Repeated measures ANOVA and adjusted F-tests when sphericity is violated: which procedure is best?

Introduction: One-way repeated measures ANOVA requires sphericity. Research indicates that violation of this assumption has an important impact on Type I error. Although more advanced alternative procedures exist, most classical texts recommend the use of adjusted F-tests, which are frequently employed because they are intuitive, easy to apply, and available in most statistical software. Adjusted F-tests differ in the procedure used to estimate the corrective factor ε, the most common being the Greenhouse-Geisser (F-GG) and Huynh-Feldt (F-HF) adjustments. Although numerous studies have analyzed the robustness of these procedures, the results are inconsistent, thus highlighting the need for further research. Methods: The aim of this simulation study was to analyze the performance of the F-statistic, F-GG, and F-HF in terms of Type I error and power in one-way designs with normal data under a variety of conditions that may be encountered in real research practice. Values of ε were fixed according to the Greenhouse-Geisser procedure (ε^). We manipulated the number of repeated measures (3, 4, and 6) and sample size (from 10 to 300), with ε^ values ranging from the lower to its upper limit. Results: Overall, the results showed that the F-statistic becomes more liberal as sphericity violation increases, whereas both F-HF and F-GG control Type I error; of the two, F-GG is more conservative, especially with large values of ε^ and small samples. Discussion: If different statistical conclusions follow from application of the two tests, we recommend using F-GG for ε^ values below 0.60, and F-HF for ε^ values equal to or above 0.60.

Non-normal Data in Repeated Measures ANOVA: Impact on Type I Error and Power.

BACKGROUND: Repeated measures designs are commonly used in health and social sciences research. Although there are other, more advanced, statistical analyses, the F-statistic of repeated measures analysis of variance (RM-ANOVA) remains the most widely used procedure for analyzing differences in means. The impact of the violation of normality has been extensively studied for between-subjects ANOVA, but this is not the case for RM-ANOVA. Therefore, studies that extensively and systematically analyze the robustness of RM-ANOVA under the violation of normality are needed. This paper reports the results of two simulation studies aimed at analyzing the Type I error and power of RM-ANOVA when the normality assumption is violated but sphericity is fulfilled. METHOD: Study 1 considered 20 distributions, both known and unknown, and we manipulated the number of repeated measures (3, 4, 6, and 8) and sample size (from 10 to 300). Study 2 involved unequal distributions in each repeated measure. The distributions analyzed represent slight, moderate, and severe deviation from normality. RESULTS: Overall, the results show that the Type I error and power of the F-statistic are not altered by the violation of normality. CONCLUSIONS: RM-ANOVA is generally robust to non-normality when the sphericity assumption is met.

Non-normal Data in Repeated Measures ANOVA: Impact on Type I Error and Power / Datos no Normales en el ANOVA de Medidas Repetidas: Impacto en el Error Tipo I y Potencia

Background: Repeated measures designs are commonly used in health and social sciences research. Although there are other, more advanced, statistical analyses, the F-statistic of repeated measures analysis of variance (RM-ANOVA) remains the most widely used procedure for analyzing differences in means. The impact of the violation of normality has been extensively studied for between-subjects ANOVA, but this is not the case for RM-ANOVA. Therefore, studies that extensively and systematically analyze the robustness of RM-ANOVA under the violation of normality are needed. This paper reports the results of two simulation studies aimed at analyzing the Type I error and power of RM-ANOVA when the normality assumption is violated but sphericity is fulfilled. Method: Study 1 considered 20 distributions, both known and unknown, and we manipulated the number of repeated measures (3, 4, 6, and 8) and sample size (from 10 to 300). Study 2 involved unequal distributions in each repeated measure. The distributions analyzed represent slight, moderate, and severe deviation from normality. Results: Overall, the results show that the Type I error and power of the F-statistic are not altered by the violation of normality. Conclusions: RM-ANOVA is generally robust to non-normality when the sphericity assumption is met.(AU)

Antecedentes: El diseño de medidas repetidas es uno de los más usados en ciencias sociales y de la salud. Aunque hay otras alternativas más avanzadas, el análisis de varianza de medidas repetidas (ANOVA-MR) sigue siendo el procedimiento más empleado para analizar las diferencias de medias. El impacto de la violación de la normalidad ha sido muy estudiado en el ANOVA intersujeto, pero los estudios son muy escasos en el ANOVA-MR. Por ello, el objetivo de este trabajo es realizar dos estudios de simulación Monte Carlo para analizar el error de Tipo I y la potencia cuando se incumple este supuesto bajo el cumplimiento de la esfericidad. Método: El estudio 1 incluye 20 distribuciones, tanto conocidas como desconocidas, manipulando el número de medidas repetidas (3, 4, 6 y 8) y el tamaño muestral (de 10 a 300). El estudio 2 incluye diferentes distribuciones en cada medida repetida. Las distribuciones analizadas representan desviación leve, moderada y severa de la normalidad. Resultados: En general, los resultados muestran que tanto el error Tipo I como la potencia del estadístico F no se alteran con la violación de la normalidad. Conclusiones: El ANOVA-MR es generalmente robusto a la no normalidad cuando la esfericidad se satisface.(AU)

Effect of variance ratio on ANOVA robustness: Might 1.5 be the limit?

Inconsistencies in the research findings on F-test robustness to variance heterogeneity could be related to the lack of a standard criterion to assess robustness or to the different measures used to quantify heterogeneity. In the present paper we use Monte Carlo simulation to systematically examine the Type I error rate of F-test under heterogeneity. One-way, balanced, and unbalanced designs with monotonic patterns of variance were considered. Variance ratio (VR) was used as a measure of heterogeneity (1.5, 1.6, 1.7, 1.8, 2, 3, 5, and 9), the coefficient of sample size variation as a measure of inequality between group sizes (0.16, 0.33, and 0.50), and the correlation between variance and group size as an indicator of the pairing between them (1, .50, 0, -.50, and -1). Overall, the results suggest that in terms of Type I error a VR above 1.5 may be established as a rule of thumb for considering a potential threat to F-test robustness under heterogeneity with unequal sample sizes.

Non-Normal data: Isa nova still a valid option? / Datos no normales: ¿es el ANOVA una opción válida?

Background: The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. Method: We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. Results: The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions (AU)

Antecedentes: las consecuencias de la violación de la normalidad sobre la robustez del estadístico F han sido estudiadas desde 1930 y siguen siendo de interés en la actualidad. Sin embargo, aunque la investigación ha sido extensa, los resultados son contradictorios, encontrándose evidencia a favor y en contra de su robustez. El presente estudio presenta un análisis sistemático de la robustez del estadístico F en términos de error de Tipo I ante violaciones de la normalidad, considerando una amplia variedad de distribuciones frecuentemente encontradas en ciencias sociales y de la salud. Método: se ha realizado un estudio de simulación Monte Carlo considerando un diseño de tres grupos y diferentes distribuciones conocidas y no conocidas. Las variables manipuladas han sido: igualdad o desigualdad del tamaño de los grupos, tamaño muestral total y de los grupos; coeficiente de variación del tamaño muestral; forma de la distribución e igualdad o desigualdad de la forma en los grupos; y emparejamiento entre el tamaño muestral con el grado de contaminación en la distribución. Resultados: los resultados muestran que el estadístico F es robusto en términos de error de Tipo I en el 100% de los casos estudiados, independientemente de las condiciones manipuladas (AU)

Non-normal data: Is ANOVA still a valid option?

BACKGROUND: The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. METHOD: We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coefficient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. RESULTS: The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.

Non-normal Distributions Commonly Used in Health, Education, and Social Sciences: A Systematic Review.

Statistical analysis is crucial for research and the choice of analytical technique should take into account the specific distribution of data. Although the data obtained from health, educational, and social sciences research are often not normally distributed, there are very few studies detailing which distributions are most likely to represent data in these disciplines. The aim of this systematic review was to determine the frequency of appearance of the most common non-normal distributions in the health, educational, and social sciences. The search was carried out in the Web of Science database, from which we retrieved the abstracts of papers published between 2010 and 2015. The selection was made on the basis of the title and the abstract, and was performed independently by two reviewers. The inter-rater reliability for article selection was high (Cohen's kappa = 0.84), and agreement regarding the type of distribution reached 96.5%. A total of 262 abstracts were included in the final review. The distribution of the response variable was reported in 231 of these abstracts, while in the remaining 31 it was merely stated that the distribution was non-normal. In terms of their frequency of appearance, the most-common non-normal distributions can be ranked in descending order as follows: gamma, negative binomial, multinomial, binomial, lognormal, and exponential. In addition to identifying the distributions most commonly used in empirical studies these results will help researchers to decide which distributions should be included in simulation studies examining statistical procedures.

Sphericity estimation bias for repeated measures designs in simulation studies.

In this study, we explored the accuracy of sphericity estimation and analyzed how the sphericity of covariance matrices may be affected when the latter are derived from simulated data. We analyzed the consequences that normal and nonnormal data generated from an unstructured population covariance matrix-with low (ε = .57) and high (ε = .75) sphericity-can have on the sphericity of the matrix that is fitted to these data. To this end, data were generated for four types of distributions (normal, slightly skewed, moderately skewed, and severely skewed or log-normal), four sample sizes (very small, small, medium, and large), and four values of the within-subjects factor (K = 4, 6, 8, and 10). Normal data were generated using the Cholesky decomposition of the correlation matrix, whereas the Vale-Maurelli method was used to generate nonnormal data. The results indicate the extent to which sphericity is altered by recalculating the covariance matrix on the basis of simulated data. We concluded that bias is greater with spherical covariance matrices, nonnormal distributions, and small sample sizes, and that it increases in line with the value of K. An interaction was also observed between sample size and K: With very small samples, the observed bias was greater as the value of K increased.

Should we rely on the Kenward-Roger approximation when using linear mixed models if the groups have different distributions?

The study explores the robustness to violations of normality and sphericity of linear mixed models when they are used with the Kenward-Roger procedure (KR) in split-plot designs in which the groups have different distributions and sample sizes are small. The focus is on examining the effect of skewness and kurtosis. To this end, a Monte Carlo simulation study was carried out, involving a split-plot design with three levels of the between-subjects grouping factor and four levels of the within-subjects factor. The results show that: (1) the violation of the sphericity assumption did not affect KR robustness when the assumption of normality was not fulfilled; (2) the robustness of the KR procedure decreased as skewness in the distributions increased, there being no strong effect of kurtosis; and (3) the type of pairing between kurtosis and group size was shown to be a relevant variable to consider when using this procedure, especially when pairing is positive (i.e., when the largest group is associated with the largest value of the kurtosis coefficient and the smallest group with its smallest value). The KR procedure can be a good option for analysing repeated-measures data when the groups have different distributions, provided the total sample sizes are 45 or larger and the data are not highly or extremely skewed.

The effect of skewness and kurtosis on the robustness of linear mixed models.

This study analyzes the robustness of the linear mixed model (LMM) with the Kenward-Roger (KR) procedure to violations of normality and sphericity when used in split-plot designs with small sample sizes. Specifically, it explores the independent effect of skewness and kurtosis on KR robustness for the values of skewness and kurtosis coefficients that are most frequently found in psychological and educational research data. To this end, a Monte Carlo simulation study was designed, considering a split-plot design with three levels of the between-subjects grouping factor and four levels of the within-subjects factor. Robustness is assessed in terms of the probability of type I error. The results showed that (1) the robustness of the KR procedure does not differ as a function of the violation or satisfaction of the sphericity assumption when small samples are used; (2) the LMM with KR can be a good option for analyzing total sample sizes of 45 or larger when their distributions are normal, slightly or moderately skewed, and with different degrees of kurtosis violation; (3) the effect of skewness on the robustness of the LMM with KR is greater than the corresponding effect of kurtosis for common values; and (4) when data are not normal and the total sample size is 30, the procedure is not robust. Alternative analyses should be performed when the total sample size is 30.

Using the linear mixed model to analyze nonnormal data distributions in longitudinal designs.

Using a Monte Carlo simulation and the Kenward-Roger (KR) correction for degrees of freedom, in this article we analyzed the application of the linear mixed model (LMM) to a mixed repeated measures design. The LMM was first used to select the covariance structure with three types of data distribution: normal, exponential, and log-normal. This showed that, with homogeneous between-groups covariance and when the distribution was normal, the covariance structure with the best fit was the unstructured population matrix. However, with heterogeneous between-groups covariance and when the pairing between covariance matrices and group sizes was null, the best fit was shown by the between-subjects heterogeneous unstructured population matrix, which was the case for all of the distributions analyzed. By contrast, with positive or negative pairings, the within-subjects and between-subjects heterogeneous first-order autoregressive structure produced the best fit. In the second stage of the study, the robustness of the LMM was tested. This showed that the KR method provided adequate control of Type I error rates for the time effect with normally distributed data. However, as skewness increased-as occurs, for example, in the log-normal distribution-the robustness of KR was null, especially when the assumption of sphericity was violated. As regards the influence of kurtosis, the analysis showed that the degree of robustness increased in line with the amount of kurtosis.

Drug awareness in adolescents attending a mental health service: analysis of longitudinal data.

One of the procedures used most recently with longitudinal data is linear mixed models. In the context of health research the increasing number of studies that now use these models bears witness to the growing interest in this type of analysis. This paper describes the application of linear mixed models to a longitudinal study of a sample of Spanish adolescents attending a mental health service, the aim being to investigate their knowledge about the consumption of alcohol and other drugs. More specifically, the main objective was to compare the efficacy of a motivational interviewing programme with a standard approach to drug awareness. The models used to analyse the overall indicator of drug awareness were as follows: (a) unconditional linear growth curve model; (b) growth model with subject-associated variables; and (c) individual curve model with predictive variables. The results showed that awareness increased over time and that the variable 'schooling years' explained part of the between-subjects variation. The effect of motivational interviewing was also significant.

Drug awareness in adolescents attending a mental health service: analysis of longitudinal data

One of the procedures used most recently with longitudinal data is linear mixed models. In the context of health research the increasing number of studies that now use these models bears witness to the growing interest in this type of analysis. This paper describes the application of linear mixed models to a longitudinal study of a sample of Spanish adolescents attending a mental health service, the aim being to investigate their knowledge about the consumption of alcohol and other drugs. More specifically, the main objective was to compare the efficacy of a motivational interviewing programme with a standard approach to drug awareness. The models used to analyse the overall indicator of drug awareness were as follows: (a) unconditional linear growth curve model; (b) growth model with subject-associated variables; and (c) individual curve model with predictive variables. The results showed that awareness increased over time and that the variable ‘schooling years’ explained part of the between-subjects variation. The effect of motivational interviewing was also significant (AU)

Uno de los procedimientos más recientemente utilizados con datos de carácter longitudinal son los modelos lineales mixtos. Su creciente interés en investigación sanitaria se constata por un aumento de los estudios que utilizan este tipo de análisis. Este trabajo se centra en los modelos lineales mixtos aplicados a un estudio longitudinal sobre el conocimiento acerca del consumo de alcohol y otras drogas en una muestra de adolescentes españoles que inician tratamiento en un centro de salud mental. Concretamente, el objetivo principal fue comparar la eficacia de un programa de entrevista motivacional con otro estándar sobre el conocimiento de las drogas. Los modelos utilizados a fin de analizar el indicador global de conocimiento sobre drogas fueron los siguientes: (a) modelo incondicional lineal de curva de crecimiento, (b) modelo de crecimiento con variables asociadas a las personas y (c) modelo de curvas individuales con variables predictoras. Los resultados mostraron que el conocimiento incrementa con el paso del tiempo y que la escolarización explica parte de la variación entre-sujetos. En cuanto al efecto de la entrevista motivacional resultó ser significativo (AU)

Regression-based techniques for statistical decision making in single-case designs.

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions about intervention effectiveness in single-case designs. Ordinary least square estimation is compared to two correction techniques dealing with general trend and a procedure that eliminates autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approach the nominal ones in the presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

General linear mixed model for analysing longitudinal data in developmental research.

Many areas of psychological, social, and health research are characterised by hierarchically structured data. Growth curves are usually represented by means of a two-level hierarchical structure in which observations are the first-level units nested within subjects, the second-level units. With data such as these, the best option for analysis is the general linear mixed model, which can be used even with longitudinal data series in which intervals are not constant or for which over the passage of time there is loss of data. In this paper an overview is given of the general linear mixed model approach to the analysis of longitudinal data in developmental research. The advantages of this model in comparison with the traditional approaches for analysing longitudinal data are shown, emphasising the usefulness of modelling the covariance structure properly to achieve a precise estimation of the parameters of the model.

Regression-based techniques for statistical decision making in singlecase designs / Técnicas fundamentadas en la regresión para la decisión estadística en diseños de caso único

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions about intervention effectiveness in single-case designs. Ordinary least square estimation is compared to two correction techniques dealing with general trend and a procedure that eliminates autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approach the nominal ones in the presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series (AU)

El estudio evalúa el rendimiento de cuatro métodos de estimación de los coeficientes de regresión utilizados para la toma de decisiones estadísticas sobre la efectividad de las intervenciones en diseños de caso único. La estimación por mínimos cuadrados ordinarios se compara con dos métodos que controlan la tendencia en los datos y un procedimiento que elimina la autocorrelación cuando ésta es significativa. Los resultados indican que las tasas empíricas y nominales de falsas alarmas no coinciden en presencia de dependencia serial o tendencia al aplicar mínimos cuadrados ordinarios o generalizados. Los métodos que controlan la tendencia muestran tasas más bajas de error Tipo I, pero no son suficientemente sensibles a efectos existentes (cambio de nivel o de pendiente), por lo que el uso de la significación estadística de los coeficientes de regresión para detectar efectos no se recomienda cuando se dispone de series cortas de datos (AU)

Técnicas de análisis aplicadas a datos longitudinales en psicología y ciencias de la salud: Periodo 1985-2005 / No disponible

Este trabajo examina el uso de los principales modelos de análisis aplicados a datos longitudinales en el ámbito de la psicología y medicina. Para ello, realizamos una revisión bibliográfica de los artículos publicados durante el período 1985-2005 en PsycInfo y Medline. Se observa que la cantidad de estudios longitudinales aumenta siguiendo el mismo patrón que en la revisión realizada por Singer y Willett (2006). Los resultados muestran que, en los últimos años, se da un mayor uso de los modelos multinivel con el consecuente decremento de los modelos clásicos. En cuanto a las técnicas aplicadas a datos no métricos, la regresión logística presenta un fuerte aumento. Otra clase de modelos como, por ejemplo, los modelos de ecuaciones estructurales, el análisis de series temporales y el análisis de supervivencia son menos utilizados. Sin embargo, en psicología se constata un ligero incremento de los modelos de ecuaciones estructurales y en medicina se produce un aumento de los análisis de supervivencia a finales del período analizado

This paper examies the use of the main analytical models applied to longitudinal data in the contexts of psychology and medicine.We carried out a bibliography review of articles published during the period 1985-2005 in PsycInfo and Medline.The quantity oflongitudinal studies increased, following the pattern reported in Singer and Willett’s review (2006). The results show that linear mixedmodels expanded a great deal in the last years, and that the use of the classical models declined. In relation to techniques applied tononmetric data, the use of logistic regression increased notably. Other varieties of models, such as structural equation models, timeseries models, and survival models, were used less. However, towards the end of the period studied, the use of structural equationand survival models for analyzing longitudinal data was becoming increasingly popular in psychological and medical research (AU)

Children of alcoholics in Spain: from risk to pathology. Results from the ALFIL program.

OBJECTIVE: To identify the possible risk factors and negative outcomes associated with parental alcoholism. A secondary aim was to determine the influence of the family density of alcoholism on children of alcoholics' (COAs) psychological functioning. METHOD: A multisite epidemiological study was conducted in 8 Spanish cities, recruiting a total sample of 371 COAs (whose parents were in contact with alcohol treatment centers and accepted to participate in this study) and 147 controls (from schools in the same localities as COAs). Both groups were 6-17 years old and received a comprehensive evaluation of mental disorders (no symptoms, subclinical symptoms or clinical diagnosis for each disorder; according to DSM-IV criteria); alcohol and other substance use (none, occasional, regular and risky consumption); school achievement (low, middle and high) and other academic performance indicators (WISC-R Information and Arithmetic subtests, school support activities and failed subjects and courses). Lastly, several cognitive functions were measured by the WISC-R Similarities, Block Design and Digit Symbol subtests, the Toulouse-Piéron test and the Stroop test. Logistic regression methods were used to compare both groups and a linear regression model was used to determine the influence of the family density of alcoholism. The following confounding variables were controlled for: age, gender, socio-economic status and family cohesion. RESULTS: Children of alcoholics' were twice as likely as controls to present subclinical symptoms and four times more likely than controls to have a definite diagnosis of any mental disorder. More specifically, COAs had a significantly higher risk than controls of attention deficit disorder/hyperactivity, depression, phobias, enuresis and tics. COAs also tended to have more symptoms of generalized anxiety disorder. COAs had worse results on all the cognitive tests used and their risk of low school achievement was nine times higher than that of controls. Family density of alcoholism was significantly related to several psychiatric disorders and to low academic and cognitive performance in these children. CONCLUSION: Children of alcoholics' whose parents are in contact with treatment centers in Spain constitute a target group for selective prevention, as they have a higher risk of different negative outcomes, which mainly include attention disorders and other cognitive deficits, depression and anxiety.

Tecnologías de la información y comunicación en la enseñanza de diseños experimentales y aplicados / Information and communication technologies in teaching applied and experimental designs

En este trabajo se presenta un modelo de innovación docente aplicado a contenidos metodológicos de la enseñanza de psicología. El modelo didáctico propuesto integra las Tecnologías de la Información y Comunicación (TIC), tales como CD-ROMs, páginas web e Internet. Estos recursos son un complemento de las clases lectivas. Las clases sirven para informar, guiar y orientar a los estudiantes a fin de que sean capaces de conseguir la información y reorganizarla de forma coherente. El objetivo de este artículo es hallar las preferencias de los estudiantes en el proceso de aprendizaje y valorar la incorporación de las TIC, mediante el cuestionario EMID (Evaluación del Modelo de Innovación Docente). Los resultados muestran que los estudiantes son conscientes de la necesidad de consultar otros materiales y que las TIC ayudan a comprender la materia desde diversas perspectivas. De esta forma, los estudiantes adquieren más autonomía en la consecución de los resultados del aprendizaje

In this work, an innovative teaching model applied to methodological contents in psychology is presented. The proposed didactic model includes Information and Communication Technologies (ICT), such as CD-ROMs, web sites and Internet. These resources complement class attendance. In the classes the students are informed, guided and oriented so that they are able to obtain information and reorganize it in a coherent way. The aim of this article is to find out the students’ learning preferences and estimate the incorporation of ICT, by means of the ETIM (Evaluation of Teaching Innovation Model) questionnaire. The results show that the students are aware of the need to consult other materials and that ICT helps students to understand the subject from various perspectives. In this way, the students become more autonomous in acquiring learning results

[Information and communication technologies in teaching applied and experimental designs]. / Tecnologías de la información y comunicación en la enseñanza de diseños experimentales y aplicados.

In this work, an innovative teaching model applied to methodological contents in psychology is presented. The proposed didactic model includes Information and Communication Technologies (ICT), such as CD-ROMs, web sites and Internet. These resources complement class attendance. In the classes the students are informed, guided and oriented so that they are able to obtain information and reorganize it in a coherent way. The aim of this article is to find out the students' learning preferences and estimate the incorporation of ICT, by means of the ETIM (Evaluation of Teaching Innovation Model) questionnaire. The results show that the students are aware of the need to consult other materials and that ICT helps students to understand the subject from various perspectives. In this way, the students become more autonomous in acquiring learning results.