Predicting reading literacy and its improvement in the ...

Learning and Individual Differences 33 (2014) 1–11

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Learning and Individual Differences journal homepage: www.elsevier.com/locate/lindif

Predicting reading literacy and its improvement in the Polish national extension of the PISA study: The role of intelligence, trait- and state-anxiety, socio-economic status and school-type☆ Joanna Maria Rajchert ⁎, Tomasz Żułtak, Marek Smulczyk Institute of Philosophy and Sociology, Polish Academy of Sciences, ul. Nowy Świat 72, 00-330 Warszawa, Poland

a r t i c l e

i n f o

Article history: Received 7 August 2013 Received in revised form 6 April 2014 Accepted 7 April 2014 Available online xxxx Keywords: Reading literacy Intelligence Trait- and state-anxiety PISA

a b s t r a c t This study investigates predictors of reading literacy and its improvement during one year of education in secondary school. In a sample of 3352 Polish students attending vocational school and 3 types of high-schools (1708 males and 1644 females, mean age 16.7 at the beginning of the study), reading literacy was measured twice in the first and the second year of secondary education using the Program for International Student Assessment (PISA) tools. During the year, intelligence was also assessed and trait- and state-anxiety test was implemented after the second reading measurement. Results showed that vocational school students had lower reading scores than high-school students in the first reading test and this difference grew larger by the second test. Girls not only outperformed boys in reading but improved their skills during the year more than boys as well. We observed positive effects of intelligence and negative overall effect of trait- and state-anxiety. SES only predicted intelligence and first time reading performance. © 2014 Published by Elsevier Inc.

1. Introduction According to Holloway (1999), reading skills are essential to the academic achievement of middle- and high school students. In today's society, reading literacy introduces a bias because it provides advantages to those who acquire the necessary skills. Literacy provides access to literate institutions and has an impact on cognition or thinking processes (Olson, 1994). Achievement in reading literacy is a prerequisite for successful participation in most areas of adult life (Cunningham & Stanovich, 1998; Smith, Mikulecky, Kibby, Dreher, & Dole, 2000). The present study investigates some promising candidates to account for differences in reading literacy of secondary-school students: trait- and state-anxiety, non-verbal intelligence, previous reading performance, and socio-economic status (SES) of the family controlling for sex and school-type. In this paper, we rely on the data from the Organization for Economic Co-operation and Development (OECD) national extension of the Program for International Student Assessment (PISA) conducted in 2009 and 2010 on a sample of approximately 4000 Polish youths. The Polish national extension of the PISA study that we used was unique because,

☆ The study was conducted within the project: “The study concerning development of the methodology of estimation of the educational added value index (EAV)”. This project has been co-financed with EU funds from the European Social Fund (ESF/II/3/2009). Contact: panel@ifispan.waw.pl. ⁎ Corresponding author. Tel.: +48 22 826 71 81; fax: +48 22 826 78 23. E-mail address: [email protected] (J.M. Rajchert).

http://dx.doi.org/10.1016/j.lindif.2014.04.003 1041-6080/© 2014 Published by Elsevier Inc.

as far as we are aware, only one additional national panel study based on the OECD PISA Program titled Transitions from Education to Employment was conducted in Switzerland (TREE, 2008). The Polish national extension of the PISA study utilised the same instruments as the original PISA study to measure three literacy domains: reading, science and mathematics not once, as in the original PISA study, but twice in one year. Another difference with the original PISA study concerned the participants of the study. While the PISA study includes 15-year olds, who in Poland attend the last year of middle school, the national extension of the PISA study included a different sample of one year older secondary school students. Furthermore, in the national extension study some additional, psychological variables were measured, such as trait- and state-anxiety or intelligence. This enabled the examination for relationships between socio-economic and socio-cultural factors, competencies, personality traits, as well as features of the school environments at the beginning of secondary education. The present study will refer to an innovative reading literacy concept, which is concerned with the capacity of students to apply knowledge and skills in reading and the ability to analyse, reason and communicate effectively as they pose, solve and interpret problems in a variety of situations (OECD, 2009a). The purpose of the PISA reading literacy assessment is to monitor the reading proficiency of 15-yearolds. Each task in the assessment is designed to gather a specific piece of evidence about that proficiency by simulating a reading activity that a reader might carry out either inside or outside of school as an adolescent or as an adult. Each PISA study is focused on a different domain; for example in 2006 it was mathematics literacy, but in 2009 the major

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J.M. Rajchert et al. / Learning and Individual Differences 33 (2014) 1–11

domain was reading (capacity to understand, use, reflect on and engage in written text). Many studies have already explored the impact of intelligence and personality variables on academic achievement (e.g. Hattie, 2009; Hecht, Burgess, Torgesen, Wagner, & Rashotte, 2000; Laidra, Pullmann, & Alik, 2007; Spielberger, 1972), but none of these studies took into consideration all of those factors at once using Structural Equation Modeling (SEM), which we intended to do. In our paper, we will first briefly present the Polish educational system and then provide evidence for the relation of our predictors to general academic and reading achievement. One section will be devoted to the investigation of gender, schooltype and SES correlations with educational and reading performance. In the next section, we will concentrate more on the psychological factors included in our study that may predict reading performance — namely intelligence and anxiety as well as previous achievement in relation to current educational results. 1.1. Individual differences in reading In earlier studies literacy learning was attributed to individual differences in many cognitive and neuropsychological processes, such as visual processing, auditory discrimination, cross-modal transfer, eyemovements, serial memory, attention, association learning and rule learning. Currently poor reading ability is considered to be a consequence of deficits in phonological processing (Brady, Braze, & Fowler, 2011; Stanovich, 2008; Tunmer & Greeney, 2010) that include: encoding of phonological information (perception), gaining access to the performance of mental operations of phonological information (awareness), retrieving information from semantic memory (lexical retrieval), retaining information in working memory (short term verbal recall) and translating letters into phonological form (recoding). Problems with any of the listed operations may lead to reading disability (dyslexia). Although phonological processing impairments are thought to be the most important in reading literacy acquisition we were more interested in non-phonological causes of weaker reading ability. What is more, children with diagnosed learning disabilities were excluded from the PISA study sample that we used. Then what other factors may explain the differences in reading literacy? According to the constructivist theory, reading comprehension (tested in our study along with other reading abilities) is not only influenced by the reader's reading skills, but also by the reader's broad knowledge, cognitive development, culture and purpose of reading (Navarez, 2001). Thus it could be possible that previous reading literacy, intelligence, school-type or SES as indirect indicators of knowledge, and cognitive functioning of culture would also predict reading literacy change. 1.2. Polish secondary educational system There are four basic types of schools in the upper-secondary level in Poland. First, there is a 3-year general high-school, providing a broad education. The second type is a 3-year profession oriented highschool. The third type is a 4-year vocational secondary school which enables pupils to obtain a vocational qualifications diploma upon passing an exam. The last type is a 2 or 3-year basic vocational school. It is the only type of upper secondary school that does not allow students to take the nationwide exam, which gives access to tertiary education. Students with the highest national middle-school exam scores primarily prefer general education, while pupils with lower scores take vocational training. There are also differences according to sex, with girls being overrepresented in general high-schools and underrepresented in basic vocational schools (LDB, 2012). This fact is partially related to differences in middle-school exam scores, as girls tend to achieve higher scores (CEB, 2008). The Core Curriculums of the General Education for upper secondary schools refer to reading as the most important skill learned by the pupil — defined as “the ability to understand, use and reflective

processing of the text, including cultural texts, leading to the achievement of pupils' goals, personal development and active participation in the life of the society”. The curriculum concerning reading in vocational schools is less extensive than in the upper secondary schools as it contains 160 h of learning during 3 years of education. The upper secondary school curriculum realisation needs at least 360 h of learning but it might be even more expanded depending on the school profile. 1.3. Gender, school-type and SES as predictors of educational and reading performance 1.3.1. Gender Numerous studies using samples from different countries provide evidence that girls outperform boys in school grades (e.g. Epstein, Elwood, Jey, & Maw, 1998; Rosander, Bäckström, & Sternberg, 2011; Spinath, Freudenthauler, & Neubaurer, 2010). This effect is frequently attributed to societal changes in attitudes towards equal opportunities for men and women, but also to differences in personality, motivation, stereotyping, and social behaviour such as aggression (Freudenthaler, Spinath, & Neubauer, 2008; Hicks, Johnson, Iacono, & McGue, 2008; Steinmayr & Spinath, 2008). When domain-specific performance is taken into account, girls tend to have better grades in language and reading related subjects (Spinath, Spinath, & Plomin, 2008; Spinath et al., 2010). This result is not surprising taking into consideration that girls have higher verbal ability and also gender stereotyping in education (Andre, Whigham, Hendrickson, & Chambers, 1999; Hyde, 2005). At the same time, some studies show that on major standardised tests girls do not perform better than boys (Deary, Strand, Smith, & Fernandes, 2006), but surely that cannot be true for all standardised tests. One such major literacy measurement is PIRLS (Progress in National Reading Literacy Study by the International Association for the Evaluation of Educational Achievement), measuring reading performance of 4th grade students (Mullis, Martin, Kennedy, & Foy, 2007). This study shows that in almost all countries and provinces girls scored higher than boys. This was also true for Poland, where girls achieved on average 17 more points than boys. This result shows that the gap in reading literacy between boys and girls appears early in the course of education. The PISA study focuses on older, 15-year-old students. The report from 2009 prepared for Poland (MEN, 2009) indicates that sex is one of the most significant predictors of reading literacy. Girls were able to solve the most difficult reading tasks 2.5 times more frequently than boys. They also obtained on average 50 more points than boys. 1.3.2. School-type In the same PISA 2009 Polish report, we find information on differences between secondary school-types in reading literacy in Poland (based on the national extension of the PISA 2009 study). Usually the vocational school has the lowest requirements and the general high school has the highest requirements, while the other two types of professional secondary schools are in the middle. Also the curriculums for reading are different. The general high school has the most expanded program, the profession oriented high school has a less expanded program, and the vocational school has the narrowest curriculum. This stratification was reflected in reading literacy measured by the PISA instruments. The best results were achieved by general high school students, mediocre results were achieved by profession oriented high school and 4-year vocational secondary school students and the lowest reading scores were obtained by basic vocational secondary school students. The school-type explained 49% of the variance in reading literacy. 1.3.3. Socio-economic status The type of school Polish students attend is also related to their social status and in particular their parents' education. Educated parents support their children more in learning, motivate them more to study,


provide better environments and transmit values that appreciate education (e.g. Davis-Kean, 2005; Roksa & Poter, 2011). Parental education accounted for 11% of the variance in reading literacy in the Polish extension of the PISA 2009 study and for 14% in the original PISA study in Poland. The PISA study uses also HISEI (Highest Standard International Socio-Economic Index of occupation Status) by Ganzeboom, De Graaf, and Treiman (1992) to measure SES of the family described in more detail in Section 2.2.4 of this paper. In the PISA 2009 study, HISEI correlated (r = .34) with reading literacy in the Polish sample. Correlations between 0.3 and 0.7 were also obtained in studies using other SES indices (parental income, occupation or educational attainment) and younger samples (White, 1982; Hecht et al., 2000; Kirby, Parilla, & Pfeiffer, 2003; Myrberg & Rosén, 2008; Park, 2008; Yang-Hansen, 2008). In the meta-analysis by Hattie (2009) based on 499 studies, the effect of SES on general educational achievement was d = .57, which was considered a notable influence. 1.4. Intelligence, state- and trait-anxiety and previous achievement relation to educational performance 1.4.1. Intelligence In the national extension of PISA, compared to the original PISA study, three new variables were included — general intelligence and state- and trait-anxiety. Spinath et al. (2010) stated that “the study of school achievement would be incomplete without considering general intelligence” (p. 481). Indeed, general intelligence in many studies was the strongest predictor of academic performance (e.g. Gottfredson, 2002, Gustafsson & Undheim, 1996; Rosander et al., 2011; Spinath et al., 2010). In the meta-analysis by Hansford and Hattie (1982), an average correlation size of r = .51 (d = 1.15) between intelligence and achievement was reported, although the relation of general intelligence to reading achievement tends to be somewhat smaller than that to math achievement (e.g. Spinath et al., 2010). Gender differences in global intelligence were rarely found (e.g. Feingold, 1988; Hedges & Nowell, 1995), but in some studies minor differences existed. In the Polish evaluation of Raven Standard Progressive Matrices, which measure global, general or non-verbal intelligence, differences between 16-year-old girls and boys emerged, although there was no such variability in other age groups (Jaworowska & Szustrowa, 2000). Also, Lynn and Irving (2004) found in the metaanalysis based on 57 Raven Progressive Matrices studies that although there were no gender differences among children aged 6–14 years, males obtain higher means from the age of 15 and the advantage is d = .33. Other studies using more complex intelligence measures showed that boys achieve better scores in verbal, numerical visuospatial and general intelligence (Freudenthaler et al., 2008; Spinath et al., 2010). On the other hand, a meta-analysis by Hyde (2005) showed that girls tend to score higher on the verbal ability component of intelligence, while boys score higher on numerical scales. 1.4.2. Anxiety Another variable that was included in our analyses was trait- and state-anxiety. State-anxiety was defined as a transitory condition of perceived tension emerging as a response to environmental cues and trait-anxiety as a relatively stable condition of anxiety proneness (Spielberger, Gorsuch, & Lushene, 1970). Girls are usually higher on trait- and state-anxiety (Wrzesniewski, Sosonowski, Jaworowska, & Fecenec, 2006) and also other anxiety measures like school or test-anxiety and neuroticism (Chamorro-Premuzic & Arteche, 2008; Freudenthaler et al., 2008; Morris, Finkelstein, & Fisher, 1976; Rosander et al., 2011). Studies exploring the relationship between anxiety and academic performance take into account mainly trait- and state-anxiety (Spielberger, 1966) or test-anxiety (Zeidner, 1998). A large number of these investigations show a negative relationship between trait-

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anxiety and academic performance usually operationalised as Grade Point Average, the result in an academic achievement test or final examination or graduation grade (e. g. Anson, Bernstein, & Hobfoll, 1984; Grinnell & Kyte, 1979; Hulse et al., 2007; Petrie & Russel, 1995; Siddique, LaSalle-Ricci, Glass, Arncoff, & Diaz, 2006; Spielberger, 1966). Spielberger (1966, 1972) argued in his papers that trait-anxiety affects state-anxiety in such a way that in a threatening situation (e. g. a test or examination situation) a person with higher trait-anxiety will react with higher state-anxiety, which would then impair performance more in complicated than in simple tasks. There are also many studies in which the relation between testanxiety and academic achievement has been analysed. These studies predominantly show that more test-anxious students have poorer academic performance (e.g., Allen, 1970; Chapell et al., 2005; Eum & Rice, 2011; Feingold, 1994; Freudenthaler et al., 2008; Hunslay, 1985; Muntz, Costello, & Korabik, 1975; Nathan & Fordham, 2000; Raffety, Smith, & Ptacek, 1997; Sarason, 1960; Zeidner, 1998). The meta-analysis results indicate as well that academic performance deteriorates along with anxiety growth (Hembree, 1988; Seipp & Heinrich, 1991) and a stronger effect is observed for testanxiety (d = − .46) than for general anxiety (d = − .32). It was also found through a meta-analysis that anxiety reducing interventions contribute to an improvement of school grades with a medium size effect ranging from d = .39 for academic test results to d = .61 for grade-averages (Schwarzer, 1990) with a mean effect size of d = .40 (Hattie, 2009). 1.4.3. Previous performance Another candidate to account for differences in reading literacy was previous reading performance. According to the meta-analysis by Hattie (2009), prior achievement is one of the most powerful factors predicting educational attainment. Based on 3607 studies, the effect size was d = .67. Prior attainment leads to gains in achievement on 48% of occasions. The Mathew's effect of the rich getting richer and the poor getting poorer was also found for reading literacy (Stanovich, 1986). It was proposed that some cognitive processes that are linked with reading ability may be the effects of reading efficiency and also that differentially advantaged individuals are exposed to a nonrandom distribution of environmental quality. 1.5. Present study The main objective of this study was to document how intelligence, anxiety and previous reading performance relate to reading performance one year later. We tested one general theoretical model that included equations predicting intelligence, state- and trait-anxiety and reading literacy at time one and time two. In our model, there were three exogenous variables: gender, SES and school-type and five main latent endogenous variables: intelligence, state- and trait-anxiety, and reading performance measured twice. Intelligence was predicted by school-type and SES. Trait-anxiety was predicted by gender and intelligence. State-anxiety was predicted by gender, trait-anxiety and time one reading performance. Reading literacy measured in 2009 was predicted by school-type, gender, SES and intelligence. In the equation for reading literacy measured one year later in 2010, school-type, gender, intelligence, time one reading literacy, trait-anxiety and state-anxiety were included. The hypothesised model is presented on Fig. 1. The specific hypotheses were as follows. Girls will be higher on traitand state-anxiety than boys and will also outperform boys and improve more than boys in reading. Students in general high schools would have better reading scores in both measurements than students in other school-types and the discrepancy will persist when we control for the effect of the first measurement. The primary reason for such differences is the relationship between intelligence and reading skills, and selection of school-type, as students with higher intelligence and reading skills more frequently choose general education. Although it is possible that

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Fig. 1. The theoretically driven model. Note. The solid lines depict positive and the dotted lines indicate the negative associations. Sex is coded: 0 – boys and 1 – girls, and the school-type is coded: 0 – general high-school and 1 – all the other school-types.

school-type also has an effect on change in reading literacy after one year of schooling. The differences in reading curriculums (different number of hours spent on the development of reading literacy) may additionally expand the discrepancy in reading skills already present at the first measurement. General high school students will have a higher intelligence score than students in other school-types and more intelligent students will improve their reading literacy more than students with lower abilities (intelligence will have an effect on second reading measurement controlling for the first measurement). Participants with higher SES will perform better in the first reading test, improve more during the year and will have a higher intelligence score, which will be negatively related to trait-anxiety. Some studies support the possibility that intelligence is related to trait-anxiety (Calvin, Koons, Bingham, & Fink, 1955; Spielberger, 1958; Wrzesniewski et al., 2006; Zweibelson, 1956). In our prediction, we followed the idea that less intelligent children would have a bigger risk of experiencing anxiety in every-day and school situations. They may feel disoriented when confronted with new or more difficult tasks like testing, which may lead to more frequent feelings of anxiety (the causal connection could also go the other way around or be reciprocal, but we could not explore it because, in the dataset that we used, intelligence was measured 6 months before anxiety). On the other hand, more intelligent children would tend to find solutions to a new problem more quickly, which would leave them less anxious and more confident. Trait-anxiety will be positively related to state-anxiety, which in turn will partially mediate the relationship between trait-anxiety and second reading score thus reading literacy will improve more among students with lower anxiety proneness. We also predicted that the results of a reading test taken one year earlier would be negatively related to state-anxiety measured one year later, just after the second reading measurement. We assumed that teenagers who scored worse on the first reading test would experience more state-anxiety related to the

second measurement as a consequence of the perceived difficulty of the task, even though they were not informed about their result after the test. State-anxiety would then predict time two reading performance, and thus partly mediate the relation between two reading measurement results. This will be the first study in which psychological variables such as intelligence and anxiety will be analysed along with the PISA standardised reading literacy data, which were gathered twice, using the same measurement apparatus. That allowed us to check for a mediating effect of time one reading performance between SES, intelligence and time two reading literacy. We could also explore whether schooltype, anxiety, gender and intelligence predict time two reading score over time one performance. In other words, we were able to test whether teenagers improved or worsened their reading literacy with regard to the first result in a reading test depending on the listed variables above. 2. Method The data used in our analysis came from the Polish extension of the PISA 2009 study. In the national extension study, the same PISA achievement measurement instruments were used on a different target population. While in the main PISA study the target population consists of 15-year old students (in Poland they are mostly last-grade middleschool students), in the extension study the participants were firstgrade students in Polish upper-secondary schools (regardless of their age). In the extension study, compared to the main PISA study, additional measures, such as State- and Trait-Anxiety Inventory (STAI), were also implemented and repeated measurement of the PISA achievements after one year of upper-secondary education were included. The data were gathered according to the procedure developed by the experts from the Polish Central Examination Board, while the Institute of Philosophy and Sociology of the Polish Academy of Sciences conducted the study. The estimates of students' achievement were


computed by the Australian Council for Educational Research, providing achievement score estimation for the PISA.

2.1. Participants and procedure A sample of first-grade students at Polish upper-secondary schools was selected in a stratified two stage sampling procedure. At the first stage, the sampling units were schools divided into four strata by school-type. Within each strata, schools were randomly selected with the probability proportional to the number of classes at stage one. The number of schools selected from each strata was proportional to the number of students in schools of a given type. 100 general highschools, 6 profession oriented high-schools, 54 vocational secondary schools and 40 basic vocational schools were sampled. At the second stage, one first-grade class in each school was selected at random with equal probabilities. Although the sample selection was technically twostage, formally it may be seen as a one-stage stratified cluster sampling with equal probabilities of selection and classes constituting the clusters. Within schools, the students with disabilities and those who did not have sufficient knowledge of the Polish language were excluded from the study. In each wave all eligible members of a class selected for the sample present at school on the day of the study participated in the study. As a result, the number of participants who took part in each wave was slightly different with 4951 participants in the first wave, 4041 participants in the second wave, 3989 participants in third wave and 3472 students who took part in all three waves. Because the age range of the participants was large due to some basic vocational school students – most likely several time graderepeaters – we decided to include in our analysis only the students who were between 15.7 and 17.7 years old at the time of the first wave (±1 year from the mean age). Therefore, the number of students included in our analysis was 3352. Age distribution in various schooltypes was very similar. During the first measurement, the youngest general and profession oriented high-school students were 15.7 years old, 4-year vocational school pupils were 15.8 years old and basic vocational school students were 16.1 years old. The eldest of the general highschool students were 17.5 years old and in other school-types the eldest students were 17.7 years old. The details of the composition of the analysed group are presented in Table 1. During the first wave which took part in March 2009, the students completed the reading skills tests in class groups first. Six months later, in October 2009, the adolescents, who were already secondgrade students, participated in the second wave of the study. During that wave intelligence was measured along with other psychological variables (self-esteem and hope), which we did not analyse in the current study. The third wave was conducted another six months later in April 2010 and included a consecutive measurement of reading literacy and the state- and trait-anxiety survey which followed the reading skills test. After the first wave of the study, post-stratification weights were computed to adjust the distribution of students of different schooltypes to known population frequencies. These weights were incorporated into the analysis conducted with BRR (Balanced Repeated Replication) and into SEM.

Table 1 Composition of analysed sample with reference to sex and the school-type. School-type

Male

Female

Sum

General high-school Profession oriented high-school Vocational school Basic vocational school Sum

756 53 604 295 1708

1219 42 268 115 1644

1975 95 872 410 3352

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2.2. Measures 2.2.1. Reading literacy Polish translations of the PISA instrument was used to measure reading performance. The instrument was not different from the original version. The instrument was designed to measure 3 types of literacy: reading, mathematical and scientific. A two-hour rotated test design included an extensive test on the major domain (in 2009: reading) and smaller subtests for two minor domains (mathematics and science). All domains were linked through the use of common test items across booklets, and plausible values were computed as student proficiency estimates. For each domain, sub-sets of link items provided the basis for measuring trends. The results of PISA have a high degree of validity and reliability (OECD, 2009b). The dataset includes plausible value (PV) estimates for each of the above three subjects, standardised into a score with a mean of 500 and a standard deviation of 100. In the study, 13 versions (booklets) of the PISA test were used. Each booklet had a similar level of difficulty. A detailed description of the development of the equal test versions can be found in the manuals (OECD 2009a, 2009b). In the present study, only the reading literacy scores were analysed. The OECD defines reading literacy as an “individual's capacity to: understand, use, reflect on and engage with written texts, in order to achieve one's goals, to develop one's knowledge and potential, and to participate in society” (OECD, 2009a, p. 14). In PISA, reading literacy is assessed in relation to the: 1. text format (lists, graphs, diagrams, a range of prose forms, such as narration, argumentation and exposition), 2. reading processes (retrieving information, forming a broad general understanding of the text, interpreting it, reflecting on its contents), and 3. situations (personal use or educational use). Example tasks assessing reading literacy can be found in the PISA documentation (available online: http://www. oecd.org/pisa/pisaproducts/pisa2009/). After the first and second measurement, the general outcomes for the particular schools, but not for the individual students, were sent to the schools. 2.2.2. Intelligence The Polish adaptation of Raven's Standard Progressive Matrices (RSPM), by Jaworowska and Szustrowa (2000) were used to measure intelligence defined as pure non-verbal reasoning. The RSPM is a penciland paper multiple choice test of non-verbal reasoning ability or general intelligence (Raven, Raven, & Court, 2003). The RSPM consists of 60 items arranged in five sets (A, B, C, D, & E) of 12 items each. Each item contains a figure with a missing piece. Below the figure, there are either six (sets A & B) or eight (sets C through E) alternative pieces to complete the figure, only one of which is correct. For each test item, the participant is asked to identify the missing segment required to complete a larger pattern. Internal consistency of RSPM in the current sample was very good with Cronbach's alpha of .90. 2.2.3. Anxiety To measure anxiety, we used the State-Trait Anxiety Inventory (STAI) by Spielberger et al. (1970) in its Polish adaptation by Wrzesniewski et al. (2006). It is a self-report instrument that differentiates between the temporary condition of state-anxiety and the longstanding quality of trait-anxiety. The STAI consists of 40 items. The test is split into a state-anxiety scale (example items: “I am tense”, “I feel secure”) and a trait-anxiety scale (example items: “I worry too much over something that really doesn't matter”, “I am content”), each having 20 items. The scale uses a 3-point (1 = almost never, 2 = sometimes, 3 = often) Likert-type scale, where higher scores indicate higher levels of anxiety. Separate scores for state- and trait-anxiety can be found (maximum score = 60 in each scale). Internal consistency for the Polish version of STAI in the 15–16 year-old sample was very good and ranged between .82 (trait among boys) and .88 (state among girls). In current sample, Cronbach's alphas were also very good: .87 for state-anxiety and .84 for trait-anxiety.

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2.2.4. Socio-economic status (SES) SES of participants was measured by HISEI — the highest of the parents' International Socio-Economic Index (ISEI). It is widely used in studies of social mobility and education, including analysis on the PISA data. ISEI scores are assigned to each category of International Standard Classification of Occupations (ISCO), so individuals with the same occupation have the same ISEI score. ISEI scores are computed as a weighted mean of years of education and earnings averaged over all individuals in a given occupation in the standardised dataset (Ganzeboom et al., 1992). As the study started in early 2009, ISCO-88 and ISEI-88 versions of classification and index were used. 3. Results Concerning the analysis of the national extension study data, there are two difficulties that must be overcome, with the first of those being an imperfect reliability of personality inventories and attainment measures. Those measurement errors tend to attenuate the observed relationship between analysed variables. The second obstacle refers to the complex design of the sample (comprehensively described in section 2.1) which requires the use of specific statistical analyses to ensure proper estimates of the model's coefficients and their standard errors. The first problem, in the case of psychological questionnaires, may be addressed by the use of SEM with latent variables. It was demonstrated that estimates of the effects between latent variables are not attenuated due to imperfect reliability (e.g. Bollen, 1989). In the case of PISA achievement measures, the same problem may be solved by the use of PVs. Technically speaking, PVs are random draws from posteriori distributions, describing how frequently students of a given ability level occur among students of a given response pattern. The posteriori distributions are estimated due to assumptions of the Rasch measurement model, values of item parameters and assumptions about distribution of ability in the population. Drawing several estimates – in the case of PISA there are 5 draws – enabling one to account for uncertainty about the true values of students' abilities in further computations. It is shown that PVs permit one to obtain unbiased estimates of the model coefficients (Monseur & Adams, 2009; OECD 2009b). The second problem concerning the complexity of the sample with respect to PISA data is often resolved with the application of the Balanced Repeated Replication (BRR) method (OECD 2009b, Rao & Shao, 1999). It is based on a repeated estimation of parameters of interest across multiple (108 in case of our dataset) subsamples to reconstruct the proper value of estimators' variance. BRR also allows one to easily incorporate post-stratification weights into analysis. We used this approach in computing summary statistics, t-tests and correlations, using R v15.0 statistical package with library “survey” v3.28. It should be noted that under a complex sample design, many difficulties occur. For example the F-test, used in ANOVA/MANOVA analysis, cannot be performed, and the r-squared statistic should be interpreted with caution, as it refers to the weighted sum of squares (Pfeffermann, 1993). We employed three analytic approaches. First we used Student's t-tests to examine differences in anxiety, intelligence and reading literacy due to participants' gender without controlling for other variables. Next we implemented correlational analyses to determine associations between SES, intelligence, anxiety and both results of reading literacy. Finally, we fitted the SEM model to determine whether reading literacy might be predicted from gender, schooltype and SES mediated by intelligence, trait- and state-anxiety as well as reading literacy measured one year earlier. 3.1. Sex differences in state- and trait-anxiety, intelligence and reading performance Descriptive statistics, student's t-test results and Cohen's d effect sizes for sex differences in anxiety, non-verbal intelligence measured by RPSM and both reading literacy scores are provided in Table 2.

Table 2 Descriptive statistics and sex differences in the study variables. M

SD

M girls SD girls M boys SD boys t

Intelligence 48.2 7.2 48.5 T-anxiety 40.3 8.4 41.4 S-anxiety 36.3 8.2 36.5 READ 1 517.7 90.6 539.5 READ 2 519.8 93.9 544.0

6.8 8.2 8.1 81.6 83.5

47.9 39.3 36.1 498.7 498.6

7.5 8.4 8.4 93.7 97.3

p

d

1.485 .141 .08 6.306 b.001 .24 1.459 .148 .05 6.457 b.001 .45 6.766 b.001 .48

Note: d = Cohen's d.

Consistent with previous reports, girls achieved higher mean reading literacy scores in the first and second measurement and also scored higher on trait-anxiety. The magnitude of the effect size for sex differences was rather small for trait-anxiety and medium for both reading literacy scores. Contrary to our predictions, girls were not higher than boys on state-anxiety. Intelligence did not differ among boys or girls. 3.2. Correlations between reading literacy and predictors Table 3 presents the correlations among the study variables (except school-type and sex which were categorical variables). SES indexed as HISEI was positively correlated with the RSPM score and reading literacy scores and very weakly and negatively with state-anxiety. SES was not associated with trait-anxiety. RSPM was positively and moderately associated with reading scores and negatively but weakly with traitand state-anxiety. Reading performance at time one and two was positively and moderately related to intelligence and negatively and poorly to trait- and state-anxiety. Large positive associations were observed between reading literacy scores and between trait-anxiety and stateanxiety. 3.3. Model predicting reading literacy SEM using Maximum Likelihood Estimation with PVs was conducted using MPlus v6.1 software. In SEM, we decided not to use BRR, but explicitly describe the sample design (belonging to strata and clusters) and post-stratification weights to statistical software (Asparouhov, 2005). In the case of SEM analysis, such an operation allows us to compute a full set of model indices, while it is impossible using BRR. Four goodness-of-fit indices proposed by Marsh, Hau, and Grayson (2005) were employed to evaluate the adequacy of the model fit: χ square, CFI, TLI and RMSEA. According to Hu and Bentler (1999), insignificant χ square, CFI and TLI over .90, and RMSEA below .06 indicate good model fit. Because χ square is highly sensitive to the sample size (it is usually significant when samples as large as 3000 participants are analysed; Brown, 2006), in our analysis we relied more on other indices of model fit. All continuous variables were standardised before being included in the model and categorical variables were dummy coded as follows: 0 — boys, 1 — girls and 0 — high school, 1 — other secondary schools. In consequence, one can compare the effect of continuous (but not categorical) predictors on the criterion variable and all unstandardised regression coefficients (b) can be interpreted analogously to Cohen's d effect size statistic (but under the control of the Table 3 Correlations among the study variables.

1. HISEI 2. Intelligence 3. T-anxiety 4. S-anxiety 5. READ 1 6. READ 2 ⁎ p b .05. ⁎⁎ p b .001.

1

2

3

4

5

6

–

.239⁎⁎ –

.034 −.079⁎⁎

.034⁎ −.079⁎⁎ .623⁎⁎

.327⁎⁎ .487⁎⁎ −.95⁎⁎ −.136⁎⁎

.326⁎⁎ .492⁎⁎ −.109⁎⁎ −.176⁎⁎ .867⁎⁎

–

–

–

–


impact of other variables included in the model). Both reading literacy scores were originally expressed on the same scale. To preserve this property after standardisation of variables, the result of the second test was standardised due to the mean and standard deviation of the first one. As a result, the effect of a given predictor on growth of reading literacy may be computed as the difference between the total effect of this predictor and the effect of this predictor mediated by the first reading literacy test. In our model, we let some of the measurement errors of the STAI items correlate. When we tested the theoretically driven model, it turned out that it fit the data quite well (χ square = 6247, DF = 1219, p b .001, RMSEA = .036, CFI = .872, TLI = .862), although the CFI and TLI indices were below the cut-off point and the χ square was significant. To improve the model fit, one insignificant path was dropped from the model (from HISEI to second reading literacy score) and one additional path (from sex to intelligence) was added. We also removed two items that loaded poorly on the state- and trait-anxiety scale (item 34 of the trait-anxiety scale “I try not to notice the difficulties and crises” that loaded = .09 and item 18 of the state-anxiety scale “I feel overexcited and rattled” that loaded = .11). After those changes, the model fit the data slightly better although the χ square (DF = 1218) = 6182.6 was still significant (p b .001). Other indices of fit were good (RMSEA = .035) or close to acceptable (CFI = .883 and TLI = .873) for model fit. All 19 state- and 19 trait-anxiety items (after one item from each scale was excluded) as well as 6 sets of RPSM tasks loaded significantly to latent variables with between .54 and .79 for RPSM, between .24 and .66 for trait-anxiety and between .33 and .70 for state-anxiety. The estimates for the other paths are presented in Fig. 2. All the path estimates were significant. The score in RPSM was predicted by school-type, gender and HISEI. All three variables accounted for 26% of the variance in intelligence.

7

Compared to general high-school students, teenagers who attended vocational schools had lower intelligence by −1.43 SD of RPSM, which can be viewed as a very substantial effect. Profiled high-school and technical secondary school students scored lower on RPSM than general high school pupils and those effects could be classified as medium according to Cohen (1988). As predicted, youth with higher HISEI scored higher on RPSM, but the effect was very small. Controlling for school-type, gender also significantly, but weakly, predicted RPSM with boys scoring higher than girls. The described effect, different from the result obtained using Student's t-test (no sex difference in RPSM was observed), was due to the disproportionate number of boys and girls attending different school-types and is discussed in detail further. The first reading literacy measurement was affected by school-type in the way we had anticipated — general high school students had higher scores than students from other schools. The effects of schooltype were medium to large. Sex had a rather small direct effect on reading literacy with girls outperforming boys in reading. Having also included the indirect path through intelligence (b = − .08), the total impact of gender was even smaller (b = .14), although still positive and significant. SES also weakly and positively affected time one reading literacy, while intelligence accounted for a moderate difference in reading performance with more intelligent teenagers scoring higher. The total effect of HISEI (direct and mediated by intelligence) on time one reading performance was thus positive (b = .12) but small. All predictors accounted for 61% of variance in time one reading literacy. Trait-anxiety, as expected, was higher among girls than among boys. Also, as anticipated, the RPSM score had a small negative effect on traitanxiety. Consequently, sex also had a small indirect effect on traitanxiety through intelligence (b = .02). The total effect of sex on traitanxiety (.35) was positive and moderate. Both RPSM and sex accounted for only a very small amount of variance in trait-anxiety, namely 4%.

Fig. 2. The empirical model with the unstandardized path estimates (b). Note. All the continuous variables were standardized. Sex is coded 0: – boys and 1 – girls, and school-type is coded: 0 – general high-school and 1 – all the other school-types. Some of the measurement errors of the trait- and state-anxiety items were correlated. In the reading tests only the subset of tasks enclosing from 14 to 59 tasks, out of 108 possible, was presented to each student. The latent-equivalent reading scores were included in the model by use of plausible values.

8


State-anxiety was predicted by trait-anxiety, sex and prior reading performance, with the most important predictor being trait-anxiety, which had a strong, positive effect on the criterion variable. Also reading performance measured one year earlier had a small negative impact on state-anxiety. Contrary to our expectations, boys had a higher stateanxiety than girls, although the impact of sex on state-anxiety through trait-anxiety was still positive (b = .23). The total effect of sex on state-anxiety (direct and mediated through trait-anxiety and reading performance) was also positive with girls having higher state-anxiety scores. Nevertheless, we have arrived at the conclusion that when we control for trait-anxiety and prior performance, boys experience more anxious feelings when confronted with testing material than girls although the effect was small. All three predictors accounted for 50% of the variance in state-anxiety. The last criterion variable that we included in our model was time two reading literacy, which was predicted by school-type, gender, RPSM, prior reading literacy and trait- and state-anxiety. All the included variables explained 79% of the variance in reading literacy with prior performance heaving the strongest positive effect. Its direct effect on time two reading performance was medium and the indirect path through state-anxiety did not add much to the total effect (b = .007). The second important predictor of time two reading score was the school-type. The biggest difference in reading scores was observed between general high-school and vocational school students. Smaller differences were noted between high-school and profiled high-school and technical school students. The direct effect of school-type on time two reading literacy was lessened by the variance related to 3 indirect paths through: (1) prior performance, (2) intelligence and (3) intelligence and prior performance. Total effects (direct and indirect) of school-type on time two literacy were: b = −1.86 for vocational school, b = −.87 for profiled high-school, and b = −.78 for technical school compared to general high-school. All those effects were large. Considering change in reading literacy due to school-type (total effect of schooltype diminished by the effect of school-type mediated by the first reading test), effects were small to medium: b = − 0.69 for vocational school, b = −.41 for profiled high-school, and b = −.31 for technical school compared to general high-school. The next important predictor of time two literacy was intelligence. The direct effect of this variable measured by RPSM on time two reading performance was positive and small, but together with the prior literacy mediation effect (b = .31) the impact of intelligence on reading literacy was medium (b = .47). The influence of SES (HISEI) on time two literacy was totally mediated by intelligence and prior literacy and had no direct impact. The total effect of sex differences on time two reading was mediated, albeit very weakly, by many other variables: (1) both trait- and stateanxiety, b = −.01, (2) solely trait-anxiety, b = .01 (3), prior reading literacy, b = .09, and (4) intelligence, b = −.02. However, sex still had an additional, but small direct effect on time two reading skills with girls achieving better scores than boys. Summarising all the paths, the gender effect was small and positive (b = .16). The smallest direct effect was connected to trait-anxiety. Controlling for all other variables in the model, trait-anxiety slightly improved time two reading literacy scores. At the same time, trait-anxiety also negatively influenced time two reading performance through state-anxiety, but the strength of that mediation path was very small (b = − .07). The total impact of trait-anxiety on reading skill was then negative, but very small (b = −.03). A small amount of variability in reading literacy was also accounted for by state-anxiety, which negatively predicted the criterion variable. 4. Discussion We sought to examine the effects of gender, school-type, SES, intelligence, anxiety and prior reading performance on later performance with the emphasis on understanding how trait- and state-anxiety affect

reading literacy in secondary school students controlling for other studied variables. Specifically, we examined gender differences in predictors and the criterion variable and also analysed zero-order correlations between study variables. In a further step, we tested how the theoretical driven model fit our data. Consistent with the literature (e.g. Epstein et al., 1998) and our hypothesis, among Polish 16- and 17-year-old secondary school students, girls obtained better reading scores in the first as well as in the second reading literacy measurement. Also in line with some previous findings (e.g. Feingold, 1988) no gender differences in intelligence were found, when no other variables, such as school-type were controlled for. Mean differences analysis also showed that although girls scored higher on trait-anxiety, which was in line with prior results (e.g. Wrzesniewski et al., 2006; Zeidner, 1998), there were no sex differences in stateanxiety. A correlation analysis also confirmed our hypotheses regarding a positive association between prior and consecutive reading literacy, SES and intelligence. In accordance with the literature review (e.g. Hembree, 1988; Seipp & Heinrich, 1991) trait- and stateanxiety were negatively correlated with reading literacy and also with intelligence although prior research concerning intelligence and anxiety association showed mixed results (e.g. Calvin et al., 1955; Spielberger, 1958). However, those were only preliminary results without control for other variables. In our SEM model, an additional important predictor, namely school-type, was accounted for. Also consistent with previous results (MEN, 2009), school-type was the strongest predictor of reading skills for both time one and time two measurements. Differences due to school-type between high-school and vocational school students were tremendous. It seems likely and logical that vocational school is not only chosen by children with lower abilities, but is also much less effective in developing reading skills than high school. Given the big difference in the extensiveness of the reading curriculum between Polish secondary school-types, the obtained result might be well understood. Even when intelligence, sex and prior reading performance were kept constant, vocational school students developed their reading skills for one year less than their peers from high-schools and thus the gap in reading skills between high-school and vocational school students increased. Another very important predictor of reading score was prior reading performance, which was consistent with other results (e.g. Hattie, 2009). We could observe that although its effect was substantial, it left some space for variability due to other predictors such as intelligence, sex and anxiety. Controlling for prior reading skill and other factors included in the model, more intelligent children improved their reading skills even more. On the other hand, state- and trait-anxiety slightly impaired such development. In our view, the most interesting finding, in fact, concerns anxiety. Its impact on reading scores is complicated by gender and it also differs for trait- and state- aspects of the variable. According to the literature listed above, girls obtain higher trait- and state-anxiety than boys. This was also true to some extent for our study — girls were higher on traitanxiety. Boys, however, had higher state-anxiety, but only when we controlled for trait-anxiety and prior reading achievement. In the database that we used, state-anxiety was measured just after the PISA test using the 2009 version of the instrument which mainly included reading tasks and thus state-anxiety was related to the recent experience of doing reading exercises. We could then presume that keeping traitanxiety and even previous performance at the same level, boys felt more distressed mostly with the reading test compared to girls. When we did not include trait-anxiety in the equation, girls have higher state-anxiety scores, just as one would expect. This finding is so interesting because it adds another possible candidate which could account for boys' underachievement in reading. Other studies that we are aware of (e.g. Rosander et al., 2011; Spinath et al., 2010) mostly concentrate on the sex gap in educational achievement and some facet of


anxiety (e.g. school or math anxiety) or neuroticism and arrive at the conclusion that those personality traits may impair or support performance predominantly in girls. Little is known about the state-anxiety role in boys taking reading tests. It would be too much to presume that our state-anxiety measurement taken right after the reading test, to some extent resembles the relation between state-anxiety and math test for girls. But if that second relation was true, we could speculate that in both cases gender stereotypes (Andre et al., 1999; Jacobs, 1991; Jacobs & Weisz, 1994) as well as gender differences in some cognitive abilities in particular may play a role. Another interesting finding, which was different from our expectations, concerned trait-anxiety which had a positive effect (and not negative as was hypothesised) on time two reading score when all other variables were controlled for, including state-anxiety and gender. The positive effect of anxiety on educational achievement was also observed in some other studies (Calapoglu, Sahin-Calapoglu, Karacop, Soyoz, & Avsaroglu, 2011; Fernandez-Casillo, Gutierrez-Rojas, & Esperanza, 2009; Mellanby & Zimdars, 2011). It is possible that such anxiety proneness, when devoid of current feelings of anxiety, can be related to higher motivation for learning or higher failure anxiety (Convington, 2009) and thus leads to slightly better results on the time two reading test. In our model predicting reading literacy, sex differences also played an important role, although the gender gap in reading scores was not as big as one could have expected according to the literature. The total sex effect depended strongly on school-type because girls more frequently attend high-school, where reading education is more effective. But when we controlled for school-type (and also other variables), sex still was responsible for a small effect in both reading scores. It is worth noting though, that girls improved their reading scores in the second test more than boys, so the sex difference in reading literacy increased over time. The differences between boys and girls in reading were visible in the first measurement as well, but if both sexes improved or worsened their scores with the same intensity in the second test, then we would not observe sex variability in that second measurement. It is also interesting that girls obtained better scores in reading tests even though in our model being a girl had a negative effect on intelligence. It should be noticed, that in the case of reading literacy, the sex effect on intelligence was present only when school-type was controlled for. It stems from the fact that girls tend to choose general high-school much more often than boys, although there are no significant differences in intelligence between genders. In our sample, only 44% of boys attended general high-school, while 74% of girls who participated in our study chose this type of school. Intelligence is strongly related to nationwide middle school exams results, which are the basis for secondary school-type selection. While mediocre intelligence leads many boys to prefer vocational schools, girls with mediocre intelligence still tend to choose general high-schools. As a result, mean intelligence among general high-school students is lower for girls than for boys. A similar relationship between intelligence, sex and school-type is observed in less prestigious school-types, which were selected more frequently by less intelligent girls. We were aware of the contradictory results concerning sex differences in intelligence, most of which showed lack of such differences (e.g. Feingold, 1994). That is why, in the theoretically driven model, we did not predict intelligence by sex. Our data did not support such conjecture (when we account for school-type) and we improved our model by inclusion of the sex–intelligence path. As we predicted, based on previous studies (Hecht et al., 2000; MEN, 2009; Mullis et al., 2007), children who came from families with higher SES had higher intelligence scores and time one reading literacy, although the effect of HISEI was small when school-type was controlled for. Children from different secondary schools differed in SES, with more economically and socially privileged children attending general high-schools than any other type of school (MEN, 2009). Parents with higher education and social position recognise the importance of education for further social and economic success much better than caregivers with lower education and place their children in schools with a higher

9

education level (MEN, 2009). To make it possible to meet general high-school requirements, parents not only motivate their children to succeed in education and engage much of their financial and time resources in children's education, but also provide cultural capital (Bourdieu, 1993). Adolescent exposure to art and culture activities at home provides cultural knowledge, skills, education and advantages that can help them succeed in the educational system. For example in the PIRLS study (Mullis et al., 2007), the average reading achievement of students with at least one university-educated parent was more than 1 SD greater than the average of those whose parents did not complete lower secondary education. Our study showed one more readable result over that which had already been established in other studies. Although children with higher SES get better scores in reading, they do not improve their notes one year later more than teenagers with lower SES when we control for other study variables. It means that they base their better achievement on the previously acquired knowledge and they do not get more from school and do not learn faster than their peers even though they are slightly more intelligent and better prepared for the educational system at the start of secondary school.

5. Conclusions and limitations Taken together, the present study further underpins the assumption that all the studied variables predict reading performance to some extent. From our analysis, it is clear that anxiety does not play a very important, but still significant role over very strong predictors like intelligence, prior literacy, sex, school-type and SES. Furthermore, this study extends past research by highlighting the importance of sex differences on state- and trait-anxiety and on reading performance as well. The study shows that under particular conditions, state-anxiety can be higher among boys and trait-anxiety can improve performance. The results also supported the hypothesis that girls not only have better scores in reading, but also improve those scores more than boys in later education. Intelligence cannot account for this difference because generally it is not higher for girls and at the same time predicts reading literacy. Nevertheless, the strongest effects on reading scores and intelligence were linked to school-type. Our study and other sources (LDB, 2012; MEN, 2009) suggest that there is a negative selection for vocational school. Not only do children with lower abilities and less educationally prepared attend those facilities, but also these schools do not develop reading skills in their students as well as high-schools do. It is not a very surprising effect when we acknowledge the differences in curriculums (less demanding in vocational school) and educational aims between schools. High schools prepare their pupils for college and vocational school for work in some profession, such as a confectioner or gardener. However, the differences exceeding 1 SD are very large and some improvements in reading education level of vocational schools could be useful concerning the importance of reading literacy in contemporary society. The main limitation of the study concerns the quite specific design of the study — not fully longitudinal, since only the reading literacy measurement was repeated and state-anxiety was measured after and not before the reading test. Whether anxiety influenced reading literacy cannot be concluded from the employed study design. It may even be that adolescents who struggled more with the test later had more anxious feelings and it was not the anxiety that impaired their performance or the relation was reciprocal. The same could be true for the causal relationship we implied between school-type and intelligence. Without an intelligence measurement before the first reading test and also before the school-type selection we cannot be fully sure of the direction of the effect. To investigate such a causal relationship, one should necessarily measure intelligence, state-anxiety and trait-anxiety preferably shortly before the first test and then a second time before the consecutive test. Further studies could employ such a revised design.

10


It would also be useful to include more complex measures of intelligence in such studies because girls and boys differ on particular components of general cognitive ability (Hyde, 2005) and it might be that verbal intelligence would explain the gender gap in reading literacy even better than non-verbal intelligence. Another change could concern anxiety measures. The assessment of specific anxiety, like test-anxiety, or even better, reading-anxiety would bring more pronounced effects on reading achievement. Besides those limitations in design which were independent of the authors' choice, the study contributed to the research field by showing a relationship to reading performance and also indicating predictors which account for the improvement of such performance. Our study also demonstrates that investigating direct as well as indirect paths from predictors to reading literacy is an important task for future research. Appendix A. 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