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Running head: Literacy Development and the Turno Escolar

A Mixed Method Study of 3rd Grade Literacy Development

in Mexico and the Influence of the Turno Escolar

Bryant T. Jensen

Arizona State University

DRAFT, March 2008

Paper presented at the 52nd Annual Meeting of the Comparative and International

Education Society (CIES) at Teachers College, Columbia University, New York City.

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Abstract

As equitable educational opportunity remains an important element of development in

Mexico, this study uses multiple methods to assess ways in which student, classroom,

and school variables influence literacy learning (or reading) for third grade students

across states (including the Federal District). In this paper I combine case study data

with descriptive statistics (frequency assessments, means, standard deviations, and

mean comparisons) and a series of hierarchal linear models to evaluate the influence of

the turno escolar (i.e., school shift) on student literacy learning. More specifically, the

influence of the turno escolar is assessed along with the modalidad escolar as school

effects on literacy learning, and the difference of such effects across states, are

evaluated. Limitations to the dataset and relevant recommendations for further research,

policy, and practices are offered.

Keywords: mixed methods; primary schooling; literacy development; school shift

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Introduction

Considered a wealthy developing (or newly industrialized) country, one of Mexico’s

greatest challenges to entering the developed world and the 21st century is extending

equitable educational opportunities to its large child population. In more specific terms,

this means improving student learning opportunities and outcomes for diverse segments

of the Mexican child populations (Vegas & Petrow, 2007). While gross economic gains

in Mexico are moving forward through free trade agreements and increased

involvement in the global market; if such are not accompanied with thoughtful

initiatives designed to improve social opportunities (such as education and health

care)—including their fair distribution among the Mexican public—national

development will be stifled (Fägerlind & Saha, 1989; Sen, 1999). Thus, equitable

educational opportunity remains an important element of development in Mexico.

Certainly the role of education in the development of economies and societies

remains a highly disputed topic within academic circles (Adams, 2001; Carnoy, 2006;

Epstein & Carroll, 2005; Fägerlind & Saha, 1989; Kubow & Fossum, 2003; Wigley &

Akkoyunlu-Wigley, 2006). Disagreements between the grand theories concerning

education in development—i.e., structural functionalism, Marxism, and

postmodernism—are attributable to ideological differences and mixed reactions to

policy reforms concerning third world development following World War II (Epstein &

Carroll, 2005). While none of the views disregard the importance of education in

development, they dispute the construction of education, its purposes, its distribution

among diverse peoples, viable definitions of “development”, and meaningful ways to

study education in the process of development. These topics will likely be sources of

debate for years to come (Adams, 2001).

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My view, and the conceptual backdrop of this paper, is that elements from each

of the major theoretical orientations can converge to account for both local and general

phenomena related to education in development. This is the lens I use to approach

conceptual concerns related to educational development of children in Mexico. Of a

matter of course, my view discounts certain principles held by each of the three grand

theories while acknowledging the enduring value of others. As an introductory measure,

I put forth three theoretical assertions concerning the role of education in development.

First, contrary to the structural-functional approach, the function of education in

society cannot be unitary. Skills emphasized in rural Oaxacan schools, for example,

differ from the curriculum at an inner-city school in Mexico City. Course content,

appropriate behavior, and institutional processes are dependent on the histories and

values held within each society, or subgroups within that society (Valenzuela &

Valenzuela, 1998). The issue of “what should be learned” is often an on-going struggle

between local political and market-driven forces particular to each society (Carnoy &

Levin, 1985). Learning is purposeful, yet the process by which that purpose is

determined and its eventual outcome vary across societies, as argued by several neo-

Marxists (Kubow & Fossum, 2003).

Second, increased educational opportunity must be viewed as more than a path

to economic well-being. While sophisticated economic analyses in recent years provide

convincing evidence on ways in which educational attainment (and more recently

academic achievement) is directly and indirectly associated with the economic

prosperity of individuals and societies, education is more than a means to capital (Sen,

1999). It has, for example, the potential of decreasing social inequality through the

provision of equitable learning opportunities (Reimers, 2000). Equally distributed

student learning within societies is vital to democracy and fair economic opportunity.

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So while the relationship between educational advancement and economic development

within societies is important, the development of education should not be viewed solely

in these terms. It should also be viewed as end of development, not merely a means

(Sen, 1999; Wigley & Akkoyunlu-Wigley, 2006).

A third and final theoretical assertion is that classroom processes are central to

our understanding of education in development. This claim highlights an important

shortcoming of structural-functional and Marxist frameworks: their inability to

adequately describe, analyze, and interpret processes within the classroom. A theory of

education in development should be conversant in curriculum, instruction, as well as the

social and cognitive development of the child. This is especially true if educational

research is to have practical relevance (Reimers & McGinn, 1997).

As perspectives in international development increasingly shift their focus from

economic indicators (e.g., per capita income, GDP, income distribution) to indices of

human development (e.g., disease risk, literacy, life expectancy, health, maternal

survival) to evaluate the progress of developing and underdeveloped nations (Sachs,

2005; Sen, 1999; United Nations Development Programme, 1997, 2000), there will be a

stronger demand for applied (and creative) work in the social sciences. In terms of the

role of education in development, the Human Developmental Index (HDI)—currently

the foremost instrument used to measure and compare human development between

nations (Chatterjee, 2005; Sen, 2000)—considers educational attainment, yet does not

seek to define educational quality or the processes that influence it.

As student learning will become an increasingly important aspect of

development in developing nations (particularly in Latin America [Vegas & Petrow,

2007]), this study analyzes the literacy performance of a nationally representative

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sample of Mexican children enrolled in third grade during the 2005-2006 school year, in

combination with case studies of three children attending different schools in Mexico.

The purpose of this study is to evaluate the influence of el turno escolar (the school

shift) on student learning nationally. Before discussing the methods and results of this

study, I offer a short literature review presenting a conceptual approach to student

learning in development, and a brief synopsis of what is currently known concerning the

sources of student learning inequality in Mexico’s basic education system (i.e., grades

1-9) at the individual, classroom, school, and state levels.

Literature Review

Psychologists, linguists, sociologists, anthrolopologists, and other social scientist

have sought for several decades to define quality in education, and to research the

intersecting factors that influence it. In this study I conceive student learning and its fair

distribution as quality—that the social and cognitive processes through which

knowledge is encoded and retained by individuals, and then equally distributed among

the student population, constitutes an important element of societal development.

Certainly the intersecting individual, social, and cultural aspects influencing

student learning are complex. And the common input-output models developed by

economists to evaluate causal relations (Glewwe & Kremer, 2006) do not do justice to

the convoluted nature of the phenomena. While clear and precise analyses producing

understandable and relevant results for practitioners and policymakers are critical

(Reimers & McGinn, 1997; Rosekrans, 2006), such efforts can be thwarted by

simplified models that disregard elemental processes which shape learning.

A practical way to approach student learning is by understanding the multiple

levels that influence its development (Bray & Thomas, 1995). This means that in

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addition to considering individual characteristics (e.g., motivation, attention, cognitive

processing, personal background), processes within the classroom, school, and state are

evaluated simultaneously. This “multilevel” approach is particularly useful when

making comparisons between groups, societies or nations (Bray & Thomas, 1995). It

conceives the effects of the school on student learning to be influenced by educational

policies and practices broadly (e.g., within districts and/or states), and that student

development and learning potential depend not only on an individual’s background and

personal attributes, but also classroom processes which are often shaped by school-level

policies and practices.

The role of classroom processes on student learning has been entertained

extensively by education researchers. Over the years this work has provided ample

evidence, for example, that the amount of time students are exposed to curriculum (i.e.,

time on task) can increase learning (Karweit, 1982; Reimers & McGinn, 1997) and that

educational policies to increase time on task, in turn, improve achievement scores

generally. However, this represents only an initial step as students benefit differentially

from increased time on task. Much is yet unknown concerning the best use of time and

differential curricular and instructional strategies needed to produce greater learning

benefits for students from diverse backgrounds (Miller, 1995; Portes, 2005), and how to

optimally train teachers to bring that about (Reimers & McGinn, 1997).

In Mexico, extensive work has been done in recent years to evaluate certain

school and state effects on student learning. A recent study by Backhoff, Andrade,

Sánchez and Peon (2007), for example, found that third grade students’ scores in

science, reading literacy (or Spanish), civic education, mathematics, geography, and

history varied substantially by school modality, a variable used in Mexico to

differentiate between five school types: private, urban public, rural public, indigenous

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education, and community courses. On average, students attending private schools

performed better in all areas compared to students in other modalities, followed by

urban public, rural public, and students in indigenous schools who demonstrated the

lowest mean performance across subjects. Surprisingly, though students enrolled

community courses (or cursos comunitarios—schools operated by the Consejo

Nacional de Fomento Educativo and located in remote locations where regular

education services do not exist) had been found in previous studies to demonstrate

among the lowest achievement patterns in sixth and ninth grade (Backhoff, Andrade,

Sánchez, & Bouzas, 2006), Backhoff, Andrade, Sánchez and Peon (2007) found their

third grade literacy and mathematics scores to be quite high, second only to children in

private schools. Perplexed at this finding, authors speculated that the unexpectedly high

scores of students attending cursos comunitarios (less than one percent of the third

grade student population in 2006) were possibly due to sampling problems or improper

test administration. Where little evidence is available to support such hypotheses,

however, the surprisingly high average performance of third grade students must be

considered representative.

In this same study, the effect of school modality varied across states. That is, the

distribution of third grade students by modality and the size of the learning difference

between schools fluctuated from state to state (including the Federal District). In

literacy learning, for example, students demonstrated state-to-state differences as large

as a half of a national standard deviation, whereas many state averages were not

statistically different from the national mean (Backhoff, Andrade, Sánchez & Peon,

2007).

In Mexico, less is known about the role of student characteristics on academic

learning. Again, most of the nationally representative work focuses on the school level

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and the state effects on student learning (Backhoff, Andrade, Sánchez, & Peon, 2007)

though some recent work by the Instituto Nacional para la Evaluación de la Educación

(INEE) assesses the influence of social inequality of family backgrounds (Backhoff,

Bouzas, Hernández, & García, 2007). This study by Backhoff and colleagues found that

students (in 3rd and 9th grade) varied greatly in terms of parent education, parent

expectations of educational attainment, amount of educational resources in the home,

and other home factors shaping the “cultural capital” related with educational success.

Children were not distributed equally among schools, but were highly clustered into

schools with other students who had similar “cultural capital” profiles. Some clustering

was found at the state level. These conditions in the home accounted for most of the

school and states effects on math and reading literacy outcomes, and some 4 to 5

percent of the student effects.

Because the individual level accounts for more variation in student learning over

and above the school and state (Backhoff, Bouzas, Hernández, & García, 2007),

theoretical and empirical work assessing individual factors (and their clustering with

schools and states) will continue to be paramount to improve educational opportunities

for Mexican children. Moreover, the classroom (often the “black box” in nationally

representative research) is an indispensable level of analysis to educational

improvement (Reimers & McGinn, 1997). Much of the individual variation accounting

for student learning is clustered within the classroom—attributable to instructional

practices and other classroom processes.

As mentioned previously, this study uses INEE data, informed by preliminary

results from three case studies, to assess the influence of the school shift on literacy

learning for third grade students in Mexico. Case study data, including classroom

observations, and interviews with students, family members, teachers, and peers, are

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used to problematize, direct, and inform quantitative analyses. This is done as a way to

understand not only the general influences el turno escolar has on literacy learning, but

also to develop hypotheses concerning ways in which it influences classroom-level

processes and, therefore, student learning.

My interest in the school shift arose from a qualitative study I conducted in

2004-2005 in which I observed classrooms and interviewed school staff, students, and

parents at urban primary schools in Mexico as a way of understanding educational

practices in schools and classrooms (Jensen, 2005, 2008). One discovery from this study

was that many children attended schools in the afternoon while most children attended

during the day (i.e., the morning shift). Apparently this practice has grown as the

Mexican government has sought to expand school access to traditionally marginalized

populations without necessarily building more schools.

Teachers and other school staff I interviewed perceived certain differences

between shifts. In terms of the children, they mentioned that those attending afternoon

shifts (i.e., el turno vespertino) had greater economic disadvantage, less academic skill

development, and were less engaged in school curricula in comparison to those

attending morning shifts (Jensen, 2005). Moreover, according to teachers interviewed,

the parents of vespertino children were less involved in school functions and

demonstrated more domestic problems than parents of matutino (i.e., the morning shift)

children. Finally, interviewees also asserted that these perceptions of children and

families within shifts were common among teachers and, as such, teachers typically

help lower academic expectations of students attending afternoon sessions.

To my knowledge, the generalizability of these notions has not been evaluated

with a national sample of children. While I do not pretend to get at each of them in this

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study, I use data from a national sample of third grade students, in combination with

three case studies, to assess how the school shift influences literacy development.

Methods

Using multiple methods in educational research is vital if we are to understand the

complexity of the phenomenon under study (Smith, 2006). Yet combining methods also

introduces difficult challenges concerning study designs and procedures, especially

when multiple method studies use concurrent rather than sequential designs. In the

present study I combine case study data (most of which is qualitative—data from

interviews and observations) with quantitative assessments of student learning to

understand ways in which the school shift influences literacy development nationally

among third grade students. More specifically, preliminary findings from case study

data analysis led me to consider school modality as an additional school-level co-

variate—i.e., in addition to the school shift—influencing literacy scores. Moreover, a

hierarchical linear model was eventually developed to test the nesting of these school

effects within states, as well as individual achievement variation within schools.

Four months before looking at the quantitative data at INEE, I sampled three

students within different school types in Mexico. Because managing educational

bureaucracies in Mexico can be daunting (Jensen, 2005), I realized I would need an

advocate—someone who knew someone who worked at the school—to be granted

access and support from school staff to carry out my study. After using my contacts to

the best of my ability, and after nearly 2 months of working hard at it, the primary

school sites were selected: a rural public school in Puebla, a private school in Mexico

City, and a urban public school in Morelos. Within each of these schools I then used

third-grade students’ grades in español (given by teacher, on a scale of 1 to 10) to select

“at random” a child who demonstrated “average” performance in class—i.e., neither

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high nor low scores. Thus, a male student was selected in Puebla, and females in

Morelos and Mexico City, as my case study participants.

Before entering the classroom to gather observational data concerning students’

behaviors, teachers’ instructional strategies, and classroom activities, I decided to

interview school staff further about the school shift. Semi-structured questions were

posed, which resulted quickly in an important consideration—private schools rarely

have shifts, and afternoon shifts in rural communities are diminishing as migration from

rural to urban sectors increases. Were the school shifts, therefore, concentrated in urban

public schools nationally? This finding from interviews with school staff made me

decide to include school modality in my evaluation of el turno escolar on student

literacy learning.

As I continue to gather case study data on these students, much of that data has

yet to be analyzed. This includes information not only on classroom processes

associated with student learning, but also descriptions of home activities and

relationships, as well as the local histories of the schools and communities within which

students learn. These data will certainly be useful as I continue to grapple with the

complexity of student literacy learning, and the contextual features shaping differential

outcomes.

Quantitative analyses assessing the influence of student literacy learning

presented in this paper, therefore, consider the influences of the school shift, school

modality, and state (including the Federal District). All data were taken from the INEE

Excale literacy exam, which assesses reading comprehension and linguistic reflection,

including knowledge of grammar, spelling, and punctuation (Backhoff, Andrade,

Sánchez, & Peon, 2007).

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The sample of students (n = 16,563) was determined through a stratification of

school modalities within states, and the random selection of students within selected

schools. Though information in the database was provided concerning the school shift

(i.e., matutino, vespertino, nocturno, discontinuo, or mixto), the sampling was not

conducted to be representative of students within turnos escolares. Thus, the

distributions of the weighted sample compared the population of students enrolled in

different school shifts within states were disparate. The original student weight,

therefore, was adjusted to account for school shift distribution (using a dichotomous

shift variable—i.e., matutuino, no matutino) within states.

The remainder of this study presents a series of descriptive statistics as well as a

hierarchal linear model to assess to influence of the school shift, the school modality,

and states on student literacy learning. For each of the subgroups considered, sample

sizes, frequencies, means, and standard deviation estimates (with their respective

standard error scores) were analyzed, as shown in the appended tables. Means and

standard deviations for subgroups with sample sizes less 30 students were suppressed

due to their questionable psychometric properties and the inflated standard errors

associated with computed estimates. Two-tailed mean comparisons using 95 percent

confidence intervals were also conducted to compare the statistical significance of

children attending matutino versus non-matutino schools within school modalities and

states. SPSS analysis software was used for data cleaning and organization and SAS

was used to compute estimates. In each case, the macros and student sample weights

provided by the INEE were applied to produce the reported estimates of plausible value

scores for each group.

A series of three-level hierarchal linear models were run to evaluate the nested

contributions of school modality and school shift (and their interaction) within state on

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student literacy scores, as well as the individual variation within school effects

(Raudenbush & Byrk, 2002). First, a null model was analyzed using HLM6 software to

determine how of much of the Excale reading literacy score variation of third grade

students with schools was accounted for at the student level, and how much of the state

variation was accounted for at the school level. In each HLM model, Level 1 was the

student level, Level 2 schools, and Level 3 states. Each model is described below.

Null Model

Level 1: Yij = π0j + eij

Level 2: π0j = ß00j + r0j

Level 3: ß00j = γ000 + u00

In the null model, Yij is equal to the individual literacy score of student i within

school j in the first level, while π0j is the intercept, defined as the expected literacy

achievement of students generally. The error term, eij, represents a unique effect

associated with person i attending school j. In the second level, ß00j is the intercept or

mean literacy achievement for urban public schools, which were coded as the base

category in each of the three HLM models. The final element at the second level, r0j, is

the random error in reading scores at the school level. At the third level, γ000 is the grand

mean of student reading achievement and u00 is the random error at the state level.

In the next model, identified as Model 1, one school-level predictor was added to

the null model to assess it’s unique influence on student literacy scores, and the variance

of this effect across states. The equations for this model are presented below.

Model 1

Level 1: Yij = π0ij + eij

Level 2: π0j = ß00j + ß01 j *(matutino) + r0 j

Level 3: ß00 j = γ000 + u00

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ß01 j = γ010 + u01

Like the null model, Yij is equal to the individual literacy score of student i within

school j in Model 1, while π0j is the intercept, defined as the expected literacy

achievement of students in general. And eij represents a unique effect associated with

person i attending school j. At the second level, again, ß00j is the intercept or mean

literacy achievement for urban public schools. ß01 j is the regression coefficient

associated with no matutino schools (including vespertino, nocturno, mixto, and

discontinuo shifts), which were coded “0”. Matutino schools were coded “1”. r0j is the

random error in reading scores not accounted for by the school shift effect. Finally, γ000

is the mean of student reading achievement respective of the model (i.e., urban public

schools), u00 is the random error at the state level, γ010 is the average difference between

matutino and urban public schools, and u01 is random error in at the state level.

In the final model, Model 2, school modalities were combined with the school

variable at the second level to determine their combined effect (including interactions of

random effects) on the literacy scores of third grade students in Mexico. Equations

associated with this model are presented below.

Model 2

Level 1: Yij = π0ij + eij

Level 2: π0ij = ß00j + ß01 j *(rural) + ß02 j *(edu ind) + ß03 j *(cc) + ß04 j *(private)

+ ß05 j *(matutino) + ß06 j *(rural_matutino) + ß07 j *(edu ind_matutino)

+ ß08 j *(cc_matutino) + ß09 j *(private_matutino) + r0 j

Level 3: ß00 j = γ000 + u00

ß01 j = γ010 + u 01

ß02 j = γ020

ß03 j = γ030

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ß04 j = γ040 + u 04

ß05 j = γ050 + u 05

ß06 j = γ060

ß07 j = γ070

ß08 j = γ080

ß09 j = γ090

The level-1 equation is repeated from the previous two models. At the school level, ß00j

is the intercept or mean literacy achievement for urban public schools associated with

the model, and ß01 j-04 j are the regression parameters associated with school modality

categories. ß01 j is the regression coefficient associated with rural public schools where

rural public schools were coded “1” and all other school modality categories were coded

“0”; ß02 j is the regression coefficient associated with indigenous education schools

where indigenous schools were coded “1” and all other school modality categories were

coded “0”; and ß04 j is the regression coefficient associated with private schools where

private schools were coded “1” and all other school modality categories were coded “0”.

Coefficient ß05 j is the regression coefficient associated with no matutino schools, which

were coded “0”, while matutino schools were coded “1”.

Coefficients ß06 j-09 j represent the regression parameters associated with

interactions between school modality categories and the school shift. More specifically,

ß06 j is the regression coefficient associated with the interaction between rural public

schools and matutino schools; ß07 j is the regression coefficient associated with the

interaction between indigenous and matutino schools; ß08 j is the regression coefficient

associated with the interaction between community courses and matutino schools; and

ß09 j is the regression coefficient associated with the interaction between private and

matutino schools. The error term, r0 j, is the residual variance of student literacy scores

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at the school level not accounted for by school modalities, the school shift, and their

interaction.

Because rural public schools, private schools, and school shifts (including

matutino or no matutino categories) were represented at the national level—i.e., in each

state—coefficients ß01 j, ß04 j, and ß05 j were conceived of as random, while the effects of

indigenous education schools or community courses were considered fixed because they

were concentrated within certain states. Moreover, the interaction effects were also

considered fixed.

Error estimates (u 01-03) at the state level, therefore, were only computed for rural

public schools, private schools, and for the dichotomous school shift variable. These

indicate the variance of the effect of school variables across states (including the

Federal District). Moreover, γ000 is the grand mean of student reading achievement

respective of the model, u00 is the random error at the state level, and γ010-090 are literacy

mean differences between each school group and the model grand mean.

Results

In this section I describe results from quantitative analyses described above—

descriptive studies (including group and subgroup distribution frequencies, means, and

standard deviations), mean comparisons, and results from hierarchal linear modeling.

Though case study data paralleling quantitative analyses in this study are largely

unanalyzed at this point, I wish to re-emphasize that interviews and observations carried

out at the three school sites shaped how I approached quantitative analyses. Specifically,

early on in this study it was brought to my attention that a thorough assessment of the

influence of the school shift on reading outcomes of elementary school children in

Mexico would require evaluated the co-influence of school modalities. Further analysis

after case study data gathering has been completed (June 2008) will certainly shed

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additional insight on the nesting of child characteristics, classroom processes, school

effects, and possibly state effects on student literacy learning. Such analyses will

present further questions in need of study in order to improve the quality and equality of

educational opportunity for Mexican students.

In the first set of descriptive analyses, I assessed the influence of the turno

escolar on reading outcomes of third grade students nationally during the 2005-2006

school year. Table 1 and Figure 1 demonstrate variation between school shifts. In the

population, 4 in 5 third grade students attended the matutino or morning shift, while

17.4 percent of third grade students attended the vespertino (or afternoon) shift, .1

percent the nocturno (or night) shift, 2.5 percent the discontinuo (or discontinuous)

shift, and less than .1 percent the mixto (or mixed) shift. Because the subsample sizes

attending the nocturno (n = 11) and mixto (n = 12) shifts were so small, standard errors

associated with mean standard deviation scores for these groups were inflated. This

produced the large confidence intervals associated with means for these two groups in

Figure 1. Thus, in subsequent analyses, descriptive statistics were suppressed (i.e., not

reported) for subsamples smaller than 30 students in which standard errors were

inflated.

Next, all non-matutino groups were collapsed into one category (see Table 2).

As shown in Table 2 and Figure 2, 20 percent of the third grade population was reported

as attending a non-matutino shift. Moreover, at the national level, children attending

morning shifts performed better, on average, than those attending others (an average

difference score of 18.7 points, roughly .2 of a national [or pooled] standard deviation).

Knowing student literacy achievement variation fluctuated by state (including the

Federal District) (Backhoff , Andrade, Sánchez, & Peon, 2007), I then decided to assess

the size fluctuation of mean differences between shifts by state.

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Table 3 includes mean literacy score variation by state, including the

distribution of children in the national sample and population attending school in each

state. While large discrepancies are found between sample and population (census data

gathered by the Secretaría de Educación Pública) frequencies (e.g., the state of

México), student weights applied to all analyses corrected distributional differences.

Figure 3 demonstrates mean student reading scores by state from the lowest to

highest averages. The national mean bar (whose width represents the 95 percent

confidence interval of the national mean estimate) offers an idea of which states are

below the national average. It should be mentioned here that students in Oaxaca were

excluded from the 2005-2006 study due to political disturbance during the time of

assessment, in which the state branch of the Sindicato Nacional de Trabajadores

Educativos (the National Teacher Labor Union) was heavily involved. While many of

the student mean scores within states were not statistically different from the national

mean, there were those who performed above and below the national average. Namely,

Guerrero, Yucatán, Tabasco, and Michoacán produced the lowest state mean score

while Baja California Sur, Nuevo León, and the Federal District were at the top.

Next, frequencies, means, and standard deviations were computed by school

shift within states. As expected, the size of the average reading score difference varied

by state (see Table 4). Moreover, the distribution of the third grade student population

enrolled into schools by shift also varied from state to state. For example, in Baja

California 33.5 percent of third grade students attended a non-matutino school, while

8.2 percent in Zacatecas did. Interestingly a few states showed small reading

achievement benefits for non-matutino over matutino students, though none of the mean

differences were statistically significant. On the other hand, large mean differences—

most of which were statistically significant—were found for several states in which

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students in non- matutino shifts scored lower than those attending matutino shifts in the

same school. The size of significant differences varied from .4 to .7 of a pooled standard

deviation, and included the states of Baja California, Chiapas, Morelos, Nuevo León,

Querétaro, Sonora, Tabasco, and Tamaulipas.

Figure 4 displays turno differences by state, in which states are ordered by their

overall mean in student reading achievement. The largest gaps between shifts are found

in states whose average achievement is either lower of higher than the national norm.

Further analysis should evaluate the within state processes that contribute to larger

learning differences between turnos.

Some of the large mean differences between school shifts by state were not

found to be statistically significant (see Table 4). For example, in Baja California Sur

the difference was larger than a third of a standard deviation, yet it was not significant.

This is due to the large standard errors associated with the mean and standard deviation

estimates of non-matutino students in this state. Estimates associated with school shifts

would be more precise if the national sample would have been stratified by turno

escolar, in addition to state and school modality. This is an important consideration if

we are to understand further the school shift phenomenon.

As mentioned above, findings from interviews related to case study data caused

me to consider the combining influence of the school shift with school modality. In

Table 5, frequencies, mean, and standard deviation estimates are provided by school

modality at the national level. Means and confidence intervals are also displayed in

Figure 5. As shown, most children attended urban public schools (61.7 percent),

followed by rural public (23.1 percent), private (8.3 percent), indigenous (6.0 percent),

and community course schools (.9 percent). Furthermore, means scores fluctuated

substantially between school modalities, where private schools scored the highest, on

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average, and indigenous education schools the lowest. Backhoff, Andrade, Sánchez and

Peon (2007) discuss these findings at length, including three hypotheses to why students

enrolled in community course schools produced a relatively high mean score when past

studies have found other age groups attending these schools to perform below the other

modalities. In short, they postulate that the unexpectedly high mean estimate of children

attending cursos comunitarios was attributable to sampling problems, improper test

administration, or that the finding represents a reality.

Next, the influence of the school shift on reading scores within school modalities

was assessed analyzing the descriptive statistics of students attending los turnos within

each modality (see Table 6). Frequency distributions show that larger portions children

attending indigenous and urban public schools were enrolled in non-matutino shifts

compared to the other school modality categories. That is, 91.4 percent of all children

enrolled in a non-matutino shift attending an indigenous or a urban public school. The

comparably low percent of rural public students attending a non-matutino shift

confirmed the finding that children in rural areas typically go to the school in the

morning—that due to immigration patterns in recent decades (i.e., migrants fleeing rural

communities), school shifts in these areas have declined.

Figure 6 displays mean literacy differences by school shift within school

modality. Again, the large confidence intervals in mean reading scores of non-matutino

children in cursos comunitarios and escuelas privadas are due to the large standard

errors associated with their mean and standard deviation estimates, which, in turn, are

directly related to the sample selection process. Certainly samples stratified by the

school shift would decrease the error terms, provide more precise estimates, and,

therefore, better interpretations concerning the combining influence of the school shift

and school modality on student learning outcomes (as well as other indicators).

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Even so, a significant mean difference in reading scores between shifts was

found for children attending urban public schools, where those attending morning shifts

performed 15.4 points higher, on average, than those attending non-morning shifts

within the same school modality.

Because descriptive analyses showed that the school shift, school modality, and

the state each accounted for unique variation in student literacy learning, multilevel

analyses were then conducted to evaluate the nested contributions of school modality

and school shift (and their interaction) within state. As a reference, descriptive statistics

and mean comparisons between school shifts by modality within states are provided in

Table 9 (estimates only provided for subgroups in which sample sizes could support the

analysis).

Multilevel coefficient estimates (produced using HLM6 software) associated

with the null model as well as Model 1 and Model 2 are provided in Table 7. Moreover,

the variance components and effect sizes associated with each model are afforded in

Table 8. In the null model, a significant portion of student achievement variance was

accounted for at the school and state levels. However, most of the variance remained at

the individual level. In Model 1, the school shift was added to the second level (i.e.,

schools). The beta coefficient associated with the school shift was found to be

statistically significant at the .05 level. Following the recommendations of Roberts and

Monaco (2006), an effect size coefficient (i.e., R2) was calculated by subtracting the

computed school-level variance component from the school-level variance component

of the null model, and then dividing the difference by the null school-level variance

component. The result was R2 = .059, meaning 5.9 percent of the student achievement

variation at the school level in the null model was accounted for by the effect of the

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school shift, at the national level. Importantly, as discovered in the descriptive analyses,

the size of the effect of the school shift varied between states (see Table 8).

In Model 2, the effect of the school modality on student literacy was combined

with the school shift in the second level of the model. Interactions between the school

shift and levels of the school modality were included. Not surprising, significant effects

were found for levels of school modality. That is, rural public schools were found to be

significantly lower than urban public schools (i.e., the base category for this variable),

and private schools were found to be significantly higher. Moreover, the effect of the

school shift remained significant at the .05 level, though the unstandardized beta

coefficient associated with the effect of the school shift decreased from 17.4 in Model 1

to 13.9 in Model 2, suggesting some shared student achievement variance between el

turno escolar and la modalidad escolar (i.e., school modality). However, none of the

interactions between the school shift variable and levels of school modality (with the

exception of the base category—urban public schools), were found to be significant.

An effect size estimate was then calculated to determine how much of the

variance in student reading achievement at the school level was accounted for by the

school shift and modality. The result suggests that 37.1 percent of the student learning

variation at the school level was accounted for by the model (see Table 8). Moreover,

the size of the effect of school modality and the school shift was found to vary by state

(see Table 8).

Discussion

Efficient and equitable student learning in Mexico, as throughout the developing and

underdeveloped world, will continue to be a salient concern for those studying and

working toward societal development. Moreover, educational research and their creative

combination of designs and methods should assist policymaker and practitioners

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involved in improving educational delivery. This means having a clearer understanding

of ways in which student variables interact with classroom processes, as well as the

school and state effects that influence differences in learning outcomes.

In this study I sought to combine methodologies to understand how the turno

escolar relates to differences in student literacy learning among Mexican students who

attended third grade during the 2005-2006 school year. Preliminary findings from case

studies helped reorient the design of quantitative analyses to include school modality.

As this area (i.e., case studies) of my study develops, I anticipate a richer understanding

of how student characteristics and classroom processes may relate to the school effects

analyzed presented in this paper.

A strength of this study was the inclusion of multilevel modeling. The

descriptive statistics included left unclear the proportional variance in student reading

performance accounted for by the school shift, school modality, and states (including

the Federal District). Computed estimates in HLM models, however, provided

information on the variance in student literacy learning accounted for at the individual,

school, and state levels (i.e., null model) generally, as well as the proportional variance

the school shift and school modality accounted for at the school level. Moreover, it was

found that the size of the school effects varied across states.

In essence, the question posed in this study—whether the school shift

significantly influenced literacy learning of elementary school students—received a

mixed answer. Yes, the influence is significant at the national level, where children

attending morning school perform better than those enrolled in other shifts. However,

the size of the effect depends on which state the students live in and the school modality

they attend. Further research should look deeper into the regions and school types in

which the shift has a stronger influence on literacy learning. Again, multiple methods

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and creative designs will be needed if this work is to have a meaningful impact on

policy and practice. Particularly, we need to know how the school shift relates with the

classroom processes (including instructional strategies, curricular approaches, and

teacher-student relationships) and student characteristics that shape learning.

The variance components of the null HLM model shown in Table 8 are very

insightful. Here, 75.7 percent of the variance in student reading achievement accounted

for by students, schools, and states nationally was accounted for at the student level.

Certainly a great deal of student variation is also accounted for by classrooms. Unless

we tweak our research tools to get at classroom processes nationally, we are able to

explain only a limited portion of student achievement variance. We are limited in our

ability to identify effective interventions in order to improve learning opportunities

broadly, and for specific subpopulations of students most at-risk for academic failure.

I conclude with a few recommendations concerning further research efforts, and

some considerations for policymakers in Mexico. Our ability to understand the

influence of the school shift on student learning nationally is slightly tainted by the fact

that this variable is not considered presently in the sampling process for Excale

assessments. As explained earlier, the errors associated with estimates computed to

understand achievement patterns among students enrolled in different shifts are quite

high in many cases. Often this is due to small subsample sizes within groups. Smaller

confidence intervals, and more precise mean comparison estimates would be facilitated

by including the school shift as an additional stratification in sampling process.

While this paper assesses the influence of the school shift on achievement

outcomes nationally, it does not show how large the effect sizes of the school shift are

in states and school modalities in which the effect was found to be significant.

El Turno Escolar 26

Segmented multilevel analyses in those states where significant differences were found

would be useful to see how large particular effects really are.

Finally, educational policymakers in Mexico may (rightly) ask, “so what?”. This

paper provides evidence that separating students into different turnos can have

unfavorable results in terms of their literacy learning outcomes. In many ways, this

phenomenon embodies the changing mindset of educational policymakers in developing

countries from a concern to access expansion in basic education to addressing quality.

Students cannot be simply stuffed into classrooms, and then be expected to learn

equally. Especially in states (i.e., Baja California, Chiapas, Morelos, Nuevo León,

Querétaro, Sonora, Tabasco, and Tamaulipas) and school modalities (i.e., urban public)

in which the turno is found to have the greatest influence on student learning,

alternatives should be considered by educational policymakers. Where possible, piloting

alternatives should be accompanied with grounded research efforts to document

progress and identify its contributors.

El Turno Escolar 27

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Figures

Figure 1. Literacy means and confidence intervals of 3rd grade Mexican children by

turno escolar (school shift) categories, 2005-2006

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Figure 2. Literacy means and confidence intervals of 3rd grade Mexican children by

dichotomous turno escolar (school shift) categories, 2005-2006

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Figure 3. Literacy means and confidence intervals of 3rd grade Mexican children by

state and the Federal District, 2005-2006

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Figure 4. Literacy means and confidence intervals of 3rd grade Mexican children by

turno escolar within state and the Federal District, 2005-2006

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Figure 5. Literacy means and confidence intervals of 3rd grade Mexican children by

school modality, 2005-2006

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Figure 6. Literacy means and confidence intervals of 3rd grade Mexican children by

turno escolar within school modality, 2005-2006

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Tables

Table 1. Third Grade Literacy Scores in Mexico by Turno Escolar Categories, 2005-

2006

Turno n % n N % N Mean score Standard deviation

Mean S.E. S.D. S.E. National 16563 100.0 2399355 100.0 500 (2.1) 100 (1.3) matutino 13809 83.4 1918884 80.0 504 (2.3) 100 (1.3) vespertino 2287 13.8 418080 17.4 489 (5.0) 97 (2.6) nocturno 11 0.1 2314 0.1 478 (37.1) 110 (36.2) discontinuo 444 2.7 59727 2.5 475 (10.6) 96 (4.0) mixto 12 0.1 350 0.0 556 (26.1) 84 (21.1)

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Table 2. Third Grade Literacy Scores and Mean Comparisons in Mexico by

Dichotomous Turno Escolar Categories, 2005-2006

Turno n % n N % N Mean scoreStandard deviation Mean ∆ C.I.

Mean S.E. S.D. S.E. National 16563 100.0 2399355 100.0 500 (2.1) 100 (1.3) matutino 13809 83.4 1918884 80.0 505 (2.3) 100 (1.4) 18.7 8.4 no matutino 2754 16.6 480471 20.0 486 (4.3) 98 (2.8) 29.1

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Table 3. Third Grade Literacy Scores in Mexico by State, 2005-2006

State n % n N % N Mean score Standard deviation

Mean S.E. S.D. S.E. National 16563 100.0 2399355 100.0 500 (2.1) 100 (1.3) Aguascalientes 546 3.3 25286 1.1 497 (7.7) 97 (5.3) Baja California 614 3.7 64192 2.7 509 (7.6) 101 (4.2)

Baja California Sur 541 3.3 10899 0.5 518 (6.9) 100 (4.7)

Campeche 472 2.8 18058 0.8 496 (9.4) 100 (5.9) Coahuila 531 3.2 54809 2.3 504 (7.9) 101 (4.3) Colima 512 3.1 11657 0.5 497 (9.8) 100 (5.6) Chiapas 532 3.2 135309 5.6 488 (11.1) 106 (7.1) Chihuahua 608 3.7 73542 3.1 501 (8.2) 99 (5.2) Distrito Federal 445 2.7 157058 6.5 531 (7.1) 99 (5.0) Durango 543 3.3 36148 1.5 494 (7.9) 99 (8.0) Guanajuato 455 2.7 123919 5.2 499 (7.4) 102 (6.5) Guerrero 493 3.0 94423 3.9 474 (6.6) 92 (5.6) Hidalgo 563 3.4 57106 2.4 494 (8.5) 100 (3.5) Jalisco 524 3.2 147472 6.1 492 (7.6) 102 (5.4) México 511 3.1 297041 12.4 502 (7.6) 93 (4.6) Michoacán 559 3.4 101094 4.2 482 (9.4) 104 (9.3) Morelos 621 3.7 35343 1.5 501 (6.2) 98 (3.0) Nayarit 483 2.9 21200 0.9 502 (6.7) 98 (4.4) Nuevo León 512 3.1 84506 3.5 526 (7.0) 101 (4.9) Puebla 652 3.9 133774 5.6 499 (7.1) 102 (5.5) Querétaro 521 3.1 38697 1.6 505 (6.2) 101 (5.4) Quintana Roo 590 3.6 28508 1.2 504 (8.3) 100 (4.9) San Luis Potosí 533 3.2 58612 2.4 502 (9.4) 96 (4.8) Sinaloa 510 3.1 59624 2.5 500 (8.7) 103 (4.5) Sonora 530 3.2 52077 2.2 509 (7.3) 98 (4.3) Tabasco 434 2.6 48788 2.0 480 (8.1) 91 (4.9) Tamaulipas 529 3.2 64013 2.7 514 (7.3) 103 (4.7) Tlaxcala 589 3.6 28782 1.2 507 (7.0) 102 (5.3) Veracruz 553 3.3 168496 7.0 495 (8.4) 95 (4.9) Yucatán 643 3.9 36888 1.5 479 (8.8) 99 (4.1) Zacatecas 414 2.5 31726 1.3 496 (8.2) 97 (5.4)

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Table 4. Third Grade Literacy Scores and Mean Comparisons in Mexico by

Dichotomous Turno Escolar Categories within State, 2005-2006

State Turno n % n N % N Mean score Standard deviation

Mean ∆ C.I.

Mean S.E. SD SE National 16563 100.0 2399355 100.0 500 (2.1) 100 (1.3) Aguascalientes matutino 471 86.3 19879 78.6 495 (6.4) 95 (5.3) 6.8 -58.6 no matutino 75 13.7 5407 21.4 502 (32.5) 104 (15.2) 72.2 Baja Calif. matutino 466 75.9 42700 66.5 527 (7.2) 100 (5.1) 49.8 22.1 no matutino 148 24.1 21492 33.5 477 (13.6) 94 (7.2) 77.4 Baja Calif. Sur matutino 453 83.7 8475 77.8 527 (6.5) 100 (4.5) 36.6 -4 no matutino 88 16.3 2424 22.2 490 (17.8) 95 (9.5) 77.3 Campeche matutino 394 83.5 14861 82.3 498 (9.2) 98 (6.0) 8.9 -30 no matutino 78 16.5 3197 17.7 489 (18.2) 105 (11.8) 47.8 Coahuila matutino 421 79.3 43074 78.6 513 (10.0) 100 (5.8) 30.8 -3.8 no matutino 110 20.7 11735 21.4 482 (14.9) 98 (7.3) 65.5 Colima matutino 406 79.3 8682 74.5 504 (9.7) 97 (4.0) 18.7 -24.9 no matutino 106 20.7 2975 25.5 485 (20.4) 102 (15.6) 62.3 Chiapas matutino 351 66.0 99950 73.9 509 (13.3) 108 (8.7) 72.7 34.9 no matutino 181 34.0 35359 26.1 437 (13.5) 87 (7.3) 110.5 Chihuahua matutino 424 69.7 53279 72.4 500 (8.6) 100 (5.6) 4.3 -22.3 no matutino 184 30.3 20263 27.6 504 (12.9) 98 (8.8) 30.9 Distrito Fed. matutino 352 79.1 116837 74.4 535 (9.3) 102 (5.5) 16.2 -18.3 no matutino 93 20.9 40221 25.6 519 (14.9) 90 (10.3) 50.6 Durango matutino 401 73.8 29382 81.3 501 (7.1) 96 (5.9) 27.6 -22.1 no matutino 142 26.2 6766 18.7 473 (25.1) 106 (18.9) 77.4 Guanajuato matutino 352 77.4 98629 79.6 501 (9.7) 102 (8.9) 10.2 -19 no matutino 103 22.6 25290 20.4 491 (11.7) 100 (8.2) 39.5 Guerrero matutino 457 92.7 84699 89.7 474 (7.4) 93 (6.2) 3.9 -39.9 no matutino 36 7.3 9724 10.3 478 (21.1) 82 (14.8) 47.7 Hidalgo matutino 558 99.1 53393 93.5 493 (8.4) 100 (3.6) n/a n/a no matutino 5 0.9 3713 6.5 n/a n/a n/a n/a n/a Jalisco matutino 425 81.1 105084 71.3 494 (8.2) 92 (4.6) 10.2 -36 no matutino 99 18.9 42388 28.7 484 (20.1) 120 (8.6) 56.4 México matutino 415 81.2 234164 78.8 508 (10.3) 98 (5.3) 19.1 -14.5 no matutino 96 18.8 62877 21.2 489 (12.7) 80 (10.0) 52.7 Michoacán matutino 469 83.9 83281 82.4 485 (10.1) 104 (11.2) 18.0 -35.4 no matutino 90 16.1 17813 17.6 467 (25.4) 96 (13.4) 71.5 Morelos matutino 520 83.7 27738 78.5 509 (7.7) 99 (2.7) 40.9 9.7 no matutino 101 16.3 7605 21.5 468 (12.7) 85 (7.4) 72.2 Nayarit matutino 431 89.2 17699 83.5 500 (7.3) 97 (5.2) 7.5 -18.2 no matutino 52 10.8 3501 16.5 508 (11.7) 98 (11.2) 33.3 Nuevo León matutino 447 87.3 61857 73.2 535 (9.7) 100 (5.1) 45.5 13.3 no matutino 65 12.7 22649 26.8 489 (11.9) 97 (9.3) 77.7 Puebla matutino 580 89.0 120046 89.7 504 (8.1) 101 (5.9) 15.1 -49.2 no matutino 72 11.0 13728 10.3 488 (28.3) 104 (15.3) 79.5 Querétaro matutino 400 76.8 30544 78.9 516 (7.8) 99 (5.6) 36.9 1.8 no matutino 121 23.2 8153 21.1 479 (14.1) 98 (9.6) 72.1 Quintana Roo matutino 445 75.4 19157 67.2 503 (6.6) 99 (6.2) 7.3 -34.6 no matutino 145 24.6 9351 32.8 510 (20.9) 103 (10.1) 49.2 San Luis Pot. matutino 466 87.4 50151 85.6 504 (8.2) 98 (5.9) 5.9 -39.3

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no matutino 67 12.6 8461 14.4 498 (23.9) 85 (11.9) 51.1 Sinaloa matutino 455 89.2 46941 78.7 506 (7.9) 104 (4.6) 27.8 -28.4 no matutino 55 10.8 12683 21.3 478 (27.8) 96 (10.3) 83.9 Sonora matutino 386 72.8 40767 78.3 523 (8.6) 100 (6.1) 35.8 8.6 no matutino 144 27.2 11310 21.7 487 (10.2) 88 (7.8) 62.9 Tabasco matutino 406 93.5 44007 90.2 484 (7.5) 92 (4.9) 40.3 1.7 no matutino 28 6.5 4781 9.8 443 (17.1) 72 (10.9) 79 Tamaulipas matutino 456 86.2 48488 75.7 526 (6.1) 101 (4.2) 52.3 11.8 no matutino 73 13.8 15525 24.3 473 (20.8) 97 (12.0) 92.7 Tlaxcala matutino 513 87.1 24640 85.6 514 (7.6) 103 (5.2) 20.9 -23 no matutino 76 12.9 4142 14.4 493 (19.8) 98 (13.9) 64.9 Veracruz matutino 493 89.2 142715 84.7 493 (8.9) 93 (3.7) 5.4 -64.3 no matutino 60 10.8 25781 15.3 498 (32.7) 102 (20.0) 75 Yucatán matutino 607 94.4 31086 84.3 478 (7.6) 100 (4.3) 18.8 -40.7 no matutino 36 5.6 5802 15.7 497 (31.2) 88 (20.2) 78.3 Zacatecas matutino 389 94.0 29136 91.8 499 (8.9) 97 (5.4) n/a n/a no matutino 25 6.0 2590 8.2 n/a n/a n/a n/a n/a

n/a – not made available – data suppressed due to small sample size (n < 30) and inflated standard errors associated with computed estimates

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Table 5. Third Grade Literacy Scores in Mexico by School Modality, 2005-2006

n % n N % N Mean score Standard Deviation

Mean SE SD SE National 16563 100.0 2399355 100.0 500 (2.1) 100 (1.3) Urbanas Públicas 6987 42.2 1479702 61.7 501 (2.8) 98 (1.5) Rurales Públicas 3841 23.2 553948 23.1 479 (3.3) 94 (3.4) Educación Indígena 1628 9.8 144793 6.0 453 (5.0) 90 (3.9) Cursos Comunitarios 326 2.0 22374 0.9 530 (7.4) 107 (5.0) Privadas 3781 22.8 198538 8.3 571 (4.0) 99 (2.7)

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Table 6. Mexico 3rd Grade Literacy Scores and Mean Comparisons by Turno within

Modality

Modality Turno n %n N %N Mean score Standard deviation

Mean ∆ C.I.

Mean S.E. S.D. S.E. National 16563 100.0 2399355 100.0 500 (2.1) 100 (1.3) Urbanas Púb matutino 4966 71.1 1081622 73.1 507 (3.6) 97 (1.8) 15.4 2.9 no matutino 2021 28.9 398080 26.9 492 (4.8) 98 (2.9) 27.9 Rurales Púb matutino 3585 93.3 515983 93.1 480 (3.9) 94 (3.4) 14.1 -10.7 no matutino 256 6.7 37965 6.9 466 (11.8) 84 (10.0) 38.9 Educ Indígena matutino 1232 75.7 103651 71.6 457 (4.9) 89 (4.4) 20.0 -3.6 no matutino 396 24.3 41142 28.4 437 (11.5) 88 (6.5) 43.7 Cursos Comu matutino 303 92.9 20927 93.5 531 (8.0) 107 (5.1) n/a n/a no matutino 23 7.1 1447 6.5 n/a n/a n/a n/a n/a Privadas matutino 3723 98.5 196701 99.1 571 (3.7) 99 (2.6) 12.8 -39.0 no matutino 58 1.5 1837 0.9 558 (26.6) 94 (9.3) 64.5

n/a – not made available – data suppressed due to small sample size (n < 30) and inflated standard errors associated with computed estimates

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Table 7. Coefficient estimates associated with hierarchal linear models

Null Model Model 1 Model 2 ß SE p ß SE p ß SE p Intercept 501.6* 2.7 0.000 487.9* 4.9 0.000 492.4* 4.6 0.000School Shift matutino 17.4* 5.7 0.005 13.9* 6.4 0.037Sch Modality rural -33.1* 14.1 0.026 educ ind -28.7 13.1 0.064 cc 33.3 19.9 0.100 private 77.4* 19.1 0.001Interactions rural*matutino 12.3 15.17 0.420 Edu ind*matutino -16.2 10.6 0.143 cc*matutino -9.7 25.5 0.705 Private*matutino -18.2 20.7 0.399

*significant at the .05 level

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Table 8. Variance Components and effect sizes associated with hierarchal linear models

Variance

Component SD p R2 Null Model Student 7548.2 86.9 School 2276.4* 47.7 0.000 State 147.5* 12.1 0.000 Model 1 Student 7555.2 86.9 School 2143.2* 46.3 0.000 0.059 State 316.4* 17.8 0.000 School Shift Effect by State 392.2* 19.8 0.000 Model 2 Student 7616.4 87.3 School 1432.3* 37.8 0.000 0.371 State 261.0* 16.2 0.000 Rural Effect by State 361.9* 19.0 0.000 Private Effect by State 458.0* 21.4 0.001 School Shift Effect by State 392.2* 19.8 0.000

*significant at the .05 level

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Table 9. Mexico 3rd Grade Literacy Scores and Mean Comparisons by Turno, Modality, and State

State Modality Turno n %n N %N Mean score Stand. Dev. Mean ∆ C.I Mean S.E. S.D. S.E. National 16563 100.0 2399355 100.0 500 (2.1) 100 (1.3) Aguascalientes Urb Púb matutino 188 73.2 12585 70.9 487 (8.7) 92 (6.0) 12.9 -58.3 non-matutino 69 26.8 5166 29.1 500 (33.7) 104 (16.5) 84.1 Rur Púb matutino 144 96.0 4679 95.1 474 (10.3) 85 (9.8) n/a n/a non-matutino 6 4.0 241 4.9 n/a n/a n/a n/a n/a Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 2 100.0 47 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 137 100.0 2568 100.0 575 (15.8) 90 (6.2) non-matutino 0 0.0 0 0.0 Baja California Urb Púb matutino 179 59.9 30169 60.5 522 (8.3) 97 (7.1) 47.3 18.7 non-matutino 120 40.1 19732 39.5 474 (14.4) 93 (8.1) 76.0 Rur Púb matutino 144 83.7 4876 77.5 500 (14.5) 93 (7.5) 20.0 -71.3 non-matutino 28 16.3 1418 22.5 520 (43.6) 93 (11.6) 111.4 Edu Ind matutino 5 100.0 1230 80.8 n/a n/a n/a n/a non-matutino 0 0.0 292 19.2 Cur Com matutino 5 100.0 243 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 133 100.0 6182 99.2 591 (15.0) 96 (6.2) non-matutino 0 0.0 50 0.8 Baja Calif Sur Urb Púb matutino 208 75.6 6105 73.3 528 (7.9) 98 (6.0) 39.4 -4.6 non-matutino 67 24.4 2227 26.7 489 (18.8) 94 (10.0) 83.3 Rur Púb matutino 114 90.5 1573 94.4 498 (15.5) 95 (10.2) n/a n/a non-matutino 12 9.5 94 5.6 n/a n/a n/a n/a n/a Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 0 0.0 1 1.5

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non-matutino 0 0.0 64 98.5 Priv matutino 131 93.6 796 95.3 576 (12.3) 101 (8.3) n/a n/a non-matutino 9 6.4 39 4.7 n/a n/a n/a n/a n/a Campeche Urb Púb matutino 143 66.5 8264 74.3 508 (13.3) 92 (8.6) 10.5 -29.0 non-matutino 72 33.5 2861 25.7 498 (17.7) 104 (14.3) 50.0 Rur Púb matutino 125 95.4 5018 93.7 476 (15.0) 99 (12.8) n/a n/a non-matutino 6 4.6 336 6.3 n/a n/a n/a n/a n/a Edu Ind matutino 2 100.0 541 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Cur Com matutino 1 100.0 135 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 123 100.0 903 100.0 572 (12.2) 102 (10.8) non-matutino 0 0.0 0 0.0 Coahuila Urb Púb matutino 159 61.4 32243 73.8 508 (13.2) 97 (8.1) 25.5 -13.3 non-matutino 100 38.6 11472 26.2 483 (15.3) 98 (7.4) 64.2 Rur Púb matutino 118 92.2 5405 95.4 471 (12.8) 82 (10.8) n/a n/a non-matutino 10 7.8 263 4.6 n/a n/a n/a n/a n/a Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 2 100.0 132 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 142 100.0 5294 100.0 584 (8.4) 97 (7.7) non-matutino 0 0.0 0 0.0 Colima Urb Púb matutino 164 61.7 6330 68.2 503 (12.3) 92 (5.2) 16.9 -28.0 non-matutino 102 38.3 2958 31.8 486 (20.6) 102 (15.7) 61.8 Rur Púb matutino 120 96.8 1388 98.8 476 (13.9) 97 (9.9) n/a n/a non-matutino 4 3.2 17 1.2 n/a n/a n/a n/a n/a Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 0 0.0 60 100.0 non-matutino 0 0.0 0 0.0 Priv matutino 122 100.0 904 100.0 554 (17.4) 109 (9.8) non-matutino 0 0.0 0 0.0

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Chiapas Urb Púb matutino 133 85.8 40194 87.1 540 (24.2) 108 (13.3) n/a n/a non-matutino 22 14.2 5979 12.9 n/a n/a n/a n/a n/a Rur Púb matutino 90 100.0 39979 99.4 488 (21.6) 108 (14.5) non-matutino 0 0.0 257 0.6 Edu Ind matutino 64 28.7 11897 29.0 463 (16.1) 88 (12.3) 31.2 -12.1 non-matutino 159 71.3 29090 71.0 431 (13.5) 86 (8.3) 74.4 Cur Com matutino 63 100.0 5516 100.0 526 (18.3) 99 (11.4) non-matutino 0 0.0 0 0.0 Priv matutino 1 100.0 2364 98.6 n/a n/a n/a n/a non-matutino 0 0.0 33 1.4 Chihuahua Urb Púb matutino 192 71.9 41366 73.8 493 (9.7) 98 (6.7) 19.7 -13.5 non-matutino 75 28.1 14687 26.2 512 (16.0) 93 (13.0) 52.8 Rur Púb matutino 89 74.8 6574 73.6 507 (16.4) 101 (16.8) 17.4 -53.2 non-matutino 30 25.2 2356 26.4 490 (28.9) 108 (15.6) 87.9 Edu Ind matutino 12 14.1 403 13.2 n/a n/a n/a n/a n/a n/a non-matutino 73 85.9 2648 86.8 469 (15.1) 95 (10.3) n/a Cur Com matutino 0 0.0 103 18.9 non-matutino 6 100.0 441 81.1 n/a n/a n/a n/a Priv matutino 131 100.0 4833 97.4 553 (15.3) 95 (9.9) non-matutino 0 0.0 131 2.6 Distrito Federal Urb Púb matutino 206 69.6 82326 67.5 510 (9.7) 94 (6.8) 8.4 -26.6 non-matutino 90 30.4 39567 32.5 519 (14.9) 90 (10.4) 43.4 Rur Púb matutino 11 100.0 1012 72.1 n/a n/a n/a n/a non-matutino 0 0.0 391 27.9 Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 0 0.0 3 12.5 non-matutino 3 100.0 21 87.5 n/a n/a n/a n/a Priv matutino 135 100.0 33496 99.3 595 (12.8) 95 (7.3) non-matutino 0 0.0 242 0.7 Durango Urb Púb matutino 139 85.8 17127 79.8 509 (10.9) 94 (8.0) n/a n/a non-matutino 23 14.2 4334 20.2 n/a n/a n/a n/a n/a Rur Púb matutino 122 92.4 10385 95.4 476 (10.0) 91 (10.0) n/a n/a

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non-matutino 10 7.6 499 4.6 n/a n/a n/a n/a n/a Edu Ind matutino 13 11.6 142 9.9 n/a n/a n/a n/a n/a n/a non-matutino 99 88.4 1298 90.1 483 (11.8) 86 (10.2) n/a Cur Com matutino 0 0.0 0 0.0 non-matutino 10 100.0 635 100.0 n/a n/a n/a n/a Priv matutino 127 100.0 1728 100.0 572 (17.3) 97 (13.7) non-matutino 0 0.0 0 0.0 Guanajuato Urb Púb matutino 118 56.5 50047 70.1 496 (16.5) 99 (8.2) 2.8 -39.1 non-matutino 91 43.5 21298 29.9 494 (12.8) 105 (8.7) 44.8 Rur Púb matutino 96 89.7 37120 90.6 485 (14.8) 95 (16.5) n/a n/a non-matutino 11 10.3 3870 9.4 n/a n/a n/a n/a n/a Edu Ind matutino 0 0.0 153 97.5 non-matutino 0 0.0 4 2.5 Cur Com matutino 10 90.9 565 91.9 n/a n/a n/a n/a n/a n/a non-matutino 1 9.1 50 8.1 n/a n/a n/a n/a n/a Priv matutino 128 100.0 10744 99.4 578 (11.2) 101 (7.8) non-matutino 0 0.0 68 0.6 Guerrero Urb Púb matutino 123 81.5 33938 80.4 475 (11.2) 84 (5.4) n/a n/a non-matutino 28 18.5 8268 19.6 n/a n/a n/a n/a n/a Rur Púb matutino 118 95.9 29592 96.4 485 (13.8) 101 (14.4) n/a n/a non-matutino 5 4.1 1120 3.6 n/a n/a n/a n/a n/a Edu Ind matutino 175 98.3 16724 98.1 444 (9.3) 76 (5.5) n/a n/a non-matutino 3 1.7 322 1.9 n/a n/a n/a n/a n/a Cur Com matutino 40 100.0 1754 100.0 608 (23.9) 129 (11.9) non-matutino 0 0.0 0 0.0 Priv matutino 1 100.0 2691 99.5 n/a n/a n/a n/a non-matutino 0 0.0 14 0.5 Hidalgo Urb Púb matutino 174 97.2 20114 86.0 502 (13.0) 96 (6.3) n/a n/a non-matutino 5 2.8 3278 14.0 n/a n/a n/a n/a n/a Rur Púb matutino 109 100.0 21141 98.5 485 (16.4) 97 (7.9) non-matutino 0 0.0 320 1.5 Edu Ind matutino 120 100.0 7435 98.5 446 (15.8) 95 (10.2) non-matutino 0 0.0 115 1.5

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Cur Com matutino 17 100.0 1023 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 138 100.0 3680 100.0 573 (11.4) 96 (8.1) non-matutino 0 0.0 0 0.0 Jalisco Urb Púb matutino 157 64.3 66822 63.6 481 (11.3) 88 (6.9) 7.6 -46.3 non-matutino 87 35.7 38205 36.4 489 (22.0) 121 (9.5) 61.4 Rur Púb matutino 93 88.6 19163 83.0 486 (14.9) 80 (11.2) n/a n/a non-matutino 12 11.4 3936 17.0 n/a n/a n/a n/a n/a Edu Ind matutino 7 100.0 1000 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Cur Com matutino 11 100.0 596 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 157 100.0 17503 98.6 553 (12.7) 96 (6.5) non-matutino 0 0.0 247 1.4 México Urb Púb matutino 164 64.3 160767 73.5 510 (14.0) 98 (7.5) 17.1 -23.4 non-matutino 91 35.7 57852 26.5 493 (14.4) 79 (10.4) 57.6 Rur Púb matutino 132 96.4 43994 90.2 474 (13.8) 81 (7.7) n/a n/a non-matutino 5 3.6 4756 9.8 n/a n/a n/a n/a n/a Edu Ind matutino 5 100.0 2978 97.0 n/a n/a n/a n/a non-matutino 0 0.0 91 3.0 Cur Com matutino 8 100.0 660 98.7 n/a n/a n/a n/a non-matutino 0 0.0 9 1.3 Priv matutino 106 100.0 25765 99.3 566 (16.0) 97 (10.9) non-matutino 0 0.0 169 0.7 Michoacán Urb Púb matutino 92 62.6 38628 71.7 484 (15.3) 98 (11.6) 15.9 -37.7 non-matutino 55 37.4 15232 28.3 468 (27.0) 96 (14.7) 69.4 Rur Púb matutino 77 100.0 30589 95.4 479 (23.2) 115 (25.1) non-matutino 0 0.0 1475 4.6 Edu Ind matutino 148 85.1 3471 81.1 455 (13.0) 88 (8.8) n/a n/a non-matutino 26 14.9 808 18.9 n/a n/a n/a n/a n/a Cur Com matutino 14 100.0 1170 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 138 93.9 9423 96.9 523 (10.5) 90 (7.7) n/a n/a

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non-matutino 9 6.1 298 3.1 n/a n/a n/a n/a n/a Morelos Urb Púb matutino 210 74.7 19413 74.2 501 (10.4) 97 (4.2) 32.3 -4.6 non-matutino 71 25.3 6763 25.8 469 (14.2) 82 (8.7) 69.1 Rur Púb matutino 154 83.7 5031 86.3 487 (11.1) 90 (6.3) 25.0 -22.1 non-matutino 30 16.3 799 13.7 462 (26.6) 108 (18.8) 72.1 Edu Ind matutino 3 100.0 82 65.6 n/a n/a n/a n/a non-matutino 0 0.0 43 34.4 Cur Com matutino 5 100.0 141 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 148 100.0 3071 100.0 592 (10.9) 87 (6.8) non-matutino 0 0.0 0 0.0 Nayarit Urb Púb matutino 129 76.3 9547 75.9 499 (10.0) 90 (6.8) 15.3 -10.5 non-matutino 40 23.7 3037 24.1 515 (12.0) 95 (11.1) 41.1 Rur Púb matutino 86 87.8 5377 92.1 484 (16.1) 99 (15.2) n/a n/a non-matutino 12 12.2 464 7.9 n/a n/a n/a n/a n/a Edu Ind matutino 103 100.0 1545 100.0 501 (14.4) 102 (9.3) non-matutino 0 0.0 0 0.0 Cur Com matutino 4 100.0 238 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 109 100.0 992 100.0 577 (16.0) 101 (9.7) non-matutino 0 0.0 0 0.0 Nuevo León Urb Púb matutino 216 81.2 44946 68.4 526 (12.4) 98 (6.8) 40.7 3.7 non-matutino 50 18.8 20717 31.6 485 (12.2) 97 (9.3) 77.7 Rur Púb matutino 90 94.7 6588 78.0 545 (17.7) 100 (12.8) n/a n/a non-matutino 5 5.3 1860 22.0 n/a n/a n/a n/a n/a Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 0 0.0 93 100.0 non-matutino 0 0.0 0 0.0 Priv matutino 141 93.4 10230 99.3 567 (13.8) 102 (8.8) n/a n/a non-matutino 10 6.6 72 0.7 n/a n/a n/a n/a n/a Puebla Urb Púb matutino 131 67.9 63886 82.8 515 (13.5) 97 (9.5) 26.6 -46.5 non-matutino 62 32.1 13247 17.2 488 (28.4) 104 (15.4) 99.7

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Rur Púb matutino 158 100.0 33975 98.8 468 (11.5) 92 (6.8) non-matutino 0 0.0 402 1.2 Edu Ind matutino 159 94.1 11854 99.4 480 (10.6) 99 (12.4) n/a n/a non-matutino 10 5.9 71 0.6 n/a n/a n/a n/a n/a Cur Com matutino 12 100.0 712 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 120 100.0 9619 99.9 580 (16.3) 99 (7.5) non-matutino 0 0.0 8 0.1 Querétaro Urb Púb matutino 107 49.3 13281 66.1 528 (15.9) 98 (7.0) 44.7 -0.3 non-matutino 110 50.7 6810 33.9 483 (14.3) 102 (12.2) 89.8 Rur Púb matutino 148 93.1 10820 90.8 465 (9.4) 74 (8.8) n/a n/a non-matutino 11 6.9 1091 9.2 n/a n/a n/a n/a n/a Edu Ind matutino 3 100.0 1120 92.6 n/a n/a n/a n/a non-matutino 0 0.0 90 7.4 Cur Com matutino 8 100.0 486 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 134 100.0 4837 96.8 604 (11.1) 89 (6.4) non-matutino 0 0.0 162 3.2 Quintana Roo Urb Púb matutino 181 61.6 12470 59.0 501 (7.4) 93 (7.6) 13.2 -28.8 non-matutino 113 38.4 8649 41.0 515 (21.2) 102 (11.0) 55.3 Rur Púb matutino 133 80.6 3727 86.8 458 (11.7) 84 (6.8) 18.2 -46.6 non-matutino 32 19.4 569 13.2 440 (31.9) 89 (18.8) 83.1 Edu Ind matutino 3 100.0 584 81.5 n/a n/a n/a n/a non-matutino 0 0.0 133 18.5 Cur Com matutino 1 100.0 86 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 127 100.0 2290 100.0 599 (14.2) 94 (7.1) non-matutino 0 0.0 0 0.0 San Luis Potosí Urb Púb matutino 105 68.6 21102 73.9 519 (14.4) 94 (9.5) 18.4 -30.8 non-matutino 48 31.4 7435 26.1 501 (26.7) 88 (13.2) 67.7 Rur Púb matutino 105 96.3 19985 96.0 485 (14.4) 94 (13.3) n/a n/a non-matutino 4 3.7 823 4.0 n/a n/a n/a n/a n/a Edu Ind matutino 118 88.7 3368 95.0 451 (13.0) 92 (8.8) n/a n/a

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non-matutino 15 11.3 177 5.0 n/a n/a n/a n/a n/a Cur Com matutino 27 100.0 1106 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 111 100.0 4590 99.4 563 (17.2) 102 (7.9) non-matutino 0 0.0 26 0.6 Sinaloa Urb Púb matutino 175 79.5 25317 69.7 502 (10.3) 99 (6.5) 27.0 -37.2 non-matutino 45 20.5 11016 30.3 475 (31.3) 96 (11.8) 91.2 Rur Púb matutino 125 92.6 15830 90.7 492 (17.0) 106 (8.9) n/a n/a non-matutino 10 7.4 1623 9.3 n/a n/a n/a n/a n/a Edu Ind matutino 3 100.0 353 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Cur Com matutino 16 100.0 799 94.8 n/a n/a n/a n/a non-matutino 0 0.0 44 5.2 Priv matutino 136 100.0 4642 100.0 577 (14.7) 92 (7.3) non-matutino 0 0.0 0 0.0 Sonora Urb Púb matutino 157 55.9 28650 72.7 525 (10.3) 95 (9.7) 37.8 8.0 non-matutino 124 44.1 10750 27.3 487 (10.3) 88 (7.9) 67.5 Rur Púb matutino 99 100.0 6466 94.0 494 (13.7) 91 (10.1) non-matutino 0 0.0 410 6.0 Edu Ind matutino 10 100.0 820 91.4 n/a n/a n/a n/a non-matutino 0 0.0 77 8.6 Cur Com matutino 1 100.0 50 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 119 85.6 4781 98.5 577 (15.0) 101 (11.6) n/a n/a non-matutino 20 14.4 73 1.5 n/a n/a n/a n/a n/a Tabasco Urb Púb matutino 149 84.2 17499 79.5 502 (10.0) 94 (8.7) 58.6 16.4 non-matutino 28 15.8 4519 20.5 443 (17.1) 72 (10.9) 100.8 Rur Púb matutino 129 100.0 22239 99.0 466 (11.7) 84 (8.4) non-matutino 0 0.0 219 1.0 Edu Ind matutino 4 100.0 1318 96.8 n/a n/a n/a n/a non-matutino 0 0.0 43 3.2 Cur Com matutino 6 100.0 367 100.0 518 (46.5) 73 (22.3) non-matutino 0 0.0 0 0.0

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Priv matutino 118 100.0 2584 100.0 541 (14.9) 95 (8.8) non-matutino 0 0.0 0 0.0 Tamaulipas Urb Púb matutino 224 75.4 36239 70.5 525 (7.5) 93 (4.5) 51.6 11.7 non-matutino 73 24.6 15172 29.5 473 (20.8) 97 (12.0) 91.6 Rur Púb matutino 127 100.0 7702 95.9 497 (12.2) 107 (8.9) non-matutino 0 0.0 326 4.1 Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 4 100.0 362 95.8 n/a n/a n/a n/a non-matutino 0 0.0 16 4.2 Priv matutino 101 100.0 4185 99.7 593 (20.7) 118 (9.4) non-matutino 0 0.0 11 0.3 Tlaxcala Urb Púb matutino 173 69.5 16131 80.5 524 (10.6) 100 (6.6) 30.3 -17.9 non-matutino 76 30.5 3897 19.5 493 (19.8) 98 (13.9) 78.5 Rur Púb matutino 177 100.0 5560 97.1 475 (12.6) 94 (10.0) non-matutino 0 0.0 164 2.9 Edu Ind matutino 4 100.0 388 83.1 n/a n/a n/a n/a non-matutino 0 0.0 79 16.9 Cur Com matutino 4 100.0 220 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 155 100.0 2341 99.9 551 (12.4) 99 (8.2) non-matutino 0 0.0 2 0.1 Veracruz Urb Púb matutino 114 70.4 63137 75.8 512 (15.5) 97 (8.0) 2.0 -77.6 non-matutino 48 29.6 20205 24.2 514 (35.7) 107 (17.3) 81.6 Rur Púb matutino 88 97.8 57876 91.6 467 (12.1) 76 (8.1) n/a n/a non-matutino 2 2.2 5278 8.4 n/a n/a n/a n/a n/a Edu Ind matutino 145 100.0 12666 98.9 476 (12.8) 94 (12.0) non-matutino 0 0.0 135 1.1 Cur Com matutino 35 100.0 2107 100.0 526 (24.3) 115 (15.1) non-matutino 0 0.0 0 0.0 Priv matutino 111 91.7 6929 97.7 560 (11.8) 90 (9.2) n/a n/a non-matutino 10 8.3 163 2.3 n/a n/a n/a n/a n/a

El Turno Escolar 54

Yucatán Urb Púb matutino 227 95.4 21053 81.4 473 (11.0) 97 (5.6) n/a n/a non-matutino 11 4.6 4798 18.6 n/a n/a n/a n/a n/a Rur Púb matutino 141 92.8 5373 89.3 458 (11.9) 92 (10.2) n/a n/a non-matutino 11 7.2 643 10.7 n/a n/a n/a n/a n/a Edu Ind matutino 126 92.0 1958 92.2 458 (11.5) 83 (7.9) n/a n/a non-matutino 11 8.0 165 7.8 n/a n/a n/a n/a n/a Cur Com matutino 0 0.0 167 100.0 non-matutino 3 100.0 0 0.0 n/a n/a n/a n/a Priv matutino 113 100.0 2702 98.9 574 (19.3) 100 (8.5) non-matutino 0 0.0 29 1.1 Zacatecas Urb Púb matutino 129 83.8 14214 88.5 509 (13.4) 94 (6.8) n/a n/a non-matutino 25 16.2 1844 11.5 n/a n/a n/a n/a n/a Rur Púb matutino 123 100.0 12625 94.4 475 (12.1) 92 (9.0) non-matutino 0 0.0 746 5.6 Edu Ind matutino 0 0.0 0 0.0 non-matutino 0 0.0 0 0.0 Cur Com matutino 7 100.0 546 100.0 n/a n/a n/a n/a non-matutino 0 0.0 0 0.0 Priv matutino 130 100.0 1751 100.0 578 (17.5) 94 (8.2) non-matutino 0 0.0 0 0.0 12.9 -58.3 n/a – not made available – data suppressed due to small sample size (n < 30) and inflated standard errors associated with computed estimates