation at the class level, it compares variance compo-nents between and within clusters, and it ranges from 0, meaningtotal independence of observations to 1.00, meaning maximum de-pendency within clusters (Snijders & Bosker, 2012). In the presentstudy, the ICCs ranged from 0.13 to 0.19 for teacher reports at T1.For students self-reports, the ICCs ranged from 0.02 to 0.05 at T2,thus implying that theywere onlymoderately affected by the nestingin classrooms (see Table 1).

To examine Research Questions 1 and 2, we used the grand-mean-centering option in Mplus, in which overall means weresubtracted from each variable (Muthn & Muthn, 19982012). Toexamine effects of teachers average expectancies for a whole class(Research Question 3), we aggregated teachers expectancies of in-dividual students. We averaged those expectancies for each classusing the between-level function in Mplus. We further calculatedcontextual effects. Following instructions by Nagengast and Marsh(2012), we used the latent aggregation procedure in Mplus in whichall Level 1 variables are implicitly grand-mean centered. Esti-mates of contextual effects that represent the effect of Level 2variables after controlling for Level 1 differences can be obtainedby subtracting the Level 1 effect from the Level 2 effect (Enders &Toghi, 2007; Kreft, de Leeuw, & Aiken, 1995; Marsh et al., 2012).

2.3.2. Regression analysesTo examine Research Questions 1 and 3, we analyzed two sep-

arate multilevel regression analyses. First, to investigate the effectsof teachers expectancies for individual students, we analyzed teacherexpectancy effects at the student level (Level 1) for both outcomevariables. Second, we conducted another multilevel regression anal-yses in which teachers expectancies were entered as a predictorat the class level (Level 2) in order to test the teachers average ex-pectancy effects for a whole class on students achievement. Bothmultilevel regression analyses on the student level and the class levelwere calculated separately for the two outcomes (math grade and

achievement test score). All analyses were conducted using MplusVersion 7 (Muthn & Muthn, 19982012). The maximum likeli-hood estimator with robust standard errors was used, yielding YuanBentler-scaled chi-square values.

2.3.3. MediationTo assess whether the effects of teachers expectancies on the

two outcomes were mediated by students self-concept (ResearchQuestion 2), we analyzed two mediation models. We specied a111 model (lower-level mediation) in which our predictor, me-diator, and outcome variable were all specied on Level 1 only (e.g.,Bauer, Preacher, & Gil, 2006; Krull & MacKinnon, 2001). For bothmodels, we used the syntax provided by Preacher, Zyphur, and Zhang(2010), in which direct and indirect effects were calculated. In moredetail, we rst entered teachers expectancies of students compe-tence and students self-concept as student-level predictors of eitherthe achievement-test outcome or themath-grade outcome. Thereby,the direct effect of teachers expectancies on students self-conceptand the indirect effect of teachers expectancies on the achieve-ment outcomes as mediated by students self-concept were alsoestimated in the respective model. We calculated the effect size ofthe indirect effects using the 2 statistic (Preacher & Kelley, 2011).2 is interpreted as the proportion of the maximum possible indi-rect effect that could have occurred. value of 0 means there is nolinear indirect effect, whereas 1 indicates that the indirect effect isas large as it could be. By using this coecient, we took into accountthe possibility that an absolute effect may seem trivial but may infact be large when one considers the range of potential values thatthe effect could have assumed. 2 is a standardized value and is in-dependent of sample size (see Preacher & Kelley, 2011 for moreinformation).

2.3.4. Measures of explained varianceIn addition to reporting unstandardized regression coe-

cients, we will report the proportion of explained variance (R2) atthe within- and between-student levels. Further, as suggested bySnijders and Bosker (1994) and following Nagengast, Trautwein,Kelava, and Ldtke (2013), we will also report the mean squaredpredictor error as an overall measure of explained variance.

3. Results

Descriptive results for teachers expectancies of their studentsmath competence and students math self-concept are summa-rized in Table 1. To test our research questions, two separatemultilevel regression analyses and multilevel mediation analyseswere conducted for each outcome variable, respectively. The rstresearch question dealt with teacher expectancy effects at the studentlevel, and Research Question 2 examined possible mediation effectsof these expectancy beliefs. Research Question 3 examined possi-ble teacher expectancy effects at the class level. We will begin bysummarizing the results for our control variables for all models. Nextwe will report the main ndings of each research question, takinginto account the effects of the control variables separately.

3.1. Control variables

We tested the effects of the control variables students sex, age,gural reasoning score, and prior achievement in all models. Co-ecients of the control variables are reported in Tables 24. Insummarizing the effects on students math achievement, we foundrather small and only a few signicant coecients for students sexand for students age, small but signicant coecients for stu-dents gural reasoning score, and rather large andmostly signicantcoecients for prior achievement on the student and class levels.


3.2. Expectancy effects at the student level (research question 1)

For Research Question 1, we analyzed whether teachers expec-tancies of their students math competences would be associatedwith students achievements at the student level. Given the overallsupport for Pygmalion effects, we expected to nd signicant resultsfor teachers expectancies of their students competences and bothachievement outcomes. Thereby, we controlled for students sex,age, gural reasoning score, and prior achievement (at T1). Grand-mean centering was used. The results of these multilevel regressionanalyses are summarized in Table 2. In the rst columns, results forthe model with students math test score as the outcome variableare reported; the columns to the right present results for the modelwith students math grade as the outcome. Concerning ResearchQuestion 1, teachers expectancies signicantly predicted both stu-dents math test score (b = .11, p = .00) and students math grade(b = .13, p = .00). The within-student R2 values were 26% and 62%,respectively.

To summarize the ndings for Research Question 1: Teachersexpectancies of their students math competences were shown tosignicantly predict changes in students math achievement, thusreplicating former studies about the Pygmalion effect in a large lon-gitudinal eld study using two achievement outcomes.

3.3. Mediation at the student level (research question 2)

Research Question 2 addressed the question of whether the as-sociation between teachers expectancies of their students mathcompetences and students achievements would bemediated by stu-dents self-concept in math. We tested our assumptions in twoseparate models for math test and math grade (see Table 3, upperand lower parts). We controlled for students sex, age, gural rea-soning score, prior self-concept, and prior achievement (i.e., priortest score or grade) on the within-students level in the model. Con-cerning Research Question 2, we found signicant associationsbetween teachers expectancies and students self-concept (b = .17,

Table 2Two regression models analyzing teachers expectancies of students math competences as predictor of students math achievement (Research question 1).

Outcome math test (T3) Outcome math grade (T3)

Variable B SE B SE

Research Question 1(outcome math test/grade)

Within-students levelSex 0.00 0.04 0.05 0.03Age (T1) 0.06** 0.02 0.01 0.03Figural reasoning (T1) 0.08** 0.01 0.04** 0.01Math test (T1) 0.35** 0.03 Math grade (T1) 0.68** 0.04Teachers expectancies (T1) 0.11** 0.03 0.13** 0.03

Variance componentsWithin-student R2 0.26 0.62

Note: Unstandardized regression coecients are reported. Grand-mean centering was used. Measurement time points: T1 = February, T3 = June. Students sex was coded:0 = male, 1 = female. Grades were reverse coded with 1 indicating the worst and 6 the best grade. R2 = explained variance.** p < .01.

Table 3Two mediation models analyzing the role of students self-concept as a possible mediator of teachers expectancies of students math competences and the outcome stu-dents math achievement (Research question 2).

Mediator students math self-concept (T2) Outcome math test (T3)

B SE B SE

Research Question 2(outcome math test)

Within-students levelSex 0.14* 0.03 0.02 0.04Age (T1) 0.02 0.02 0.06** 0.02Figural reasoning (T1) 0.01 0.01 0.09** 0.01Math test score (T1) 0.02 0.03 0.34** 0.03Students self-concept (T1) 0.55** 0.03 0.04 0.03Teachers expectancies (T1) 0.17** 0.03 0.11** 0.03Students self-concept (T2) 0.08* 0.04


Mediator students math self-concept (T2) Outcome math grade (T3)

B SE B SE

Research Question 2(outcome math grade)

Within-students levelSex 0.17** 0.04 0.03 0.03Age (T1) 0.02 0.02 0.00 0.03Figural reasoning (T1) 0.01 0.01 0.04** 0.01Math grade (T1) 0.15** 0.04 0.64** 0.04Students self-concept (T1) 0.52** 0.03 0.02 0.03Teachers expectancies (T1) 0.09* 0.04 0.15** 0.04Students self-concept (T2) 0.07** 0.03


Note: In the upper part of the table, results of the mediation model for the math-test outcome are summarized; in the lower part, results for the math-grade outcome aresummarized. Unstandardized regression coecients are reported. Grand-mean centering was used. Measurement time points: T1 = February, T2 = April, T3 = June. Stu-dents sex was coded: 0 = male, 1 = female. Grades were reverse coded with 1 indicating the worst and 6 the best grade. R2 = explained variance.* p < .05. ** p < .01.


p = .00, in the math test model vs. b = .09, p = .01, in the math grademodel). We also found signicant direct effects of students self-concept (T2) on students achievement (b = .08, p = .08, for the mathtest, b = .07, p = .01, for math grade). The indirect effect of teachersexpectancies on the math test mediated by students self-conceptwas not signicant (b = .01, p = .07). The indirect effect of teachersexpectancies onmath grade mediated by students self-concept wassmall and signicant (b = .01, p = .05). We calculated the effect-size measure 2, which displays the size of the indirect effect relativeto the maximum possible indirect effect given the constraints of thevariancecovariance matrix of the three variables involved in theanalysis. The Level 1 mediation effect size was rather small with2 = 0.03 for the math test and 2 = 0.09 for math grade. We furthercalculated the within-student R2 values, which resulted in 27% ex-plained variance in students math test score and 63% explainedvariance in students math grade on the within-students level. Themain results of the two multilevel mediation models are summa-rized in Fig. 1.

To summarize the ndings for Research Question 2: The asso-ciation between teachers expectancies of their studentscompetences and students achievements were partially mediatedby students self-concept in math for the math-grade outcome butnot for the math-test outcome.

3.4. Expectancy effects at the class level (research question 3)

Research Question 3 addressed whether teachers average ex-pectancies of their students math competence would be reectedby the students achievements. To this end, teachers average ex-pectancies were applied at the class level to predict students twomath achievement outcomes (see Table 4). In both models, we con-trolled for students sex, age, gural reasoning score, prior mathachievement, and teachers expectancies of individual students mathcompetence at the within-students level.

In addressing Research Question 3, we found no signicant as-sociation between teachers average expectancies and students later

Table 4Two regression models analyzing teachers aggregated expectancies of their class math competences (on the between-students level) as a predictor of students math achieve-ment (Research question 3).

Outcome math test (T3) Outcome math grade (T3)

Variable B SE B SE

ResearchQuestion 3

Within-students levelSex 0.01 0.03 0.05 0.03Age (T1) 0.06** 0.02 0.01 0.03Figural reasoning (T1) 0.08** 0.01 0.04** 0.01Math test (T1) 0.34** 0.03 Math grade (T1) 0.67** 0.04Teachers expectancies (T1) 0.13** 0.03 0.14** 0.04

Between-students levelTeachers average expectancies (T1) 0.04 0.11 0.04 0.08

Variance componentsWithin-student R2 0.27 0.62Between-student R2 0.41 0.71Snijders & Boskers R2 0.31 0.64

Note: Unstandardized regression coecients are reported. Grand-mean centering was used for the calculation of context effects (model constraints). Measurement timepoints: T1 = February, T3 = June. Students sex was coded: 0 = male, 1 = female. Grades were reverse coded with 1 indicating the worst and 6 the best grade. R2 = explainedvariance.** p < .01.

Level 2

Level 1

Level 2

Level 1

0.17** 0.08*

Math test score (T3)

0.11**

0.09* 0.07**

Math grade (T3)

0.15**

Teachers expectancies (T1)

Students self-concept (T2)

Students self-concept (T2)

Teachers expectancies (T1)

Fig. 1. Results of multilevel mediation models at the within-students level (Research Question 2): Students self-concept was assumed to mediate the effect of teachersexpectancies on students math achievement. Measurement time points: T1 = February, T2 = April, T3 = June. Although not illustrated, models were calculated controllingfor students sex, age, gural reasoning score, prior math achievement, and prior self-concept (T1) in math.*p < .05. **p < .01.


math test score (b = .04, p = .70) or students latermath grade (b = .04,p = .66). We analyzed contextual effects by subtracting the Level 1effects from the Level 2 effects. The contextual effect was negativefor the math test (b = 0.17, p = .14) as well as for math grade(b = 0.11, p = .23), but the coecients were not signicant. There-fore, for students with equal preconditions, being in a classenvironment with students for whom their teacher held generallyhigh or low expectancies in math had no association with their in-dividual math achievement. We further calculated the explainedvariance components, resulting in within-student R2 values of 27%and 62%, between-student R2 values of 41% and 71%, and Snijdersand Boskers R2 values of 31% and 64%. The large and signicantbetween-student R2 value can be explained by the impact of thecontrol variables, especially prior achievement on students laterachievement (the results of the control variables at the between-students level are not displayed in Table 4).

To summarize the ndings for Research Question 3, teachersaverage expectancies were found to have no association with stu-dents test score or math grade after controlling for students sex,age, gural reasoning score, teachers expectancies at the within-students level, and prior achievement.

4. Discussion

Teachers form expectancies of their students achievements. Ac-cording to the Pygmalion effect, students perceive and react to theirteachers expectancies, and these perceptions and reactions resultin more or less positive learning outcomes, no matter whether theteachers prior expectancies were accurate or not. And indeed, instudying the expectations of 73 teachers and the achievements oftheir fth-grade students, we found that teachers expectancies werepositively associated with students math achievement at the endof the school year.

4.1. Achievement measures

In the present study, we focused on two indicators of studentsachievement, thus allowing us to compare effects. In Research Ques-tion 1, teachers expectancies predicted grades and test scores equally,and the coecients for math grade were slightly higher only on adescriptive level. Regarding the mediation analyses, the direct effectof teachers expectations on student outcomes was again identi-cal and slightly higher for the math-grade outcome than for themath-test outcome. For Research Question 3, results for teachersexpectancies on the within level (those on the between level werenot signicant) were identical. Thus, we found almost identical co-ecients for teachers expectancies on students math grades andon their test scores. As teachers were responsible for reporting thedata used in the study as well as for giving the grades, we may haveexpected higher coecients for the math-grade outcome as foundby Jussim and Eccles (1992), for example.

One explanation for this nding might be that although teach-ers reported their expectancies on students competences and wecould expect that those expectations would be highly correlatedwithstudents actual performance, school grades are not just objectiveindicators of students performance, but they have other func-tions as well (i.e., pedagogical, informational, selection, etc.; Lorenz& Artelt, 2009). Tests have the advantage of focusing more on stu-dents real achievement. However, one-time achievement tests areinuencedmore by situational events during test taking than grades,which incorporate achievement assessments of several occasionsin written and verbal form over a whole school year. All in all, asboth methodsgrades and testshave their strengths and weak-nesses, we proted from combining the twomethods in the presentstudy and suggest combining them in future studies as well.

Regarding the mediation analyses in Research Question 2, wefound signicant results for the outcome grade but not for theachievement test. On a theoretical basis, the teacher common-method aspect mentioned earlier could explain this result. However,as the results were really small, caution is warranted on interpret-ing the difference at all. Future mediation studies should try toreplicate (and maybe extend) the results we found.

4.2. Effects of teachers average expectancies of their students

Because of a lack of research, we were interested in possible as-sociations between teachers average expectancies of the mathcompetence of an entire class and students later math achieve-ment. We wanted to gather new insights into the dynamics ofteacher expectancies on students achievement within a class-room; thus, we separated expectancies on the within-student andbetween-student levels. Our results indicated that teacher expec-tancies of individual students competence but not teachers averageexpectancies of their class competence were positively related tostudents achievement (see Tables 2 and 4). Thus, when control-ling for students sex, age, gural reasoning score, and priorachievement, themagnitude of teachers average expectancies seemsunimportant for their students achievement. There are at least sixpossible reasons for this nding. First, maybe teachers expectan-cies at the between-students level are simply not associated withstudents achievement. The lack of published studies that have takenthis association into account in educational eld studies mightprovide a rst hint. However, further research is needed to evalu-ate this hypothesis. Second, maybe teachers do not form anovergeneralized opinion of their class but rather form a differen-tiated opinion of each of their students. As Brophy claimed: It isnot appropriate to deny important individual differences by tryingto maintain very high expectancies for all students (Brophy, 1983,p. 657). Third, although using an aggregated measure of teachersaverage expectancies is a method that has been used previously (e.g.,Smith et al., 1998), a different approach might be more fruitful. Forinstance, one might prot from using measures that directly tapteachers class impression rather than aggregating teachers ex-pectancies that were formed on the individual level. With such anapproach, it would be possible to detect existing differences betweenteachers impressions of individuals and of the class as a whole. Afourth reason might concern the time frame used: it could be thatthe time period was too short for teachers expectancies to inu-ence students achievement. Teachers form expectancies regardingtheir students already early in the school year (Brophy, 1983). Teacherexpectancies may be based on teachers subjective characteristics,e.g., teachers stereotypes, or on more objective information re-garding students characteristics (Trouilloud et al., 2002). Teacherexpectancies developed over half of the school yearmight be foundedonmore objective information about students. However, teacher ex-pectancies formed earlier may have a greater effect on studentsoutcomes than expectancies assessed mid-half of the school year.

Fifth, maybe the impact of expectancies for a class on the in-structional practices of teachers is smaller than the impact ofexpectancies for individual students. This means that teachersaverage expectancies for a class could be associated with teach-ers general instructional decisions, such as choice of amount of tasks,diculty of tasks, or class homework. However, during instruc-tion in class, teacher expectancies for individual students mightdene the interaction with students, and teachers communica-tion of differential expectations for individual students could be themore important information for students. Thus, in order to moti-vate a class, teachers might convey their high expectation of theclasses abilities, but then turn to individual students and addressthem directly, especially those for whom they have high expectan-cies. Sixth, in line with the fth reason, maybe it is not the existence


of teachers individual and teachers whole-group expectancies perse that have the greatest impact on students self-concept andachievement, but a potential discrepancy between these expectan-cies perceived by students. If students perceive that their teachershold high expectancies for them and for the class as a whole, theremight be no incremental effect of the whole-class expectancies, asthey contain no additional or contradictory information. However,for students who perceive high individual expectancies but lowwhole-class expectancies, or low individual expectancies but highwhole-class expectancies, there might be differential effects of thewhole-class expectancy on their self-concept and achievement.

4.3. Limitations and future research

The present study has several strengths. First, investigating thePygmalion effect in a nonexperimental longitudinal eld study withreal expectancies and real achievement scores allows for high eco-logical validity, which is a precondition for the generalizability ofresults. Second, the separation of teachers expectancies of indi-vidual students competences (the within level) versus theirexpectancies of the class as a whole (the between level) enabledus to examine potential positive and negative effects on studentsoutcomes at both levels. As a preliminary nding for further re-search, the nonsignicant nding of teachers average expectationsneeds to be replicated in additional studies. Third, teachers as-sessed all students in their class rather than a small number of targetchildren as has frequently been the practice in other studies; in ad-dition, we used a relatively large number of teachers. This approachprovided us with a relatively large set of data, and we were there-fore able to make more reliable and valid conclusions.

Nevertheless, the present study still has a number of limita-tions. As we assessed only one teacher per class, no interraterreliability could be calculated. Second, we measured the constructwith a relatively small set of items. However, as teachers had to llout questionnaires at several time points and assess the items forall students in a class, it was necessary to limit the number of items.Third, although we proted from restricting the study to one subjectdomain (i.e., mathematics) by taking the domain specicity of mostmotivational and affective constructs into account (e.g., Bong, 2001;Goetz et al., 2007), the level of generalizability to other subjects needsto be tested in future studies.

Fourth, future longitudinal research concerning teacher expec-tancy effects might also benet from an even longer time periodbetween collecting teacher expectancies and students self-concepts.We chose the time point for gathering teachers expectancies in Feb-ruary, drawing on the literature on teacher expectancy effects toincrease the amount of valid information that teachers had aboutthe verbal and non-verbal behavior of their students (e.g., Kenny,2004; Jussim, 1989). Furthermore, the time lag of February until Julywas chosen due to logistical reasons, as it was the only possibleoption for assessing grades from the same teacher in our sampleof 5th grade students that had transitioned from the elementaryschool to secondary school. So far, it remains unclear which timelag is the most suitable for an adequate measurement of teachersexpectancies. That is, it needs to be studied whether teachers ex-pectancies assessed at the beginning of the school year have greaterpredictive validity for students outcomes than teachers expectan-cies assessed at later time points in the school year.

Fifth, future studies should study if and how teachers commu-nicate the expectancies for whole classes, e.g., by observation classes,by interviewing teachers/students, or by using teacher/student per-ceptionmeasures (for review see Shulman, 1986). In addition, futurestudies could also ask students if they are aware of the expectan-cies their teacher has for the whole class (e.g., My teacher thinksthat this class is capable of achieving good test results) and for in-dividual students (e.g., My teacher thinks that I am capable of

achieving good test results). With this assessment method, the dif-ferential impact of the perceived teachers expectancies forindividuals and the class on individual students outcomes, espe-cially under control of students competences and self-concept, couldbe analyzed. To the best of our knowledge, the studies conductedto date only assess students perceptions of their teachers individ-ual expectancies (Dickhuser & Stiensmeier-Pelster, 2003; Freibergeret al., 2012; Weinstein, Marshall, Brattesani, & Middlestadt, 1982);therefore, examining students view of both teacher expectanciesmight be an interesting addition to current expectancy research. Pos-sible differential and interaction effects of teachers individual andwhole-class expectancies on students outcomes as well as class-room variability in the perception of these expectancies (e.g.,Weinstein et al., 1982) should be examined in future studies.

Finally, we recommend that future studies investigate whetherassessing teachers whole class expectancies directly (e.g., This classis capable of achieving good test results) is better suited to studyclass-level effects of teacher expectancies than using averaged scoresof teachers expectancies for individual students (e.g., This studentis capable of achieving good test results). Future research needsto explore which strategy used to assess the expectancies for wholeclasses yields more insight, especially regarding differences in stu-dents achievement.

Acknowledgments

Alena Friedrich, Barbara Flunger, Benjamin Nagengast, KathrinJonkmann, and Ulrich Trautwein, Hector Research Institute of Ed-ucation Sciences and Psychology, Europastrae 6, 72072 Tbingen,Germany. This research was supported by a grant from the GermanFederal Ministry of Education and Research to Bernhard Schmitz andUlrich Trautwein (01JH0918). Alena Friedrich was a member of theGraduate School Empirical Educational Research, which is sup-ported by the Ministry of Science, Research, and the Arts in Baden-Wrttemberg.

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Pygmalion effects in the classroom: Teacher expectancy effects on students' math achievement Introduction Teachers' expectanciesPygmalion in the classroom The role of different achievement outcomes Students' self-concept as a potential mediator Teachers' expectancies at the class level The present study Method Sample Measures Teacher reports Student self-reports Students' achievement Control variables Statistical analyses Multilevel structure Regression analyses Mediation Measures of explained variance Results Control variables Expectancy effects at the student level (research question 1) Mediation at the student level (research question 2) Expectancy effects at the class level (research question 3) Discussion Achievement measures Effects of teachers' average expectancies of their students Limitations and future research Acknowledgments References

Friedrich, Flunger, Nagengast, Jonkmann & Trautwein (2015). Teacher Expectancy Effects. ContEdPsych

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