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All participants were recruited from Hartford, CT and its outlying suburbs by letters sent home from the school district office following a school choice lottery (see below) in each of 4 years spanning 2010–2013; each participating child was in the study for 3 years, so data collection spanned from fall 2010 through spring 2016. Letters were sent to parents of all 3-year-olds who had been entered in a lottery listing one of two public Montessori magnet schools as their first choice; the letters were accompanied by contact, demographic, and school information forms, a permission letter, and an envelope to return their information to the study coordinator. Parents were sent a $10 gift card as a thank you for returning the information forms. After spring tests each year, children were sent an age-appropriate book and parents were sent a $50 gift card.

The lottery was done by computer at the Connecticut State Department of Education’s Regional School Choice Office in Hartford, CT in May of each year. A child’s parent or guardian had submitted a lottery application during the period spanning October through February, selecting one of the two Montessori schools as their first of five school choices. The lottery selection was random except for neighborhood, sibling, and staff preferences. Staff children were disqualified from the study but 2 study children were admitted to a Montessori via the sibling preference; their siblings had presumably been admitted at random so the latent parent characteristics the lottery was intended to control for were still present. One control child had been admitted to Montessori but did not attend because the parents “did not like the neighborhood the school was in”; all other participants who gained admission to one of the two Montessori schools did become enrolled there. These two siblings and the admitted non-attender were assigned to the school program group they were actually in, but removing the two siblings and placing the cross-over child in the experimental group (“intent-to-treat”) had no meaningfully effect on results. For example, the ANCOVA on Time 4 academic achievement strengthens slightly when these changes are made, from F(2,119) = 7.24, p = 0.008, ηp2 = 0.06 to F(2,117) = 9.58, p = 0.002, ηp2 = 0.08. For philosophical reasons (such as grouping participants according to the treatment actually received) the study’s original group assignment was retained.

Over 4 years, 174 children were admitted to the study; 141 were retained in the final sample. Of these 141, 122 children were tested at all 4 time points, and 19 were tested at 3 time points. Of these 19, one joined the study at Time 2, 2 missed one test session, and 16 moved or crossed over between Time 3 and Time 4. 11 of these were in Montessori and 5 were control children. The control children who were lost had all moved; this lost subset of control children had performed significantly lower in academic achievement at earlier time points than the control children who did not move. The Montessori children who were lost at Time 4 did not significantly differ from those who remained in the study. Thus attrition patterns bias Time 4 results toward better outcomes for the control sample. For the variables reported here and the remaining children, 2.6% of data is missing due to experimenter error, child non-compliance, or interruptions in testing.

Of the 33 children who were admitted but excluded from the study, 23 children contributed insufficient data; 4 of these (2 Montessori) were lost between Times 1 and 2 and 19 (9 Montessori) were lost between Times 2 and 3. The children who were lost did not differ from other children in terms of parent education, parent income, ethnicity, or gender. The decision not to include these children was based on a preference for actual over imputed data. The other 10 excluded children (6 Montessori) had insufficient English (n = 5), speech delay (n = 3), or other learning disabilities (n = 2).

Children’s academic ability was assessed using the Woodcock–Johnson IIIR Tests of Achievement according to the instructions in the manual (Woodcock et al., 2001). Because there were no age differences across samples, raw scores were used for all Woodcock–Johnson tests. The Picture Vocabulary subtest assessed vocabulary, and the Letter-Word subtest assessed reading. Because the Montessori schools both taught cursive letters, the printed letters in the earlier items on the Letter-Word subscale were overlaid with cursive letters when testing Montessori students. Ordinary print letters were retained from the point when the test changes from letter to word identification. Early mathematical achievement was measured with the Applied Problems subtest, followed by the Calculation subtest if children scored 19 points or higher. These scores were summed for a Math score. The Math, Letter-Word, and Picture Vocabulary score loaded on a common factor (see Appendix) and were highly correlated (rs > 0.80), so to reduce the number of comparisons in the study, these scores were combined (by adding Z-scores) for an overall Academic Achievement measure (e.g., Lipsey et al., 2017).

We used four tasks from the Theory of Mind Scale (Wellman and Liu, 2004) omitting the lowest level (Diverse Desires) for brevity since 3-year-olds typically pass this level. As an example, in the Knowledge Access task, children were shown what was hidden in the drawer of a doll-house-sized bureau, and then shown a doll who they were told had not seen inside the drawer. They were asked if the doll knew what was inside the drawer, and if the doll had seen inside the drawer; both answers had to be correct for a child to be given credit. Children were given Knowledge Access first, followed by Contents False Belief, Diverse Beliefs, and Hidden Emotion, for final scores of 0–4. The contents, dolls, and doll names changed for each test session. For example, for contents false-belief task, one year the child saw a Band-Aid box with crayons inside, another year a raisin box with buttons inside, another year a Crayons box with rubber bands inside, and another year a Cheerios box with beads inside. Since children entered the study for four consecutive years, each material set came first for a portion of the sample.

One object acquisition story from Rubin’s Social Problem-Solving Test - Revised was administered (Rubin, 1988) each year. In these stories, children were shown two other preschoolers, one of whom had a coveted resource like a swing and had had it for a “long, long time” and the other of whom wanted that resource. Children were asked what the second child could do or say to get the resource, what else they could do or say, and what the child him- or herself would do or say. Children’s use of strategies considering fairness and justice for both parties were coded. Although there is no limit to the number of such solutions a child might give, in reality the range was 0–3 at all four test points. Interrater reliability on 20% of all responses across all years was 0.99.

Executive Function was assessed with two tasks. For Head-Toes-Knees-Shoulders (Ponitz et al., 2009), children were first asked to touch their head, then to touch their toes. Children were then told that they were playing an “opposite game” in which they must touch the opposite part of the body than the experimenter said. Children were then administered 10 items, each scored 0–2, with 0 indicating the child followed the command literally, 1 meaning the child touched the incorrect body part first and then corrected themselves without prompting, and 2 meaning the child touched the correct (opposite) body part. If a child scored 10 points or more on the first 10 items, a second series of 10 items was administered which included knees and shoulders; the maximum points a child could earn was 40.

Second, the Design Copy subtest from the Visuospatial Processing section of the NEPSY-II was administered and scored according to the manual (Korkman et al., 2007). Children were shown a paper with a 4 × 4 grid with four figures across the top and third rows. The first figure was a vertical line; the experimenter showed children how to copy the line in the box below it (first box, second row), saying (for 3- and 4-year-olds), “See this line? I will draw one here. Now you draw one here,” handing the child the pencil and pointing to the second figure (a horizontal line) and the box below it. For 5-year-olds, and for the remaining items, the experimenter simply pointed to the top figure then the blank box below it, saying, “Copy this one here.” This continued for up to 16 figures until a child failed to successfully copy three figures consecutively. An independent coder coded a randomly selected subset of children at each test period, and interrater reliabilities across the two coders were excellent: rs = 0.98 (32 children at Time 1); 0.96 (22 children at Time 2); 0.95 (14 children at Time 3); 0.90 (22 children at Time 4).

The puzzle task (modified from Smiley and Dweck, 1994) designed to test mastery orientation was given in two parts. First, children were given a fairly easy puzzle for their age, along with a picture of what the completed puzzle should look like. The picture was turned over while children solved the puzzle. After 2 min or when children completed the puzzle (whichever occurred first), they were given a much more difficult puzzle to solve and its completed picture which was then turned over. However, in this puzzle there were also pieces that had been switched with a similar puzzle, rendering the puzzle unsolvable. Children were again given 2 min to work on the puzzle. Then they completed several other tasks, and finally the experimenter brought out both puzzles again, told children that they had some extra time, and asked which one they wanted to work on and why; children could opt for neither or the easier puzzle (scored 0), or the more difficult puzzle (scored 1).

A questionnaire was developed to assess children’s enjoyment of academic (school and reading) and leisure (media and play) tasks; four filler questions were included as well. There were four questions about each of the focal topics, and children rated their enjoyment by pointing to a sad, neutral, or happy face. These responses were coded as 0, 1, or 2, and added together. Since young children often give the highest possible ratings on such scales (Ladd et al., 2000), to get variability, responses at the end of each school year (so they had experience with the school tasks) were summed, and liking for academic tasks was subtracted from liking of recreational tasks, reflecting how much more each child liked recreational than scholastic activities across preschool.

Alternative Uses was used to assess creativity (Guilford and Christensen, 1973). First, as a warm-up, children were shown a photograph of an object (e.g., a pencil) and the experimenter said, “See this? This is a pencil. Can you tell me as many different things that you can think of that you can do, play or make with this?” If children made no reply in 10 s, the experimenter prompted with one use. The first of two test items was presented in the same way (“See this? This is a bucket…”). Responses were recorded for 1 min, with the experimenter prompting “What else?” If a child was producing responses and then appeared to run out of ideas (did not respond for a few seconds), the second item was shown and the same process repeated. For both test items the total time during which responses counted was 2 min; responses given after 2 min were not included.

Each intelligible response was scored as standard or non-standard. Categories were exclusive. For example, a standard use for a towel would be to wipe one’s body, and a non-standard use would be to place it over one’s head to pretend that one is a ghost. Analyses were conducted on the number of non-standard uses each child gave, collapsed across both items at each assessment. The actual range of responses was 0 to 5 total non-standard uses. Two coders independently coded a randomly selected subset of the data (ns below). Reliability was r = 0.80 on 16 children who were double-coded at Time 1; 0.73 (45 children at Time 2); 0.79 (46 children at Time 3); 0.82 (40 children at Time 4).

Although equal at the start of school, the Montessori group advanced at a higher rate across the study years, as illustrated in Figure 2; ΔB = 0.13 (SE = 0.067), p < 0.05. This initial analysis did not control for demographic variables because there were no differences, as would be expected given random assignment, but to confirm this a second growth model was created controlling for gender, household income, and Time 1 executive function. This confirmed that while both groups were equal at intercept in academic achievement, Montessori predicted a steeper slope of growth, whereas none of the control variables predicted a steeper slope in the overall sample. The result from the growth curve analysis was confirmed by an ANCOVA on Time 4 academic achievement, controlling for academic achievement at Time 1, F(2,119) = 7.24, p = 0.008, ηp2 = 0.06. Independent samples t-tests showed that the groups were not yet different at Time 1 or Time 2, and that significant differences in academic achievement had emerged by the last two time points (approximately 4 and 5 years of age): t(136) = 2.10, p = 0.04, Cohen’s d = 0.36, and t(122) = 2.26, p = 0.03, Cohen’s d = 0.41, respectively.

Although children’s scores were equal at the initial test, a linear growth curve model showed that Montessori children had a significantly steeper rate of growth across the preschool years, ΔB = 0.10 (SE = 0.04), p < 0.05. This result remained in a second growth curve model that controlled for age, household income, and Time 1 Executive Function. Using a different analytic approach, an ANCOVA on Time 4 Theory of Mind scores controlling for Time 1 scores also showed a significant difference favoring the Montessori group, F(2,115) = 4.47 p = 0.04, ηp2 = 0.04. Scores were examined at each time point. For Times 1 and 2 the two groups were not different. At Time 3, the difference was significant, t(135) = 2.09, p = 0.04, Cohen’s d = 0.36, and at the end of kindergarten (Time 4), the difference was a trend, t(122) = 1.74, p = 0.08, Cohen’s d = 0.32. These results show that social cognition developed more rapidly in children attending Montessori schools.

Children in the two samples were equivalent throughout the study with respect to their social problem-solving skills; the average number of justice-related responses ranged from 0.24 to 0.97 across the 4 time points. An ANCOVA on Time 4 Social Problem Solving controlling for Time 1 comparing Montessori and control samples was non-significant F(1,117) = 0.20 p = 0.66, ηp2 = 0.002, nor was the group difference significant at any time point with independent samples t-tests.

Linear growth curve analyses did not indicate differences in the growth of executive function. An ANCOVA on Time 4 executive function controlling for Time 1 only showed a trend toward a difference, with Montessori children scoring more highly: F(2,118) = 3.00, p = 0.09, ηp2 = 0.03. Only at Time 3 was the difference significant, t(135) = 2.09, p = 0.04, Cohen’s d = 0.35. Evidence that Montessori magnet preschools lead to better executive function as compared to that developed by control children attending other preschools is not strong here.

At the first two time points, there were no group differences: 37 of 70 Montessori (53%) and 35 of 71 control children (49%) chose to try a difficult puzzle again on one or both occasions (Fisher’s Exact test, p = 0.74). By the time children were 4 and 5, at Times 3 and 4, school program effects were significant, with Fisher’s Exact test showing more Montessori children made the mastery choice (45 of 69 or 65%) than did control children (33 of 71 or 47%), p = 0.03, two-tailed. Thus, children who were randomly assigned to a Montessori program were more likely to have a growth mindset by the latter half of their preschool years. Children’s explanations for their choices were consistent with the underlying orientation. Easy puzzle choosers said things like, “Because it’s easier,” whereas difficult puzzle choosers said things like, “Because I think I can do it.”

An ANOVA showed that the Montessori children were relatively more positive about school-related activities than were the control children, F(1,116) = 5.69 p = 0.02, ηp2 = 0.05 (see Figure 3). This suggests that the Montessori children’s achievement gains were not at the expense of their enjoying school.

Children in the two samples were equivalent throughout the study with respect to their creativity; average non-standard uses scores ranged from 0.31 to 1.55 across the 4 time points. An ANCOVA on Time 4 Creativity controlling for Time 1 Creativity comparing Montessori and control samples was non-significant F(1,94) = 0.96 p = 0.33, ηp2 = 0.01, nor was the group difference significant at any time point with independent samples t-tests.

Income is typically associated with school achievement. This was the case in the control sample, as shown in the right hand side of Figure 4 using data from the final test point (Time 4). The left hand side shows this relation for the Montessori sample. Among children in Montessori, the correlation between academic achievement and household income across the entire study was 0.23, whereas in the control sample it was twice that: 0.46. Using the Fisher transformation, this difference in correlations was significant, Z = 2.46, p = 0.01. To further examine this, 1000 bootstrapped samples were generated; the 95% bootstrap confidence intervals of Δr was (0.04, 0.39), supporting that the correlations between income and academic achievement in the two samples are significantly different. The smaller correlation among Montessori children might be a simple function of their being in magnet schools, since this is in essence the point of magnet schools [although their success at this is mixed (Ballou, 2009)]. However, for the subgroup of 15 control children who were at other magnet schools, the correlation between academic achievement and household income was even stronger, suggesting the mitigated income-achievement correlation for Montessori children is not merely due to their being in magnet schools.

How strong the gains in academic achievement were among just the lower income children is also of interest, because of the income achievement gap. Although the income range was very broad, there was not a sufficiently large subsample to only examine those living below the poverty line, so instead we examined the study subsample with a household income below the median split. For this lower income half of the sample (n = 67), mean household income was $32,627; SD = 18,443; the federal poverty line for a family of 4 in Connecticut was $24,600. At Time 1, an ANCOVA on academic achievement controlling for age (because there was a slight age difference in the subsamples), showed no difference between the Montessori and control lower income subsamples, whereas by Time 4 the lower income Montessori subsample had significantly higher academic achievement than the lower income control subsample, F(1,62) = 6.86, p = 0.01, ηp2 = 0.10; see Figure 5. This result also held when controlling for Time 1 academic achievement: F(1,61) = 7.25, p = 0.009, ηp2 = 0.11.

Furthermore, Montessori education greatly reduced the achievement gap across the preschool years. A series of four t-tests compared the lower income Montessori children with the higher income control children at each time point. For the higher income half of the sample (n = 74, including 7 at the median income of 70,000), mean household income was $105,804; SD = 33,123. The higher income control children outperformed lower income Montessori children at Times 1 and 2, t(64) = 2.47, p = 0.02, Cohen’s d = 0.61 and t(61) = 2.43, p = 0.02, Cohen’s d = 0.61, respectively. At Time 3, the difference was reduced by a third in terms of effect size and was no longer significant, t(62) = 1.59, p = 0.12, Cohen’s d = 0.40, and by the end of kindergarten (Time 4), the difference was reduced by yet another third, t(62) = 1.59, p = 0.41, Cohen’s d = 0.21. Thus, the effect size of the income achievement gap went from 3/5 of a standard deviation at age 3, to 2/5 at age 4, and finally to 1/5 at the end of the 3rd year in Montessori. Within the Montessori sample, the same series of tests showed trending (p = 0.06 at Time 1) or significant income-group differences in academic achievement at the first three time points but not at the last one, t(56) = 1.41, p = 0.16, although the difference was still a third of a standard deviation in size, Cohen’s d = 0.37. By contrast, within the control sample, the higher income subgroup performed a full standard deviation better than the lower income subgroup, Cohen’s d = 0.98. The higher income Montessori children were the highest performers in the study by the end of kindergarten (Time 4, see Figure 5), but the lower-income children were doing much better in Montessori classrooms than in control schools by this last time point.

Second, we examined the predictive power of executive function for achievement. For both Montessori and control children, higher executive function predicted academic achievement at Time 1 (the intercept). In the control sample, as expected from many studies, executive function also predicted the slope of academic achievement in the latent growth curve model, ΔB = -0.067, SE = 0.03, p = 0.05. By contrast, initial levels of executive function had no influence on the slope of academic achievement for children in the Montessori programs, ΔB = 0.009, SE = 0.03, p = 0.76. Thus, in terms of academic outcomes, in Montessori classrooms children with low executive function do as well as children with high executive function. In other words, special supplementary curricula targeting executive function are not needed to equalize achievement outcomes for children in Montessori programs; academic achievement was higher overall, and children with lower executive function were not at a disadvantage.

First, one might ask whether the standards for entering a program to be a Montessori teacher are higher. Most of the Montessori teachers in this study trained at the AMI teacher training center in Hartford. Up to the time of this study, the training center courses were usually undersubscribed, so the center took virtually all applicants (Hall, personal communication, June, 2017). In addition, virtually all those who take the 9-month course are awarded a diploma. However, it is feasible that people who are attracted to Montessori teacher training interact differently with children, and this difference could be responsible for the results obtained. Other studies have shown non-trivial teacher effects at preschool. For example, a large study of prekindergarten classrooms in states that support pre-K (as does Connecticut) indicated two teacher variables that are most predictive of child achievement (Mashburn et al., 2008): (1) teacher emotional support, which predicts social outcomes and (2) teacher instructional support (asking high-level questions, scaffolding children’s thinking), which supports academic outcomes. It is possible that the Montessori teachers were higher on these variables even prior to their Montessori teacher training. Further research should examine this, perhaps through questionnaires given to people commencing Montessori vs. conventional teacher education programs.

Second, the teacher training for Montessori might create better teachers. In terms of time and course intensity, the AMI training seems comparable to the training required for an early childhood teaching certificate. It involves 9 months of lectures and practice teaching, creation of a set of notes explaining Montessori theory and curriculum, and a final examination. The AMI “professors”—the people who teach the teacher-trainees—typically had at least 5 years as an AMI-certified classroom teacher followed by about 7 years of apprenticeship to another teacher trainer, so they are also highly trained. However, one difference to early childhood education is that in Montessori teacher training courses, one focuses on just one system and theory (Cossentino, 2005). By contrast, teachers in conventional teacher education programs typically learn many theories and methods. Whether learning a single theory or multiple ones creates better teachers is an empirical question.

Another possibility, which also needs to be studied, is that Montessori teacher training changes teachers, perhaps by making them more sensitively responsive or higher in instructional support. If this is the case, then Montessori teachers are different but for a reason that is generic to Montessori education. Throughout Dr. Montessori’s books, a warm and loving attitude to children is expressed, and Montessori teachers are expected to come to embody this attitude (Lillard, 2017). In addition, Montessori teachers adopt high expectations of children, for example expecting them to achieve independence in ways that people rarely expect at least in American culture today. Even before age 3, Montessori children are expected to set the table, prepare a meal, and clean up, for example. Five-year-olds multiply and divide 4-digit numbers [see Figure 6; Montessori (2016) describes how this is achieved in high-fidelity Montessori classrooms], and carry out other complex tasks on their own. The combination of warmth, trust, and high expectations that is imparted to teachers during the Montessori teacher training might change them in ways that would make their students have better outcomes even if the teachers did not go on to implement a Montessori curriculum.

Various means should be used in future studies to look at the degree to which teachers might be responsible for better outcomes in Montessori education. First, one could examine attitudes toward and interactions with children prior to, during, and following teacher education courses, comparing those in Montessori and conventional training, to see how each type of teacher training changes people. Second, measures of teacher–child interaction could be used in studies like this, and entered as separate predictors in regression models, to see whether teacher interaction style in Montessori loads as or more strongly on outcomes than it does in studies of conventional teachers, for example using the CLASS (Pianta et al., 2012).