Pub Date : 2017-10-02DOI: 10.1080/11356405.2017.1377991
E. Tovar-García
Abstract This study analyses the possible influence of cultural activities such as visits to theatres, museums, exhibitions, zoos, circuses, excursions, trips and campaigns on educational progress. It uses longitudinal data taken from the Russia Longitudinal Monitoring Survey, which is a unique nationally representative survey; the sample consists of schoolchildren aged six to14. The methodology makes use of the panel data structure and logit regressions to effectively control for time-invariant determinants of educational achievements, and the method directly controls for socioeconomic status, students’ health, public school, region, age and gender. Consequently, this study accurately measured the isolated effect of cultural activities. The findings suggest that these cultural activities correlated positively with educational progress, as the cultural capital theory predicts. However, participation in these activities without parents did not show significant effects. Therefore, parental participation seems necessary to observe a positive association between cultural activities and educational progress, in accordance with the social capital theory.
{"title":"Cultural activities and educational progress: evidence from Russian longitudinal data / Actividades culturales y progreso educativo: evidencia basada en datos longitudinales procedentes de Rusia","authors":"E. Tovar-García","doi":"10.1080/11356405.2017.1377991","DOIUrl":"https://doi.org/10.1080/11356405.2017.1377991","url":null,"abstract":"Abstract This study analyses the possible influence of cultural activities such as visits to theatres, museums, exhibitions, zoos, circuses, excursions, trips and campaigns on educational progress. It uses longitudinal data taken from the Russia Longitudinal Monitoring Survey, which is a unique nationally representative survey; the sample consists of schoolchildren aged six to14. The methodology makes use of the panel data structure and logit regressions to effectively control for time-invariant determinants of educational achievements, and the method directly controls for socioeconomic status, students’ health, public school, region, age and gender. Consequently, this study accurately measured the isolated effect of cultural activities. The findings suggest that these cultural activities correlated positively with educational progress, as the cultural capital theory predicts. However, participation in these activities without parents did not show significant effects. Therefore, parental participation seems necessary to observe a positive association between cultural activities and educational progress, in accordance with the social capital theory.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127176149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-03DOI: 10.1080/11356405.2017.1367171
Bo Hu, L. Qin, Meghan Sullivan, Jonathan Templin
Abstract Evaluating test scores is an essential process, critical in both educational research and practice. To scientifically understand and utilize test scores, educators and researchers need to choose appropriate psychometric models to analyse and interpret assessment data. In this paper, we discuss two classes of psychometric models that have been widely used in educational measurement: item response theory (IRT) models and diagnostic classification models (DCMs). Specifically, the IRT discussion focuses on producing scores on a numerical continuum using the two-parameter logistic model. We then discuss methods for producing scores based on ordinal classifications with DCMs and compare and contrast such scores with those from IRT models. In addition, through step-by-step examples, we demonstrate how to obtain estimates from and interpret results from each model we present. We conclude the paper with considerations in and suggestions for choosing an appropriate psychometric model.
{"title":"Contemporary approaches to psychometrics: item response theory and diagnostic classification models / Enfoques contemporáneos sobre psicometría: los modelos de la teoría de respuesta al ítem y los modelos de clasificación de diagnósticos","authors":"Bo Hu, L. Qin, Meghan Sullivan, Jonathan Templin","doi":"10.1080/11356405.2017.1367171","DOIUrl":"https://doi.org/10.1080/11356405.2017.1367171","url":null,"abstract":"Abstract Evaluating test scores is an essential process, critical in both educational research and practice. To scientifically understand and utilize test scores, educators and researchers need to choose appropriate psychometric models to analyse and interpret assessment data. In this paper, we discuss two classes of psychometric models that have been widely used in educational measurement: item response theory (IRT) models and diagnostic classification models (DCMs). Specifically, the IRT discussion focuses on producing scores on a numerical continuum using the two-parameter logistic model. We then discuss methods for producing scores based on ordinal classifications with DCMs and compare and contrast such scores with those from IRT models. In addition, through step-by-step examples, we demonstrate how to obtain estimates from and interpret results from each model we present. We conclude the paper with considerations in and suggestions for choosing an appropriate psychometric model.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128108914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-03DOI: 10.1080/11356405.2017.1367168
R. Walters, Lesa Hoffman
Abstract Educational researchers and school administrators frequently evaluate academic outcomes collected from cross-sectional sampling designs with overt nested structures, such as when students are nested within schools. More recently, interest has focused on the longitudinal collection of academic outcomes to evaluate a student’s growth across time. In a longitudinal context, the repeatedly measured academic outcomes are nested within a student. Proper analysis of longitudinal data requires the hierarchical linear model to quantify the extra correlations within students created by the nested sampling structure. In this article, we introduce the hierarchical linear model used to quantify and predict between-student differences in a repeatedly measured continuous maths achievement outcome. This introduction is presented as a conversation representative of those we have frequently with individuals who lack statistical training in hierarchical linear models for longitudinal data. Specifically, we cover why repeated-measures ANOVA may not always be appropriate, how the hierarchical linear model can be used to quantify between-student differences in change and how student- and occasion-level predictors can be properly modelled and interpreted.
{"title":"Applying the hierarchical linear model to longitudinal data / La aplicación del modelo lineal jerárquico a datos longitudinales","authors":"R. Walters, Lesa Hoffman","doi":"10.1080/11356405.2017.1367168","DOIUrl":"https://doi.org/10.1080/11356405.2017.1367168","url":null,"abstract":"Abstract Educational researchers and school administrators frequently evaluate academic outcomes collected from cross-sectional sampling designs with overt nested structures, such as when students are nested within schools. More recently, interest has focused on the longitudinal collection of academic outcomes to evaluate a student’s growth across time. In a longitudinal context, the repeatedly measured academic outcomes are nested within a student. Proper analysis of longitudinal data requires the hierarchical linear model to quantify the extra correlations within students created by the nested sampling structure. In this article, we introduce the hierarchical linear model used to quantify and predict between-student differences in a repeatedly measured continuous maths achievement outcome. This introduction is presented as a conversation representative of those we have frequently with individuals who lack statistical training in hierarchical linear models for longitudinal data. Specifically, we cover why repeated-measures ANOVA may not always be appropriate, how the hierarchical linear model can be used to quantify between-student differences in change and how student- and occasion-level predictors can be properly modelled and interpreted.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130088884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-03DOI: 10.1080/11356405.2017.1368162
J. Hilbe
Abstract This monograph provides an overview of the various regression models used to analyse count response models. We begin by defining counts and the methods used to model count data. We then discuss the basic count model — Poisson regression — focusing on the nature of equi-dispersion, which occurs when the mean and variance are identical in value. Equi-dispersion is a distributional assumption of the Poisson model. We examine how to determine when this assumption is violated, which results in extra-dispersion; i.e., either under- or overdispersion. Extra-dispersion biases the Poisson model standard errors, leading us to accept or reject a model when we should not. The negative binomial model is generally used to model generic overdispersion, but if we know the cause of the overdispersion we can select an alternative count model that appropriately adjusts for it. The same is the case with under-dispersion. Aside from looking at the Poisson and negative binomial models, we also evaluate models such as generalized Poisson, Poisson inverse Gaussian, two-part hurdle models, zero-inflated mixture models and other varieties of count model. Finally, we provide a brief look at Bayesian count models, showing how to estimate a Bayesian negative binomial model.
{"title":"The statistical analysis of count data / El análisis estadístico de los datos de recuento","authors":"J. Hilbe","doi":"10.1080/11356405.2017.1368162","DOIUrl":"https://doi.org/10.1080/11356405.2017.1368162","url":null,"abstract":"Abstract This monograph provides an overview of the various regression models used to analyse count response models. We begin by defining counts and the methods used to model count data. We then discuss the basic count model — Poisson regression — focusing on the nature of equi-dispersion, which occurs when the mean and variance are identical in value. Equi-dispersion is a distributional assumption of the Poisson model. We examine how to determine when this assumption is violated, which results in extra-dispersion; i.e., either under- or overdispersion. Extra-dispersion biases the Poisson model standard errors, leading us to accept or reject a model when we should not. The negative binomial model is generally used to model generic overdispersion, but if we know the cause of the overdispersion we can select an alternative count model that appropriately adjusts for it. The same is the case with under-dispersion. Aside from looking at the Poisson and negative binomial models, we also evaluate models such as generalized Poisson, Poisson inverse Gaussian, two-part hurdle models, zero-inflated mixture models and other varieties of count model. Finally, we provide a brief look at Bayesian count models, showing how to estimate a Bayesian negative binomial model.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125017569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-03DOI: 10.1080/11356405.2017.1368163
Amir Hefetz, Gabriel Liberman, Naymé-Daniela Salas
Abstract The availability of computerized statistical packages allows us to plug in our data and to expect a set of estimates, which we can communicate in our final research report. However, statistical software is not an end; it is only the means. Our responsibility as researchers is to develop a set of arguments that explain why our final methodological choice is the better one, which will yield reliable answers for the study questions within the theoretical setting. Journals of all types require authors to deploy innovative statistical models when analysing collected data. Yet, the problem of advanced modelling strategies still remains — authors disregard key assumptions, choose the wrong analytical strategies and are not aware of alternative strategies to support or reject their hypotheses. This special issue provides readers with a reference framework for some of the most common methodological concerns. The articles included in this monographic issue deal with relatable scenarios and offer state-of-the-art statistical approaches to data treatment. We are confident that this special issue will be extremely useful to past and future authors of Cultura y Educación, and we hope it will increase the quality of the papers published by the journal.
{"title":"Advanced statistical modelling ideas, a challenge for research in culture and education / Ideas sobre modelos estadísticos avanzados: un desafío para la investigación en cultura y educación","authors":"Amir Hefetz, Gabriel Liberman, Naymé-Daniela Salas","doi":"10.1080/11356405.2017.1368163","DOIUrl":"https://doi.org/10.1080/11356405.2017.1368163","url":null,"abstract":"Abstract The availability of computerized statistical packages allows us to plug in our data and to expect a set of estimates, which we can communicate in our final research report. However, statistical software is not an end; it is only the means. Our responsibility as researchers is to develop a set of arguments that explain why our final methodological choice is the better one, which will yield reliable answers for the study questions within the theoretical setting. Journals of all types require authors to deploy innovative statistical models when analysing collected data. Yet, the problem of advanced modelling strategies still remains — authors disregard key assumptions, choose the wrong analytical strategies and are not aware of alternative strategies to support or reject their hypotheses. This special issue provides readers with a reference framework for some of the most common methodological concerns. The articles included in this monographic issue deal with relatable scenarios and offer state-of-the-art statistical approaches to data treatment. We are confident that this special issue will be extremely useful to past and future authors of Cultura y Educación, and we hope it will increase the quality of the papers published by the journal.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126104192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-03DOI: 10.1080/11356405.2017.1367907
Jichuan Wang, Amir Hefetz, Gabriel Liberman
Abstract Structural equation modelling (SEM) is a comprehensive and flexible statistical technique for testing complex relationships between variables, including both observed variables and latent variables (constructs or factors), with multiple pathways. In the past two decades, SEM has quickly pervaded in various research fields, such as psychology, sociology, education, economics, etc. This study provides a brief non-mathematical introduction to SEM for educational researchers who are interested in SEM but do not have advanced statistical backgrounds. This study presents the basic concepts of SEM, describes the steps of SEM implementation and discusses some issues that are often encountered in SEM applications. Examples of measurement model (confirmatory factor analysis, CFA) and general structural equation model are demonstrated, using real educational research data. Computer program Mplus was applied for modelling; the programming syntax and selected output for the example models are included.
{"title":"Applying structural equation modelling in educational research / La aplicación del modelo de ecuación estructural en las investigaciones educativas","authors":"Jichuan Wang, Amir Hefetz, Gabriel Liberman","doi":"10.1080/11356405.2017.1367907","DOIUrl":"https://doi.org/10.1080/11356405.2017.1367907","url":null,"abstract":"Abstract Structural equation modelling (SEM) is a comprehensive and flexible statistical technique for testing complex relationships between variables, including both observed variables and latent variables (constructs or factors), with multiple pathways. In the past two decades, SEM has quickly pervaded in various research fields, such as psychology, sociology, education, economics, etc. This study provides a brief non-mathematical introduction to SEM for educational researchers who are interested in SEM but do not have advanced statistical backgrounds. This study presents the basic concepts of SEM, describes the steps of SEM implementation and discusses some issues that are often encountered in SEM applications. Examples of measurement model (confirmatory factor analysis, CFA) and general structural equation model are demonstrated, using real educational research data. Computer program Mplus was applied for modelling; the programming syntax and selected output for the example models are included.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126801273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-03DOI: 10.1080/11356405.2017.1365425
Amir Hefetz, Gabriel Liberman
Abstract Surveys and tests contain multiple test items, sets of repeated tests or multiple survey questions. Commonly, these units are arranged within instruments subject to varying contexts of tests or questions. The analyst’s goal is to discover communalities across these items such that items can be reduced down to common meaningful factors. We provide a literature review that supports our further choice between exploratory analytical models for analysing empirical data and for building a guide to interpreting results. The purpose of this research is to provide a methodological and systematic framework for researchers who consider exploratory analyses. Following a comparison between factor extraction methods, we suggest various approaches to look at the association between the original variables and the factor, as well as correlations between factors. Our empirical case study data is a survey instrument of 19 items from a questionnaire developed by the Branco Weiss Institute in Israel, for evaluating at-risk high school and intermediate school students. Properties of the data such as the sample size, the quality of data by means of distribution patterns and extreme values, and correlations between the original items are considered. We argue that a concurrent integration of two fundamental processes — the empirical model fit and the substantive meaning — are essential in the process of implementing exploratory analysis results. The main conclusion is that the process of exploring latent factors needs an allocation of analytical resources, similar to other statistical modelling practices. Data and context mutually function as the platform for arriving at the optimal number of factors and their item composition. The exploratory factor analysis is a powerful tool for researchers who are ready to operate this tool properly.
{"title":"The factor analysis procedure for exploration: a short guide with examples / El análisis factorial exploratorio: una guía breve con ejemplos","authors":"Amir Hefetz, Gabriel Liberman","doi":"10.1080/11356405.2017.1365425","DOIUrl":"https://doi.org/10.1080/11356405.2017.1365425","url":null,"abstract":"Abstract Surveys and tests contain multiple test items, sets of repeated tests or multiple survey questions. Commonly, these units are arranged within instruments subject to varying contexts of tests or questions. The analyst’s goal is to discover communalities across these items such that items can be reduced down to common meaningful factors. We provide a literature review that supports our further choice between exploratory analytical models for analysing empirical data and for building a guide to interpreting results. The purpose of this research is to provide a methodological and systematic framework for researchers who consider exploratory analyses. Following a comparison between factor extraction methods, we suggest various approaches to look at the association between the original variables and the factor, as well as correlations between factors. Our empirical case study data is a survey instrument of 19 items from a questionnaire developed by the Branco Weiss Institute in Israel, for evaluating at-risk high school and intermediate school students. Properties of the data such as the sample size, the quality of data by means of distribution patterns and extreme values, and correlations between the original items are considered. We argue that a concurrent integration of two fundamental processes — the empirical model fit and the substantive meaning — are essential in the process of implementing exploratory analysis results. The main conclusion is that the process of exploring latent factors needs an allocation of analytical resources, similar to other statistical modelling practices. Data and context mutually function as the platform for arriving at the optimal number of factors and their item composition. The exploratory factor analysis is a powerful tool for researchers who are ready to operate this tool properly.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127959212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-03DOI: 10.1080/11356405.2017.1365426
H. Bar
Abstract One of the most common problems facing empirical researchers is when a portion of the data is missing. We will review three different types of ‘missingness’, namely missing completely at random, missing at random and missing not at random, and we will discuss how missing data can affect data analysis. We review methods to deal with missing data, including the simple ‘complete-case analysis’ approach, in which we only use the observations in the data set for which all the data is available, and the more sophisticated ‘multiple imputation’ approach, in which we repeat the analysis using multiple (completed) copies of the data set, and obtain the estimates of interest by averaging across all analyses. We will demonstrate how to implement solutions to missing data and review the limitations of the methods.
{"title":"Missing data — mechanisms and possible solutions / Datos ausentes: mecanismos y posibles soluciones","authors":"H. Bar","doi":"10.1080/11356405.2017.1365426","DOIUrl":"https://doi.org/10.1080/11356405.2017.1365426","url":null,"abstract":"Abstract One of the most common problems facing empirical researchers is when a portion of the data is missing. We will review three different types of ‘missingness’, namely missing completely at random, missing at random and missing not at random, and we will discuss how missing data can affect data analysis. We review methods to deal with missing data, including the simple ‘complete-case analysis’ approach, in which we only use the observations in the data set for which all the data is available, and the more sophisticated ‘multiple imputation’ approach, in which we repeat the analysis using multiple (completed) copies of the data set, and obtain the estimates of interest by averaging across all analyses. We will demonstrate how to implement solutions to missing data and review the limitations of the methods.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130858221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-04-03DOI: 10.1080/11356405.2017.1305074
M. Lorenzo-Moledo, A. Godás-Otero, Miguel A. Santos-Rego
Abstract This work is focused on deepening our understanding of family actions when participating in school life and of the intensity of their involvement with the school work that their children perform, or should perform, at home. Specifically, this study examines whether there is a relationship between family participation and involvement, what are the situations that characterize the differential performance of fathers and mothers; an analysis is also carried out of each situation to identify which elements determine their participation or involvement behaviour. To this end, the sample consisted of 279 subjects from Latin America with children enrolled in the fifth or sixth grades of primary education. The results show that involvement and participation are independent phenomena, that mothers are more involved and participate more in their children’s school life, and that the elements determining these two actions are different for both mothers and fathers.
{"title":"Main determinants of immigrant families’ involvement and participation in school life / Principales determinantes de la implicación y participación de las familias inmigrantes en la escuela","authors":"M. Lorenzo-Moledo, A. Godás-Otero, Miguel A. Santos-Rego","doi":"10.1080/11356405.2017.1305074","DOIUrl":"https://doi.org/10.1080/11356405.2017.1305074","url":null,"abstract":"Abstract This work is focused on deepening our understanding of family actions when participating in school life and of the intensity of their involvement with the school work that their children perform, or should perform, at home. Specifically, this study examines whether there is a relationship between family participation and involvement, what are the situations that characterize the differential performance of fathers and mothers; an analysis is also carried out of each situation to identify which elements determine their participation or involvement behaviour. To this end, the sample consisted of 279 subjects from Latin America with children enrolled in the fifth or sixth grades of primary education. The results show that involvement and participation are independent phenomena, that mothers are more involved and participate more in their children’s school life, and that the elements determining these two actions are different for both mothers and fathers.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-04-03DOI: 10.1080/11356405.2017.1305073
Ángeles Axpe, V. Acosta, A.Mª. Moreno, G. Ramírez
Abstract The aim of this research has been to test the efficacy of an intervention programme on lexical-semantic problems in children with Specific Language Impairment (SLI). The sample consisted of 34 children diagnosed with SLI and 34 children with typical language development, all enrolled in different schools on the island of Tenerife (Canary Islands, Spain). For the selection of the sample, CELF-3, Peabody, ITPA Auditory Association and Visual Association subtests and the K-BIT IQ tests were used. The intervention programme consisted of 72 sessions of 30 minutes each, using noun and action naming activities along with conversation and categorization tasks in order to increase vocabulary and improve lexical semantic competence. Significant results were achieved in aspects related to the acquisition of vocabulary as well as in lexical semantics or related terms. The educational implications are clear insofar as lexical-semantic aspects are key in terms of activating other linguistic components and in improving the academic progress of SLI students.
{"title":"Application of a lexical-semantic intervention programme for students with Specific Language Impairment / Aplicación de un programa de intervención léxico-semántica en alumnado con Trastorno Específico del Lenguaje","authors":"Ángeles Axpe, V. Acosta, A.Mª. Moreno, G. Ramírez","doi":"10.1080/11356405.2017.1305073","DOIUrl":"https://doi.org/10.1080/11356405.2017.1305073","url":null,"abstract":"Abstract The aim of this research has been to test the efficacy of an intervention programme on lexical-semantic problems in children with Specific Language Impairment (SLI). The sample consisted of 34 children diagnosed with SLI and 34 children with typical language development, all enrolled in different schools on the island of Tenerife (Canary Islands, Spain). For the selection of the sample, CELF-3, Peabody, ITPA Auditory Association and Visual Association subtests and the K-BIT IQ tests were used. The intervention programme consisted of 72 sessions of 30 minutes each, using noun and action naming activities along with conversation and categorization tasks in order to increase vocabulary and improve lexical semantic competence. Significant results were achieved in aspects related to the acquisition of vocabulary as well as in lexical semantics or related terms. The educational implications are clear insofar as lexical-semantic aspects are key in terms of activating other linguistic components and in improving the academic progress of SLI students.","PeriodicalId":153832,"journal":{"name":"Cultura y Educación","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128475433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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