This document provides 4 examples of how to report results from MANOVA analyses. The summaries are:
1) The first example examines gender differences and finds significant multivariate effects for gender of baby and participant. Univariate analyses find differences for consumer trends variables.
2) The second example examines teaching practice variables and finds interactions between teaching sector and linguistic status. Further analyses split on these variables.
3) The third example compares male and female teachers' current and preferred ICT use, finding gender differences for current but not preferred use.
4) The fourth example reports multiple significant multivariate effects for variables like country, university, and demographics. It provides detail on follow up analyses for interactions.
This document provides an overview of multivariate analysis of variance (MANOVA), including its assumptions, decision process, statistical tests used (e.g. Box's M test, Hotelling's T2, Roy's greatest characteristic root), and advantages over multiple univariate ANOVAs. It also discusses post-hoc tests, provides an example of how to interpret MANOVA output, and notes some limitations and disadvantages of the technique.
ANOVA is a statistical technique used to determine whether the means of groups are statistically different from each other. It can be used to establish cause-and-effect relationships with a certain degree of certainty. There are different types of ANOVA for different study designs. The basic parts of an ANOVA include sums of squares, degrees of freedom, mean squares, and the F-statistic. ANOVA can be performed in Excel using the data analysis tool. An example shows how ANOVA was used to analyze measurement data from multiple inspectors.
MANOVA is an extension of ANOVA that allows for multiple dependent variables. It tests whether multiple means of two or more groups differ. With MANOVA, we can compute relationships between independent variables, dependent variables, and between dependent and independent variables. Some advantages of MANOVA over separate ANOVAs include a better chance of discovering important factors and protecting against Type I errors. MANOVA assumes random sampling, normal distribution, linearity, homogeneity of variances, and multivariate normality. It splits the total scatter matrix into a between groups scatter matrix and within groups scatter matrix to calculate an F value similar to ANOVA.
This document provides an overview of multivariate analysis of variance (MANOVA). It explains that MANOVA assesses the effect of one or more independent variables on two or more dependent variables simultaneously, accounting for correlations between dependent variables. Some key points covered include assumptions of MANOVA like multivariate normality and homogeneity of covariance matrices. Examples are given to illustrate when MANOVA may be more advantageous than conducting multiple ANOVA tests.
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Repeated measures ANOVA is used to compare mean scores on the same individuals across multiple time points or conditions. It extends the dependent t-test to allow for more than two time points or conditions. Key assumptions include having a continuous dependent variable, at least two related groups or conditions, no outliers, normally distributed differences between groups, and sphericity. Repeated measures ANOVA separates variance into between-subjects, between-measures, and error components to test if there are differences in mean scores between related groups while accounting for correlations between measures on the same individuals.
This document outlines a presentation on Multivariate Analysis of Variance (MANOVA) given by Prof. Dr. Izani Ibrahim at the National University of Malaysia. The presentation introduces MANOVA and compares it to ANOVA, explaining that MANOVA can test for differences across multiple dependent variables simultaneously based on categorical independent variables. It discusses the geometry of MANOVA and how centroids represent groups in multivariate space. It also covers assumptions, test statistics like Pillai Trace and Hotelling Trace, and comparisons between MANOVA and Discriminant Analysis.
This document provides an overview of multivariate analysis of variance (MANOVA), including its assumptions, decision process, statistical tests used (e.g. Box's M test, Hotelling's T2, Roy's greatest characteristic root), and advantages over multiple univariate ANOVAs. It also discusses post-hoc tests, provides an example of how to interpret MANOVA output, and notes some limitations and disadvantages of the technique.
ANOVA is a statistical technique used to determine whether the means of groups are statistically different from each other. It can be used to establish cause-and-effect relationships with a certain degree of certainty. There are different types of ANOVA for different study designs. The basic parts of an ANOVA include sums of squares, degrees of freedom, mean squares, and the F-statistic. ANOVA can be performed in Excel using the data analysis tool. An example shows how ANOVA was used to analyze measurement data from multiple inspectors.
MANOVA is an extension of ANOVA that allows for multiple dependent variables. It tests whether multiple means of two or more groups differ. With MANOVA, we can compute relationships between independent variables, dependent variables, and between dependent and independent variables. Some advantages of MANOVA over separate ANOVAs include a better chance of discovering important factors and protecting against Type I errors. MANOVA assumes random sampling, normal distribution, linearity, homogeneity of variances, and multivariate normality. It splits the total scatter matrix into a between groups scatter matrix and within groups scatter matrix to calculate an F value similar to ANOVA.
This document provides an overview of multivariate analysis of variance (MANOVA). It explains that MANOVA assesses the effect of one or more independent variables on two or more dependent variables simultaneously, accounting for correlations between dependent variables. Some key points covered include assumptions of MANOVA like multivariate normality and homogeneity of covariance matrices. Examples are given to illustrate when MANOVA may be more advantageous than conducting multiple ANOVA tests.
My attractive effective presentation is the proof of my hard work as i made it for those who can not take interest in their studies so as they can see this they will take interest too as well as for those who really want to do come thing different from others , they can use my presentation if any kind of help you want just mail me at ammara.aftab63@gmail.com
Repeated measures ANOVA is used to compare mean scores on the same individuals across multiple time points or conditions. It extends the dependent t-test to allow for more than two time points or conditions. Key assumptions include having a continuous dependent variable, at least two related groups or conditions, no outliers, normally distributed differences between groups, and sphericity. Repeated measures ANOVA separates variance into between-subjects, between-measures, and error components to test if there are differences in mean scores between related groups while accounting for correlations between measures on the same individuals.
This document outlines a presentation on Multivariate Analysis of Variance (MANOVA) given by Prof. Dr. Izani Ibrahim at the National University of Malaysia. The presentation introduces MANOVA and compares it to ANOVA, explaining that MANOVA can test for differences across multiple dependent variables simultaneously based on categorical independent variables. It discusses the geometry of MANOVA and how centroids represent groups in multivariate space. It also covers assumptions, test statistics like Pillai Trace and Hotelling Trace, and comparisons between MANOVA and Discriminant Analysis.
The document provides guidance on reporting the results of an ANCOVA analysis in APA format. It recommends including that a one-way ANCOVA was conducted to determine differences between levels of an independent variable on a dependent variable while controlling for a covariate. An example is given using athlete type as the independent variable, slices of pizza eaten as the dependent variable, and weight as the covariate. The document also provides a template for reporting the F-ratio, degrees of freedom, and significance level.
Analysis of variance (ANOVA) everything you need to knowStat Analytica
Most of the students may struggle with the analysis of variance (ANOVA). Here in this presentation you can clear all your doubts in analysis of variance with suitable examples.
My attractive effective presentation is the proof of my hard work as i made it for those who can not take interest in their studies so as they can see this they will take interest too as well as for those who really want to do come thing different from others , they can use my presentation if any kind of help you want just mail me at ammara.aftab63@gmail.com
This document provides an overview of two-way ANOVA and MANOVA. It defines two-way ANOVA as an analysis method used for studies with two or more categorical explanatory variables and a quantitative outcome. Two-way ANOVA allows investigating the main effects of two factors and their interaction. The document also describes MANOVA, which assesses the effect of one or more independent variables on two or more dependent variables. It provides the assumptions, advantages, and an example of two-way MANOVA. In conclusion, two-way ANOVA and MANOVA are appropriate for analyzing studies with multiple explanatory variables and outcomes.
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
The document provides guidance on reporting the results of a one-way ANOVA in APA format. It recommends including that a one-way ANOVA was conducted to examine the effect of an independent variable on a dependent variable. It provides a template for reporting the F-statistic, degrees of freedom, and significance level based on the ANOVA output. Filling in the specifics of the independent variable, dependent variable, and ANOVA results completes the report.
This document discusses using one-way ANOVA in SPSS to compare mean salaries among employee age groups. It finds a significant difference in monthly salaries between the three age groups. Post hoc tests show that all three group means are significantly different from one another. Two other examples are presented: the first finds no significant difference in the importance of growth and development between age groups, while the second does find a significant difference in the importance of a safe work environment between the youngest and oldest age groups specifically.
This document discusses repeated measures ANOVA. It explains that repeated measures ANOVA is used when the same participants are measured under different treatment conditions. This allows researchers to remove variability caused by individual differences. The document outlines the components of the repeated measures ANOVA F-ratio, including the numerator which is the variance between treatments and the denominator which is the variance due to chance/error after removing individual differences. It also discusses how to conduct hypothesis testing and calculate effect size for repeated measures ANOVA.
This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines key terms like factors, interactions, F distribution, and multiple comparison tests. For one-way ANOVA, it explains how to test if three or more population means are equal. For two-way ANOVA, it notes you must first test for interactions between two factors before testing their individual effects. The Tukey test is introduced for identifying specifically which group means differ following rejection of a one-way ANOVA null hypothesis.
This document provides an overview of quantitative analysis techniques using SPSS, including data manipulation, transformation, and cleaning methods. It also covers univariate, bivariate, and other statistical analysis methods for exploring relationships between variables and differences between groups. Specific techniques discussed include computing new variables, recoding, selecting cases, imputing missing values, aggregating data, sorting, merging files, descriptive statistics, correlations, regressions, t-tests, ANOVA, non-parametric tests, and more.
This document discusses repeated measures designs and analyzing data from such designs using repeated measures ANOVA. It explains that repeated measures ANOVA involves comparing measures taken from the same subjects across different treatment conditions while controlling for individual differences. The document provides details on the null and alternative hypotheses, calculating variance components, and assumptions of repeated measures ANOVA.
Repeated measures ANOVA is used to compare average scores on the same individuals across multiple time periods or treatment conditions. It controls for individual differences by having each subject serve as their own control. The repeated measures ANOVA tests whether population means are equal across conditions while accounting for within-subject variability. It has advantages of increased power but disadvantages like carryover effects. Assumptions include continuous, normally distributed dependent variables and independence of observations.
This document presents information on multivariate analysis of variance (MANOVA). It discusses when MANOVA is appropriate to use and its advantages over univariate ANOVA. Specifically, it notes that MANOVA considers multiple dependent variables simultaneously and is more powerful than conducting separate univariate tests. The document provides an example of a two-factor mixed MANOVA design investigating the effects of sex and chocolate type on ratings of chocolate taste, crunchiness, and flavor.
Normal or skewed distributions (descriptive both2) - Copyright updatedKen Plummer
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
This document provides information on two-way repeated measures designs, including when to use them, their structure, and how to analyze the data. A two-way repeated measures design is used to investigate the effects of two within-subjects factors on a dependent variable simultaneously. All subjects are tested at each level of both factors. This design allows comparison of mean differences between groups split on the two within-subject factors. The document describes the analysis process, including testing for main effects, interactions, and simple effects using SPSS. An example is provided to illustrate a two-way repeated measures design investigating the effects of music and environment on work performance.
MANOVA is an extension of ANOVA that allows for analysis of multiple dependent variables. It tests whether changes in independent variables have statistically significant effects on two or more dependent variables. If the MANOVA results indicate significance, then individual ANOVA tests must be examined to determine which dependent variable(s) are driving the effect. A key advantage is the ability to analyze multiple outcomes simultaneously, while controlling for increased complexity and loss of degrees of freedom with additional variables.
This document provides an overview of various multivariate analysis of variance (MANOVA) techniques, including one-way MANOVA, two-way MANOVA, and multivariate analysis of covariance (MANCOVA). It defines each technique, provides examples, and outlines the assumptions that must be met to use each one. Key assumptions for MANOVA include having continuous dependent variables, categorical independent groups, independence of observations, adequate sample sizes, no outliers, multivariate normality, linear relationships between dependent variables, no multicollinearity, and homogeneity of variance-covariance matrices. The document also describes how to test assumptions and interpret MANOVA results in SPSS.
This document discusses the sign test, a nonparametric statistical method used to test for differences between paired observations. It defines the sign test and describes different types, including two-sided and one-sided tests. Examples are provided to illustrate how to calculate and interpret the sign test for a two samples, median of a single sample, and preference between two products. The conclusion discusses when nonparametric vs parametric tests are most appropriate based on sample size and assumption violations.
Reporting the wilcoxon signed ranks testKen Plummer
A Wilcoxon Signed-Ranks Test was conducted to compare pre-test and post-test ranks. The results indicated that post-test ranks were statistically significantly higher than pre-test ranks, with a Z score of 21 and p value less than .027. The document provided guidance and examples for reporting the results of the Wilcoxon Signed-Ranks Test in APA style.
A researcher assigns 33 subjects to 3 groups receiving dietary information via different modes: an online website, a nurse practitioner, or a video. The researcher measures 3 dependent variables related to the presentation: difficulty, usefulness, and importance. MANOVA is an appropriate analysis to determine if the modes of presentation have a significant effect on a combination of the dependent variables, while accounting for correlations between them. The researcher can use MANOVA to test whether the interactive website is superior to the other modes in conveying the information in a comprehensive yet cost-effective manner.
ANOVA, ANCOVA, MANOVA, and MANCOVA are statistical analyses used to test differences between groups.
ANOVA tests for differences between 2 or more means and partitions variances into sums of squares between and within groups. ANCOVA controls for additional factors, called covariates, to reduce error and increase power.
MANOVA assesses the effect of independent variables on multiple dependent variables simultaneously, accounting for correlation between variables. It tests for overall differences using a multivariate F value. Univariate follow-ups can then examine differences on each individual dependent variable.
MANCOVA extends MANOVA to include controlling for covariates, allowing evaluation of changes in dependent variables while accounting for additional continuous factors measured at different
The document provides guidance on reporting the results of an ANCOVA analysis in APA format. It recommends including that a one-way ANCOVA was conducted to determine differences between levels of an independent variable on a dependent variable while controlling for a covariate. An example is given using athlete type as the independent variable, slices of pizza eaten as the dependent variable, and weight as the covariate. The document also provides a template for reporting the F-ratio, degrees of freedom, and significance level.
Analysis of variance (ANOVA) everything you need to knowStat Analytica
Most of the students may struggle with the analysis of variance (ANOVA). Here in this presentation you can clear all your doubts in analysis of variance with suitable examples.
My attractive effective presentation is the proof of my hard work as i made it for those who can not take interest in their studies so as they can see this they will take interest too as well as for those who really want to do come thing different from others , they can use my presentation if any kind of help you want just mail me at ammara.aftab63@gmail.com
This document provides an overview of two-way ANOVA and MANOVA. It defines two-way ANOVA as an analysis method used for studies with two or more categorical explanatory variables and a quantitative outcome. Two-way ANOVA allows investigating the main effects of two factors and their interaction. The document also describes MANOVA, which assesses the effect of one or more independent variables on two or more dependent variables. It provides the assumptions, advantages, and an example of two-way MANOVA. In conclusion, two-way ANOVA and MANOVA are appropriate for analyzing studies with multiple explanatory variables and outcomes.
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
The document provides guidance on reporting the results of a one-way ANOVA in APA format. It recommends including that a one-way ANOVA was conducted to examine the effect of an independent variable on a dependent variable. It provides a template for reporting the F-statistic, degrees of freedom, and significance level based on the ANOVA output. Filling in the specifics of the independent variable, dependent variable, and ANOVA results completes the report.
This document discusses using one-way ANOVA in SPSS to compare mean salaries among employee age groups. It finds a significant difference in monthly salaries between the three age groups. Post hoc tests show that all three group means are significantly different from one another. Two other examples are presented: the first finds no significant difference in the importance of growth and development between age groups, while the second does find a significant difference in the importance of a safe work environment between the youngest and oldest age groups specifically.
This document discusses repeated measures ANOVA. It explains that repeated measures ANOVA is used when the same participants are measured under different treatment conditions. This allows researchers to remove variability caused by individual differences. The document outlines the components of the repeated measures ANOVA F-ratio, including the numerator which is the variance between treatments and the denominator which is the variance due to chance/error after removing individual differences. It also discusses how to conduct hypothesis testing and calculate effect size for repeated measures ANOVA.
This document provides an overview of analysis of variance (ANOVA) techniques, including one-way and two-way ANOVA. It defines key terms like factors, interactions, F distribution, and multiple comparison tests. For one-way ANOVA, it explains how to test if three or more population means are equal. For two-way ANOVA, it notes you must first test for interactions between two factors before testing their individual effects. The Tukey test is introduced for identifying specifically which group means differ following rejection of a one-way ANOVA null hypothesis.
This document provides an overview of quantitative analysis techniques using SPSS, including data manipulation, transformation, and cleaning methods. It also covers univariate, bivariate, and other statistical analysis methods for exploring relationships between variables and differences between groups. Specific techniques discussed include computing new variables, recoding, selecting cases, imputing missing values, aggregating data, sorting, merging files, descriptive statistics, correlations, regressions, t-tests, ANOVA, non-parametric tests, and more.
This document discusses repeated measures designs and analyzing data from such designs using repeated measures ANOVA. It explains that repeated measures ANOVA involves comparing measures taken from the same subjects across different treatment conditions while controlling for individual differences. The document provides details on the null and alternative hypotheses, calculating variance components, and assumptions of repeated measures ANOVA.
Repeated measures ANOVA is used to compare average scores on the same individuals across multiple time periods or treatment conditions. It controls for individual differences by having each subject serve as their own control. The repeated measures ANOVA tests whether population means are equal across conditions while accounting for within-subject variability. It has advantages of increased power but disadvantages like carryover effects. Assumptions include continuous, normally distributed dependent variables and independence of observations.
This document presents information on multivariate analysis of variance (MANOVA). It discusses when MANOVA is appropriate to use and its advantages over univariate ANOVA. Specifically, it notes that MANOVA considers multiple dependent variables simultaneously and is more powerful than conducting separate univariate tests. The document provides an example of a two-factor mixed MANOVA design investigating the effects of sex and chocolate type on ratings of chocolate taste, crunchiness, and flavor.
Normal or skewed distributions (descriptive both2) - Copyright updatedKen Plummer
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
This document provides information on two-way repeated measures designs, including when to use them, their structure, and how to analyze the data. A two-way repeated measures design is used to investigate the effects of two within-subjects factors on a dependent variable simultaneously. All subjects are tested at each level of both factors. This design allows comparison of mean differences between groups split on the two within-subject factors. The document describes the analysis process, including testing for main effects, interactions, and simple effects using SPSS. An example is provided to illustrate a two-way repeated measures design investigating the effects of music and environment on work performance.
MANOVA is an extension of ANOVA that allows for analysis of multiple dependent variables. It tests whether changes in independent variables have statistically significant effects on two or more dependent variables. If the MANOVA results indicate significance, then individual ANOVA tests must be examined to determine which dependent variable(s) are driving the effect. A key advantage is the ability to analyze multiple outcomes simultaneously, while controlling for increased complexity and loss of degrees of freedom with additional variables.
This document provides an overview of various multivariate analysis of variance (MANOVA) techniques, including one-way MANOVA, two-way MANOVA, and multivariate analysis of covariance (MANCOVA). It defines each technique, provides examples, and outlines the assumptions that must be met to use each one. Key assumptions for MANOVA include having continuous dependent variables, categorical independent groups, independence of observations, adequate sample sizes, no outliers, multivariate normality, linear relationships between dependent variables, no multicollinearity, and homogeneity of variance-covariance matrices. The document also describes how to test assumptions and interpret MANOVA results in SPSS.
This document discusses the sign test, a nonparametric statistical method used to test for differences between paired observations. It defines the sign test and describes different types, including two-sided and one-sided tests. Examples are provided to illustrate how to calculate and interpret the sign test for a two samples, median of a single sample, and preference between two products. The conclusion discusses when nonparametric vs parametric tests are most appropriate based on sample size and assumption violations.
Reporting the wilcoxon signed ranks testKen Plummer
A Wilcoxon Signed-Ranks Test was conducted to compare pre-test and post-test ranks. The results indicated that post-test ranks were statistically significantly higher than pre-test ranks, with a Z score of 21 and p value less than .027. The document provided guidance and examples for reporting the results of the Wilcoxon Signed-Ranks Test in APA style.
A researcher assigns 33 subjects to 3 groups receiving dietary information via different modes: an online website, a nurse practitioner, or a video. The researcher measures 3 dependent variables related to the presentation: difficulty, usefulness, and importance. MANOVA is an appropriate analysis to determine if the modes of presentation have a significant effect on a combination of the dependent variables, while accounting for correlations between them. The researcher can use MANOVA to test whether the interactive website is superior to the other modes in conveying the information in a comprehensive yet cost-effective manner.
ANOVA, ANCOVA, MANOVA, and MANCOVA are statistical analyses used to test differences between groups.
ANOVA tests for differences between 2 or more means and partitions variances into sums of squares between and within groups. ANCOVA controls for additional factors, called covariates, to reduce error and increase power.
MANOVA assesses the effect of independent variables on multiple dependent variables simultaneously, accounting for correlation between variables. It tests for overall differences using a multivariate F value. Univariate follow-ups can then examine differences on each individual dependent variable.
MANCOVA extends MANOVA to include controlling for covariates, allowing evaluation of changes in dependent variables while accounting for additional continuous factors measured at different
Analysis of Variance (ANOVA), MANOVA: Expected variance components, Random an...Satish Khadia
This document provides an introduction to analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). It discusses key concepts like variance components, fixed and random models, and the assumptions of MANOVA. The goals of ANOVA are described as estimating variance components, evaluating genetic contributions, and testing hypotheses. MANOVA tests for differences in multiple dependent variables simultaneously, which can protect against Type I errors compared to multiple ANOVAs. Both methods require assumptions like normality and homogeneity of variances.
Reporting a multiple linear regression in apaKen Plummer
A multiple linear regression was calculated to predict weight based on height and sex. A significant regression equation was found (F(2,13)=981.202, p<.000), with an R2 of .993. Participants' predicted weight is equal to 47.138 + 2.101(height) - 39.133(sex), where height is measured in inches and sex is coded as 0 for male and 1 for female. Both height and sex were significant predictors of weight.
Reporting a single linear regression in apaKen Plummer
The document provides a template for reporting the results of a simple linear regression analysis in APA format. It explains that a linear regression was conducted to predict weight based on height. The regression equation was found to be significant, F(1,14)=25.925, p<.000, with an R2 of .649. The predicted weight is equal to -234.681 + 5.434 (height in inches) pounds.
Este documento describe los tipos de análisis multivariado y el análisis multivariado de la varianza (MANOVA). El MANOVA permite estudiar la influencia de una o más variables independientes sobre dos o más variables dependientes correlacionadas. Calcula estadísticos como Lambda de Wilks que comparan la variabilidad entre grupos con la variabilidad dentro de los grupos para determinar si hay diferencias estadísticamente significativas. Explica los conceptos clave como las matrices de sumas de cuadrados y productos cruzados, autovectores, autovalores y cómo
The One-Way MANCOVA analyzes the influence of one independent variable on multiple dependent variables while controlling for one or more covariate factors. It first conducts a regression to remove the effect of covariates, then performs a MANOVA on the residuals. This allows it to increase the power of the MANOVA by explaining more variability in the model and control for confounding factors. A One-Way MANCOVA requires at least one independent variable, two or more dependent variables, and one or more covariates. It can be performed using SPSS's General Linear Model procedure.
This document summarizes and critiques a research paper that examined the effects of handicap source, frequency, and performance on observer impressions. It used a 2x2x2 factorial ANOVA/MANOVA design. The summary found partial support for the paper's 3 hypotheses but noted low variance explanation and some contrary findings. The critique raised concerns about not testing assumptions, excluding variables from MANOVA, and small effect sizes. It concluded the paper advanced the field but improvements could have been made by addressing the critique's methodological points.
1) A trial was conducted to compare the effects of three drugs (losartan, alasartan, and blasartan) on systolic blood pressure in hypertensive patients.
2) The mean reductions in systolic BP were 4.95, 7.74, and 7.44 mmHg for the losartan, alasartan, and blasartan groups respectively.
3) One-way ANOVA indicated a statistically significant difference between the groups, so pairwise t-tests were used to determine which groups differed. The alasartan and blasartan groups had significantly greater reductions than the losartan group.
MANOVA stands for Multivariate Analysis of Variance. It is used to compare means across multiple populations and variables simultaneously. The F test is used to compare means across k populations. It assumes the populations have equal variances and tests if the population means are equal against the alternative that at least one pair of means is different. The F statistic is calculated and compared to a critical value from the F distribution to determine if there are statistically significant differences between population means.
This document discusses various methods for appraising investments, including:
- Discounting future cash flows to calculate net present value using an appropriate discount rate.
- Considering the timing of cash flows, as cash received sooner is more valuable than cash received later.
- Using metrics like internal rate of return, payback period, and accounting rate of return, but recognizing their limitations compared to net present value.
- Selecting projects that yield returns above the minimum acceptable rate, with the rate being higher for riskier projects.
This document provides guidance on reporting the results of a single sample t-test in APA format. It includes templates for describing the test and population in the introduction and reporting the mean, standard deviation, t-value and significance in the results. An example is given of a hypothetical single sample t-test comparing IQ scores of people who eat broccoli regularly to the general population.
El documento describe los métodos de análisis multivariado, los cuales permiten analizar múltiples variables medidas para cada objeto de estudio. Explica que existen tres tipos de técnicas: métodos de dependencia que analizan las relaciones entre variables independientes y dependientes, métodos de interdependencia que identifican cómo están relacionadas todas las variables, y métodos estructurales que analizan las relaciones entre variables independientes y dependientes y entre ellas mismas. Finalmente, detalla algunas técnicas específicas como la regresión, anális
This document discusses hypothesis testing, including:
- A hypothesis test is a method for making decisions using data to test an unproven statement about a factor or phenomenon.
- The null hypothesis states there is no difference between what is observed and what is expected. The alternative hypothesis specifies an alternative statement.
- Steps in hypothesis testing include formulating the hypotheses, selecting a statistical test, collecting data, determining critical values and probabilities, and deciding whether to reject or fail to reject the null hypothesis.
- Parametric tests assume a known distribution while nonparametric tests make no assumptions. Common tests mentioned include t-tests, z-tests, F-tests, chi-square tests, and rank correlation tests.
This document defines multivariate analysis techniques as procedures for analyzing associations between two or more sets of measurements made on objects in samples. These techniques crystallize large volumes of data into more meaningful scores while accounting for all relevant information. Common techniques include multiple regression, discriminant analysis, multivariate analysis of variance, factor analysis, cluster analysis, and multidimensional scaling. Discriminant analysis classifies groups and examines differences between them using discriminate functions. Multiple regression involves predicting a dependent variable from two or more independent variables.
This document provides an overview of analysis of variance (ANOVA). It describes how ANOVA was developed by R.A. Fisher in 1920 to analyze differences between multiple sample means. The document outlines the F-statistic used in ANOVA to compare between-group and within-group variations. It also describes one-way and two-way classifications of ANOVA and provides examples of applications in fields like agriculture, biology, and pharmaceutical research.
This document discusses using analysis of variance (ANOVA) in marketing research. It provides an example of an experiment conducted on 20 respondents to test ice cream flavors and prices. The experiment uses a randomized design with one-way ANOVA. The results include descriptive statistics, cross tabulations of sales by flavor and price, and chi-square tests. Experimental designs discussed include completely randomized design, randomly blocked design, and factorial experiments. ANOVA is described as partitioning total variance into between-group and within-group variance.
This document discusses the null hypothesis for a one-way analysis of covariance (ANCOVA). It explains that a one-way ANCOVA compares the influence of an independent variable with at least two levels on a dependent variable, while controlling for the effect of a covariate. The document provides a template for writing the null hypothesis, which states that there is no significant effect of the independent variable on the dependent variable when controlling for the covariate. It gives two examples applying this template.
The document discusses a study that examines the relationship between community-based question answering (CQA) sites, students' existing knowledge (stock knowledge), and the academic achievement of grade 11 students in Camaya Campus. It provides background on CQA sites and stock knowledge. The study aims to determine if and how CQA site usage, stock knowledge, and academic achievement as measured by GPA are correlated. A questionnaire was administered to 50 students to collect data on CQA site and stock knowledge use as well as GPA. Statistical analysis found moderate correlations between CQA site use and stock knowledge when considering duration and frequency. Both CQA sites and stock knowledge were found to be moderately effective for academic achievement. Students had an average satisfactory G
This instructional plan aims to improve teacher-student rapport at Alief Hastings High School through a training on communication and rapport building techniques. The plan provides background on the problem of poor rapport contributing to dropout rates among Hispanic students. It then outlines the goals, objectives, assessment, and instructional strategy for a training session. The training will teach teachers and administrators to identify students' communication preferences and mirror them to build rapport. It will cover representational systems, eye accessing cues, mirroring, matching, and anchoring. Formative and summative assessments are included to evaluate skills. The plan provides appendices with materials to support the instruction and assessment.
This dissertation by Jennifer T. Butcher examined factors related to job satisfaction and retention of alternatively certified teachers. The study aimed to identify aspects of alternative certification programs that influence whether these teachers remain in the profession. Descriptive statistics were used to analyze demographic data and determine relationships between variables like program structure and teachers' intent to stay in the field. The results could help improve alternative certification and support retaining qualified teachers.
This document proposes developing a Potential for Quality Education Metric (PQEM) to measure the propensity for schools to provide quality education. The author reviews literature on defining and measuring education quality. Based on this, the proposed PQEM model uses normalized metrics for pupil-teacher ratio, free/reduced lunch ratio, and violent crime density, weighted together. Results show the PQEM moderately correlates to education quality measures. Removing outliers improves correlation, suggesting additional factors need consideration to strengthen the model's predictive ability.
Parent education and high school achievementSamira Rahmdel
This document summarizes a study that explored the relationship between parental education level and student achievement in English as a foreign language. The study administered tests of English language achievement and collected demographic data from 1352 high school students in Iran. Statistical analysis found that students whose parents had secondary education or higher scored significantly higher on the English test than students whose parents had primary education. However, there was no significant difference between students whose parents had secondary versus higher education. The results provide evidence that higher parental education levels are positively associated with higher student achievement in English as a foreign language.
Effects of jigsaw cooperative learning strategy on students’ achievement by g...Alexander Decker
This document discusses a study that examined the effects of using the Jigsaw cooperative learning strategy on gender differences in mathematics achievement among secondary school students in Kenya. Specifically, the study aimed to determine if gender affects achievement when Jigsaw is used to teach the mathematics topics of Surds and logarithms.
The study used a Solomon four-group, non-equivalent control group design and involved 160 students from four secondary schools. All students were administered a pre-test and post-test on the topics, with the experimental groups receiving instruction through Jigsaw and the control groups receiving conventional teaching methods. The results of the study showed that there was no statistically significant gender difference in mathematics achievement when students were taught using the Jigs
This chapter reviews literature related to factors that affect student performance in public and private schools. Several studies from various countries, including Pakistan, have used statistical techniques like OLS regression, decomposition analysis, and logit models to assess the effectiveness of different factors on student achievement. Commonly, private schools are found to have higher quality facilities and educated teachers compared to public schools. However, private schools lack trained and experienced teachers. Studies also show that factors like family background, income level, parental education, student-teacher ratio, and school facilities significantly impact student performance. Competition from private schools can positively impact productivity in public schools.
Parent Perceptions of Child Emergent Literacy: Influence of InterventionNemours
Parent involvement is critical for the development of early literacy skills. This poster represents a preliminary look at the Reading Readiness Skills Checklist and its accuracy in predicting parents’ perceptions of their children’s early literacy skill level. Generally speaking, parents’ ratings of their children were accurate when compared to children’s performance on early literacy assessments in the fall and spring. Parents of children who were at-risk for reading delays accurately predicted their children’s phonological awareness but were not quite as good at seeing improvements in other early literacy skills assessed. Results from this research validate the Reading Readiness Skills Checklist as an appropriate measure to use with parents. These results have implications for parent workshops that may help parents understand key skills they can work on at home with their children in fun and easy ways.
The assessment of deep word knowledge in young learnersCindy Shen
The document summarizes a study that assessed deep word knowledge in young first and second language learners. The study developed a Word Association Task (WAT) to measure productive lexical knowledge. 795 Dutch-learning third and fifth graders completed the WAT and a definition task. Results showed the WAT had acceptable reliability and validity, though it measured a slightly different construct than the definition task. While easy to administer, the WAT only partially overlapped with definition scores, suggesting it provides a different perspective on deep word knowledge.
Assignment Content1. Top of FormProfessional dispositions ha.docxbraycarissa250
Assignment Content
1.
Top of Form
Professional dispositions have been defined as the “values, commitments, and professional ethics that influence behavior toward candidates, families, colleagues and communities and affect candidate learning, motivation and development as well as the educator’s own professional growth” (NCATE, 2000).
Dispositions can also be described as attitudes and beliefs about counseling, as well as professional conduct and behavior. Not all dispositions can be directly assessed, but aspects of professional behavior are assessed during classes and field experiences in counseling settings.
Review the Master of Science in Counseling Professional Dispositions.
To prepare for professional dispositions assessments in this program, write a 700 word paper in which you:
· Reflect on your personal strengths in connection to the dispositions. Support your ideas with examples.
· Identify areas for personal growth in connection to the dispositions. Support your ideas with examples.
· Outline an action plan for developing the identified areas for personal growth.
· Describe why it is important to adhere to the dispositions. How do they support professionalism in counseling? How do they make a counselor effective?
Format your assignment according to course-level APA guidelines.
Bottom of Form
The title for this Special Section is Developmental Research and Translational
Science: Evidence-Based Interventions for At-Risk Youth and Families, edited by
Suniya S. Luthar and Nancy Eisenberg
Processes of Early Childhood Interventions to Adult Well-Being
Arthur J. Reynolds, Suh-Ruu Ou, Christina F. Mondi, and Momoko Hayakawa
University of Minnesota
This article describes the contributions of cognitive–scholastic advantage, family support behavior, and school
quality and support as processes through which early childhood interventions promote well-being. Evidence
in support of these processes is from longitudinal cohort studies of the Child–Parent Centers and other pre-
ventive interventions beginning by age 4. Relatively large effects of participation have been documented for
school readiness skills at age 5, parent involvement, K-12 achievement, remedial education, educational attain-
ment, and crime prevention. The three processes account for up to half of the program impacts on well-being.
They also help to explain the positive economic returns of many effective programs. The generalizability of
these processes is supported by a sizable knowledge base, including a scale up of the Child–Parent Centers.
Growing evidence that early childhood experiences
can improve adult well-being and reduce educa-
tional disparities has increased attention to preven-
tion (Braveman & Gottlieb, 2014; Power, Kuh, &
Morton, 2013). Early disparities between high- and
low-income groups are evident in school readiness
skills, which increase substantially over time in
rates of achievement proficiency, delinquency, and
educational attainment (Braveman ...
American Journal of Multidisciplinary Research and Development is indexed, refereed and peer-reviewed journal, which is designed to publish research articles.
American Journal of Multidisciplinary Research and Development is indexed, refereed and peer-reviewed journal, which is designed to publish research articles.
Geert Driessen 2019 Encyclopdia Teacher ethnicity, pupil ethnicity, and educa...Driessen Research
In many countries, ethnic minority teachers are strongly underrepresented. It is often assumed that if there were more minority teachers, minority pupils would achieve much better. This assumption has rarely been empirically tested. In search of proof, the present study reviews the literature. 24 relevant studies were found, all pertaining to the US. The findings show that there is little empirical evidence that a stronger degree of ethnic match, be it in the form of a one-to-one coupling of teachers to pupils with the same ethnic background, or a larger share of minority teachers at an ethnically mixed school, leads to predominantly positive results. Insofar positive effects were found, they apply to a greater extent to subjective teacher evaluations than to objective achievement outcome measures.
Driessen, G. (2020). Teacher-pupil ethnicity match and achievement. Encyclopedia, 10 November 2020. Retrieved from: https://encyclopedia.pub/178
This paper analyzes factors that affect academic proficiency rates in Minnesota public schools. It measures the relationship between four variables - free and reduced lunch rates (FRL), expenditures per student (EXPEND/ENROL), enrollment (ENROL), and reading proficiency (READ) - and math proficiency rates using data from 325 school districts from 2012-2016. The author develops an empirical model and expects FRL and EXPEND/ENROL to have negative relationships with math proficiency, while ENROL may also negatively impact proficiency due to higher student-teacher ratios. The goal is to determine the effect of each variable and which factors positively or negatively influence proficiency.
· Independent Design Project Literature Review and Research Log .docxodiliagilby
· Independent Design Project: Literature Review and Research Log: Entry 4
Literature Review and Research Log
Independent Design Project
Continue research for your independent design project paper by determining the application of advanced state-of-the-art robotics in relation to your design. Use these references to update or modify your design as necessary. Identify how your design reflects applicable categories of advanced state-of-the-art robotics.
Create a new entry to your research log (Module 4) and enter each reference you found relating to the application of robotic fundamentals (at least five). Place these references in alphabetical order, in the proper current APA format, with a brief description of the resource and its applicability.
Be sure to keep these files for use when you complete your week 9 final design project. You will need to add any applicable items from these logs to your final project.
The title for this Special Section is Developmental Research and Translational
Science: Evidence-Based Interventions for At-Risk Youth and Families, edited by
Suniya S. Luthar and Nancy Eisenberg
Processes of Early Childhood Interventions to Adult Well-Being
Arthur J. Reynolds, Suh-Ruu Ou, Christina F. Mondi, and Momoko Hayakawa
University of Minnesota
This article describes the contributions of cognitive–scholastic advantage, family support behavior, and school
quality and support as processes through which early childhood interventions promote well-being. Evidence
in support of these processes is from longitudinal cohort studies of the Child–Parent Centers and other pre-
ventive interventions beginning by age 4. Relatively large effects of participation have been documented for
school readiness skills at age 5, parent involvement, K-12 achievement, remedial education, educational attain-
ment, and crime prevention. The three processes account for up to half of the program impacts on well-being.
They also help to explain the positive economic returns of many effective programs. The generalizability of
these processes is supported by a sizable knowledge base, including a scale up of the Child–Parent Centers.
Growing evidence that early childhood experiences
can improve adult well-being and reduce educa-
tional disparities has increased attention to preven-
tion (Braveman & Gottlieb, 2014; Power, Kuh, &
Morton, 2013). Early disparities between high- and
low-income groups are evident in school readiness
skills, which increase substantially over time in
rates of achievement proficiency, delinquency, and
educational attainment (Braveman & Gottlieb, 2014;
O’Connell, Boat, & Warner, 2009). In this article, we
review evidence for three major processes by which
early childhood interventions (ECIs) promote well-
being and reduce problem behaviors. These are (a)
cognitive advantage, (b) family support behavior
(FS), and (c) school quality and support (SS).
The accumulated research widely supports these
processes as critical targets o ...
An Opportunity To Learn US History What NAEP Data Suggest Regarding The Oppo...Brandi Gonzales
This document summarizes a study that examined National Assessment of Educational Progress (NAEP) U.S. History assessment data to better understand factors associated with 12th grade students' historical knowledge. The study found that opportunity to learn (OTL), such as instructional exposure, is associated with learning outcomes. However, Black students experience an opportunity gap, receiving less instructional exposure in U.S. History. When controlling for socioeconomic factors, instructional exposure positively predicted achievement, though some differences remained. Both student characteristics and instructional factors significantly impacted achievement. The study indicates culturally congruent instruction is needed to ensure positive learning experiences for Black students.
This article discusses the role of parental involvement in closing the academic achievement gap between minority and disadvantaged students and their white and Asian counterparts. It argues that improving school quality alone through legislation like No Child Left Behind is not sufficient, and that parental accountability is missing from such efforts. The article highlights research showing parental involvement is highly predictive of student success. It recommends expanding NCLB to include mechanisms for holding parents accountable and engaging them in their children's education.
The article discusses the role of parents in closing the academic achievement gap between minority and disadvantaged students and their white and Asian counterparts. It argues that while legislation like No Child Left Behind aims to address this gap, it neglects to hold parents specifically accountable for their level of involvement in their children's education. The article highlights how proven strategies for parental involvement could be implemented under NCLB's framework, with a focus on parental responsibility. It offers suggestions for mechanisms to make parents accountable for their children's school success.
A Study on Relationship between Achievement Motivation and Academic Achieveme...ijtsrd
The aim of this longitudinal study was to Relationship between Achievement Motivation and Academic Achievement in English among High School Students. A sample of 300 students participated in the study. Results of structural equation modeling showed that mastery goals approach and avoidance were indirect predictors of both behavioral and cognitive engagement through seeking help from teachers. Performance goals avoidance, but not approach orientation were associated with cognitive engagement through help seeking behaviors. Overall, these results suggest that achievement goals are key drivers of changes in academic engagement in early high school and that their contribution is explained by seeking help from teachers. Practical implications, limitations, and future research directions are discussed. Mr. CP. Senthil Kumar | Dr. T. Sangeetha ""A Study on Relationship between Achievement Motivation and Academic Achievement in English among High School Students"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25141.pdf
Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/education/25141/a-study-on-relationship-between-achievement-motivation-and-academic-achievement-in-english-among-high-school-students/mr-cp-senthil-kumar
This article examines the associations between classroom quality, the percentage of children with special needs in a classroom, and child outcomes in Head Start programs. The study used four instruments to measure classroom quality, social behavior, pre-literacy skills, and family experiences. Hierarchical linear modeling found that most variance in child outcomes was within classrooms. Children from higher quality homes scored higher, while children with special needs scored lower. Children in higher quality classrooms and with a higher percentage of special needs peers had better academic outcomes.
1. Reporting MANOVA: Four examples (not necessarily definitive)
Contents page
Contents page.......................................................................................................................................... i
MANOVA reporting ................................................................................................................................. 1
Example 1: Gender in the future........................................................................................................1
Example 2: NESB teaching practice..................................................................................................1
Examining associations between scale scores and demographic variables................................1
Example 3: ICT ..................................................................................................................................3
Example 4: Internationalisation..........................................................................................................4
Method of Analysis .......................................................................................................................4
Multivariate Effects .......................................................................................................................4
Univariate Effects – Background Variables ..................................................................................6
i
2. Reporting MANOVA: Four examples (not necessarily definitive)
MANOVA reporting
Example 1: Gender in the future
An initial MANOVA examined age and educational literacy as covariates, the three latent
variables as dependent variables (DVs), and gender of baby in survey and gender of
participant as independent variables (IVs). After excluding age and educational level as
nonsignificant, a follow-up MANOVA examined associations between the DVs and IVs
described above. It showed a significant multivariate effect for the three latent variables as a
group in relation to the gender of the baby in the survey (girl versus boy: p<.001) and the
gender of the participant completing the survey (p<.01). However, the interaction between
gender of participant and gender of baby in survey was nonsignificant.
Univariate analyses for the effect of the baby in the survey significant predicted responses
related to consumer trends (p<.05), with responses significantly more positive for girl than
boy babies. Follow-up nonparametric tests for items related to consumer trends indicated that
male and female participants as a group were likely to indicate that baby girls in the future
were not only more likely to follow fashion trends (p<.01) but also to keep up with
technology (p<.01).
Univariate analyses for gender of participant significantly predicted responses related to
consumer trends such that males responded more positively than females and for emerging
lifestyle such that females responded more positively than males. Follow-up nonparametric
tests for items related to consumer trends indicated that despite the significant univariate
effect, the gender of participant did not influence responses to these items significantly.
Follow-up nonparametric tests for items related to emerging lifestyle indicated that females
were more likely to respond that babies of either gender would in future be able to live
anywhere (p<.05).
Example 2: NESB teaching practice
Examining associations between scale scores and demographic variables
A series of multivariate ANOVAs were conducted with six demographic variables as
independent variables, and with intercultural understanding and teaching confidence as
dependent variables. The six independent variables included: years of teaching experience,
teaching sector, training opportunities, linguistic status, cultural groups taught, and
percentage of NESB students in classrooms. Significant associations were examined further
by non-parametric testing (Kruskal-Wallis). Findings revealed nonsignificant associations
between the scale scores for intercultural understanding and four of the six independent
variables, including: Years of teaching experience, type of training; experience working with
different cultural groups; and numbers of NESB students being taught. However, the
interaction between teaching sector and teachers’ linguistic status was significant (Roy’s
largest root=0.039, F (6, 526)=3.413,p<0.05).
1
3. Reporting MANOVA: Four examples (not necessarily definitive)
monoling
3.5
95% CI Cultural proficiency
3.0
2.5
Yrs P-3 Yrs 4-5 Yrs 6-7 Yrs 8-12
TeachArea
Figure 5. Interaction between teaching area and monolingual status
As shown in Figures 5 and 6, univariate testing indicated this interaction to be significant
(F(3,263)=3.34,p<05) such that monolingual teachers of students in Years 4 and 5 appeared
to respond more positively than those in the early years of primary schooling or in secondary
school. In contrast, the differences appeared to be less pronounced for multilingual teachers.
multling
4
95% CI Cultural proficiency
3
2
1
Yrs P-3 Yrs 4-5 Yrs 6-7 Yrs 8-12
TeachArea
Figure 6. Interaction between teaching area and multilingual status
This interaction was examined further by splitting the dataset by linguistic status and
performing MANOVAs for monolingual and multilingual teachers separately. As expected,
the multivariate effect for Teaching status was significant for monolingual but not for
multilingual teachers. The effect of teaching area for monolingual teachers was particularly
pronounced in relation to being able to determine the proficiency of PI students as readers
and writers. Teachers of students in Years 6 and 7 were significantly less likely
( χ 2 = 10.229(3), p < 0.05) than others, especially those working in the first five years of
schooling, to respond positively to the proposal that they were able to determine the English
language proficiency of PI students as readers and writers.
2
4. Reporting MANOVA: Four examples (not necessarily definitive)
A separate MANOVA was used to examine the association between training opportunity
(Preservice, In-service, PostGrad) as IV, and Academic and Cultural confidence as DVs, the
interaction between training opportunities at post-graduate and pre-service levels was
significant (Roy’s Largest Root=0.03, F(2,277)=3.91,p<0.05). Univariate testing found the
effect to be significant for teacher confidence (F(1,278)=7.29,p<0.01). Follow-up testing
indicated that teachers with any type of training opportunity: pre-service, in-service, or
postgraduate training were more confident than those without that training on all three items
representing the factor associated with teachers’ confidence. In addition, those with
postgraduate training only were more confident than those with either pre-service or in-
service training only.
The percentage of NESB students taught by a teacher influenced scores at the multivariate
level (Roy’s Largest Root =0.03, F(2,224)=3.26,p<0.05). The interaction of the percentage of
NESB students taught with the teaching of different cultural groups also influenced scores at
the multivariate level (Roy’s Largest Root =0.06, F(19,225)=2.83,p<0.05)
Univariate testing indicated the effect for Percentage of NESB students
(F(1,225)=6.52,p<0.05) and also the interaction between Percentage of NESB students and
cultural group (F(5,225)=2.83,p<0.05) to be statistically significant. Examination of mean
estimates indicated that students with more than 20% of NESB students in their classroom
were more positive in terms of responses on the scale. The interaction between percentage of
NESB students and teaching of cultural groups is illustrated in the error plot provided in
Figure 5. This figure indicates that teachers of Indigenous students were more confident than
those with fewer such students.
Follow-up nonparametric Kruskal-Wallis tests were used to examine associations between
percentage of NESB for each of the three items on the teaching confidence scale, and for
each of the six cultural groups. These tests found that teachers with more than 20% of NESB
students in their classroom where Indigenous students were included were significantly more
likely to report confidence in determining PI students’ proficiency as speakers and listeners
(χ2(1)=4.71,p<0.05), and in responding to PI students’ English literacy needs
(χ2(1)=6.18,p<0.05). In addition, teachers with more than 20% of NESB students, where
European students were included, were significantly more likely (p<<0.05) to report
proficiency in responding to PI students as readers and listeners (χ2(1)=5.44,p<0.05) plus
confidence in responding to literacy needs (χ2(1)=6.18,p<0.05).
Example 3: ICT
A MANOVA was used to compare the current and preferred means of male and female
teachers for the two dimensions of ICT use defined by the instrument, namely: (D1) ICT as a
tool for the development of ICT-related skills and the enhancement of curriculum learning
outcomes; and (D2) ICT as an integral component of reforms that change what students learn
and how school is structured and organised.
The multivariate result was significant for gender, Pillai’s Trace = .02, F = 3.50, df = (4,924),
p = .01, indicating a difference in the level of student use of ICT between male and female
teachers. The univariate F tests showed there was a significant difference between males and
females for D1, F = 7.73, df = (1,927), p = .01, and D2, F = 6.59, df = (1,927), p = .01, with
respect to how frequently their students currently use ICT.
However, the F tests for both dimensions on the preferred scale were not significant, F =
1.55, df = (1,927), p = .21 for D1, and F = .00, df = (1,927), p = .99 for D 2. Thus, male and
female teachers were not significantly different in their preferred level of student use of ICT.
3
5. Reporting MANOVA: Four examples (not necessarily definitive)
Table 5 displays the means for male and female teachers for the current and preferred scales
for both dimensions of student ICT use.
Table 5: A comparison of means (with Standard Deviations) for male and female teachers for the two
dimensions of ICT use by students for both the Current and Preferred scales (N = 929)
Teacher Dimension 1 Dimension 2
Dimension 1 Current Dimension 2 Current
Gender Preferred Use 2.75 Preferred Use 2.47
Use 1.97 (0.61)* Use 1.58 (0.54)*
Female (0.62) (0.70)
Male 2.1 (0.60)* 2.81 (0.59) 1.68 (0.56)* 2.47 (0.67)
* indicates significance at p < .05
As can be seen in Table 5, male teachers perceived that their students currently use ICT more
frequently than the students of female teachers for both the curriculum enhancement and
transformation dimensions of ICT use. However, a non-significant result for both dimensions
of the preferred scale indicates that there is no real difference between male and female
teachers with respect to how they’d prefer their students to use ICT.
Example 4: Internationalisation
Method of Analysis
Analyses of the relationship between the IVs, the first three scales of internationalisation, and
the 10 subscales of the affective scales were conducted using MANOVAs. Of the 58 IVs
considered, one to two were entered into the MANOVA at a time, with combinations of IVs
selected such that cell sizes equalled or exceeded 30 (i.e., sufficient cell size to ensure
normalcy of distribution of individual differences). When significant interactions were found,
the file was split by both variables and MANOVAs were conducted with the other variable
and only the significant findings were reported. Whenever Levene’s test for homogeneity of
variance was significant at the p<.01 level (in most cases on one to four of the 13 scales and
subscales), nonparametric statistics (Kruskal-Wallis) were used to confirm the effects
obtained via the MANOVAs. When significant interactions were found on scales for which
Levene’s was significant, the file was split by the significant variable and Kruskal-Wallis was
used to confirm the effects on the other variable. In almost all cases, the Kruskal-Wallis tests
confirmed the findings of the MANOVAs. In those cases, the results of the MANOVAs only
were reported. In cases where significant results were found on one test but not the other,
they were not reported. Because of the large number of IVs and DVs, the consequent number
of significance tests, and the increased likelihood of making a Type I error, only results
significant at the p<.001 level were reported (Abdi, 2007).
Multivariate Effects
Significant multivariate effects were found for the majority of IVs (see Table 24). There were
no significant multivariate effects for major: biological or physical sciences; major: social
sciences, law, criminology, or international studies; and socio-economic status. In addition,
no statistically significant results were found for students who had studied abroad in terms of
the effect of level of school at which they studied abroad, duration, number of study abroad
experiences, or level of immersion. Also, for students who had travelled abroad, no
significant effects were found for travel abroad at any age except 18 and above. Among those
who had travelled to a developing country, only non-significant effects were obtained in
relation to those who had gone for the purposes of work, cultural exchange, or study abroad.
4
6. Reporting MANOVA: Four examples (not necessarily definitive)
Table 24. Significant Multivariate Effects (at p<.001 level)
Variable(s) Pillai’s Trace F df Error df
Country .166 16.735 13 1093
Year at University .073 6.645 13 1093
Country * Year at University .034 2.917 13 1091
University .213 10.036 26 2186
University * Year at University .054 2.318 26 2180
Total Years at University .137 4.035 39 3279
Age .141 6.369 26 2186
Gender .158 15.827 13 1093
Race/Ethnic Group .145 6.569 26 2182
Born out of the Country .086 7.875 13 1093
Second Language Spoken at Home .276 31.994 13 1092
Mother Born Abroad .043 3.729 13 1089
Father Born Abroad .034 2.961 13 1089
Mother Born Abroad * Father Born Abroad .033 2.868 13 1089
Mother’s Education .135 2.327 65 5445
Father’s Education .108 1.831 65 5405
GPA .088 2.006 39 2595
Major: Business, Economics or Hospitality .042 3.682 13 1093
Major: Humanities, Communication, Journalism, or Foreign Languages .061 5.450 13 1093
Major: Education .031 2.720 13 1093
Major: Engineering, Aviation, IT, or Mathematics .052 4.570 13 1093
Major: Health, Human, or Medical Sciences .031 2.656 13 1093
How Often International News Watched on TV or Listened to on Radio .345 10.928 39 3279
How Often Read International News in Newspaper, Magazine, or Online .294 9.138 39 3279
Religion .186 5.531 39 3273
Frequency of Attendance at Religious Services .112 3.246 39 3279
Political Beliefs .341 7.839 52 4368
TV Stations Watched for International News: Australia .297 5.746 39 2043
TV Stations Watched for International News: U.S. .302 2.302 52 1464
International Major .110 10.411 13 1093
Courses with Primarily International Content .158 7.195 26 2186
Courses with Some International Content .162 7.387 26 2184
Participation in Group Projects with International Students .067 6.028 13 1093
Number of International Friends .367 11.693 39 3276
Dated Someone from Another Country .122 11.680 13 1093
Number of International Events Attended in the Past Year .218 6.596 39 3279
Number of International Lecturers or Teaching Assistants .135 2.926 52 4360
Study Abroad .155 15.327 13 1086
Other Travel Abroad .067 6.054 13 1089
Travel to a Developing Country (including only those who had studied or travelled abroad) .082 5.037 13 737
Including only those who had travelled to a developing country:
Purpose of Travel to Developing Country: Lived with Own Family .189 4.967 13 277
Purpose of Travel to Developing Country: Tourism or Military .132 3.232 13 277
Including only those who had travelled abroad:
Other Travel Abroad: Ages 18 and Up .074 4.338 13 707
Other Travel Abroad: Number of Trips .099 1.858 39 2121
Purpose of Other Travel Abroad .208 6.299 26 1414
Duration of Other Travel Abroad .196 2.795 52 2828
5
7. Reporting MANOVA: Four examples (not necessarily definitive)
Univariate Effects – Background Variables
Country. Country was entered into a MANOVA with the DVs. Significant univariate effects
were found on two scales and three subscales such that American students scored
significantly higher on all five (see Table 25). Question-level examinations of differences
were performed for several scales using MANOVAs. On the scale of IB – Academic
Involvement, significant differences were found on both questions. On the scale of IA&P –
Cultural and National Self-Awareness, significant differences were only found for question
17: “It upsets me when migrants or international visitors criticise my country.” On the scale
of IA&P – Cultural Pluralism, significant differences were only found for question 5: “I
prefer to work with students from my own country on groups projects – it makes things
easier.”
Year at university. Year at university was entered into a MANOVA with the DVs. Significant
univariate effects were found for year at university on two scales and three subscales such
that final year students scored significantly higher on all five (see Table 26).
Table 25 Significant Univariate Effects for Country (at p<.001 level)
df 99.9% Confidence Interval
Dependent Variable df F Country Means
error Lower Bound Upper Bound
Australia 18.467 16.115 20.819
Foreign Language Proficiency 1 1105 42.581
U.S. 26.305 23.116 29.495
Knowledge of a Specific Region or Australia 4.897 3.931 5.862
1 1105 18.065
Country U.S. 6.992 5.683 8.301
Australia 15.457 15.120 15.795
IA&P - Cultural Pluralism 1 1105 12.899
U.S. 16.077 15.619 16.535
IA&P – Cultural and National Self- Australia 5.637 5.415 5.859
1 1105 13.156
Awareness U.S. 6.049 5.748 6.350
Australia 5.372 5.143 5.602
IB – Academic Involvement 1 1105 100.087
U.S. 6.546 6.235 6.858
Table 26. Significant Univariate Effects for Year at University (at p<.001 level)
df Year at 99.9% Confidence Interval
Dependent Variable df F Means
error University Lower Bound Upper Bound
Knowledge of a Specific Region or First year 4.655 3.614 5.696
1 1105 21.704
Country Final year 6.860 5.696 8.023
First year .475 .452 .498
International Knowledge 1 1105 47.323
Final year .548 .522 .573
CC Skills – Intercultural First year 21.761 21.318 22.204
1 1105 13.555
Communication and Teamwork Final year 22.502 22.007 22.997
First year 5.571 5.314 5.828
IB – Academic Involvement 1 1105 17.158
Final year 6.055 5.767 6.342
First year 13.319 12.945 13.692
IB – Political Involvement 1 1105 29.331
Final year 14.238 13.820 14.655
6
8. Reporting MANOVA: Four examples (not necessarily definitive)
Interaction between country and year at university. Country and year at university were
entered into a MANOVA. One significant interaction was found on the scale of Knowledge
of a Specific Region or Country (F(1,1103) = 13.948, p<.001). The file was split by country
to examine the interaction. It was found that the improvement for final year students on the
scale of Knowledge of a Specific Region or Country held true only for American students.
The file was then split by year at university and a MANOVA was performed to investigate
the effects for country. It was found that final year American students performed significantly
better than final year Australian students on the scale of Knowledge of a Specific Region or
Country.
Table 27. Significant Univariate Effects for University
df 99.9% Confidence Interval
Dependent Variable df F University Means
error Lower Bound Upper Bound
GU 18.467 16.115 20.820
Foreign Language Proficiency 2 1104 21.665 KSU 25.445 20.887 30.003
UCBS 27.131 22.665 31.596
GU 4.897 3.931 5.862
Knowledge of a Specific Region
2 1104 9.534 KSU 7.398 5.527 9.269
or Country
UCBS 6.603 4.770 8.436
GU .515 .494 .537
International Knowledge 2 1104 11.546 KSU .533 .491 .575
UCBS .455 .414 .496
GU 5.637 5.416 5.859
IA&P - Cultural and National
2 1104 8.243 KSU 5.880 5.450 6.309
Self-Awareness
UCBS 6.211 5.790 6.632
GU 21.849 21.440 22.259
CC Skills - Intercultural
2 1104 8.031 KSU 22.932 22.138 23.726
Communication and Teamwork
UCBS 22.151 21.373 22.929
GU 5.372 5.143 5.602
IB – Academic Involvement 2 1104 50.376 KSU 6.466 6.021 6.911
UCBS 6.623 6.187 7.059
University. University was entered into a MANOVA with the DVs. Significant univariate
effects were found on three scales and three subscales (see Table 27). Pairwise comparisons
revealed that for the scales of Foreign Language Proficiency and IB – Academic
Involvement, GU students scored significantly lower than students from both American
universities. Further MANOVAs confirmed that the significant differences in IB – Academic
Involvement held true for both questions. On the scales of
Knowledge of a Specific Region or Country and CC Skills – Intercultural Communication
and Teamwork, GU students scored significantly lower than students from KSU. Question-
level comparisons for CC Skills – Intercultural Communication and Teamwork showed that
KSU students scored significantly higher than GU students on questions 1 (“I have worked
successfully with international students on group projects.”) and 4 (“Sometimes international
students have different communication styles, but we still manage to communicate well.”)
and higher than UCBS students on question 1 as well. On the scale of International
Knowledge, students from UCBS scored significantly lower than students from the other two
7
9. Reporting MANOVA: Four examples (not necessarily definitive)
universities. On the scale of IA&P – Cultural and National Self-Awareness, GU students
scored significantly lower than UCBS students. Question-level analysis showed that
significant differences on this scale were only found on question 17 (It upsets me when
migrants or international visitors criticise my country.)
Interaction between university and year at university. One significant interaction was found
between university and year at university on the scale of Knowledge of a Specific Region or
Country (F(2,1101) = 6.784, p<.001). To examine the interaction, the file was split by
university and a MANOVA was run for year at university. Because GU was the only
university in Australia, these results will be the same as for country and will not be repeated
here. The only significant finding was that final year students from UCBS scored
significantly higher than first year students on the scale of Knowledge of a Specific Region or
Country.
Total years at universityhe t number of years at university was entered into a MANOVA with
the DVs. Significant univariate effects were found for two scales and six subscales (see Table
28). Pairwise comparisons revealed that for Knowledge of a Specific Region or Country,
students with one year or less of university scored significantly lower than those with more
than three of university. On the scales of International Knowledge and IB – Political
Involvement, students with one year or less of university scored significantly lower than all
the other groups. On the subscales of IA&P – Cultural Pluralism, CC Skills – Intercultural
Communication and Teamwork, IB – Academic Involvement, and IB – Intercultural
Curiosity and Involvement students with one year or less of university scored significantly
lower than those students with four or more years of university. Finally, on the subscale of
IA&P – Cultural and National Self-Awareness, students with greater than four years of
university scored significantly higher than those with one year or less or one to three years of
university.
Age. Age was entered into a MANOVA with the DVs. Significant univariate effects were
found on two scales and four subscales (see Table 29). Pairwise comparisons showed that for
the scale of Knowledge of a Specific Region or Country, 16-18 year olds had significantly
lower scores than both of the other age groups. On the scale of International Knowledge, all
three age groups were significantly different from one another. On the subscales of IA&P –
Cultural Pluralism and IB – Political Involvement, students aged 23 and over were
significantly different from both of the other age groups. On the subscales of CC Skills –
Intercultural Communication and Teamwork and IB – Intercultural Curiosity and
Involvement, students aged 23 and over were significantly different than those between the
ages of 16 and 18.
8
10. Reporting MANOVA: Four examples (not necessarily definitive)
Table 28Significant Univariate Effects for Total Years at University
df Total Years at 99.9% Confidence Interval
Dependent Variable df F Means
error University Lower Bound Upper Bound
1 or less 4.046 2.907 5.185
Knowledge of a Specific >1 and < or = 3 5.885 4.123 7.647
3 1103 18.071
Region or Country >3 and < or = 4 6.202 4.319 8.085
>4 8.605 6.868 10.342
1 or less .459 .434 .484
>1 and < or = 3 .528 .489 .567
International Knowledge 3 1103 26.800
>3 and < or = 4 .538 .497 .580
>4 .573 .535 .612
1 or less 15.406 15.002 15.810
>1 and < or = 3 15.502 14.877 16.127
IA&P – Cultural Pluralism 3 1103 5.725
>3 and < or = 4 15.913 15.245 16.580
>4 16.270 15.654 16.886
1 or less 5.708 5.442 5.974
IA&P – Cultural and >1 and < or = 3 5.608 5.197 6.019
3 1103 5.721
National Self-Awareness >3 and < or = 4 5.656 5.217 6.095
>4 6.233 5.827 6.638
1 or less 21.712 21.221 22.203
CC Skills – Intercultural >1 and < or = 3 22.081 21.323 22.840
Communication and 3 1103 5.859
Teamwork >3 and < or = 4 22.268 21.457 23.079
>4 22.828 22.080 23.576
1 or less 5.528 5.244 5.812
IB – Academic >1 and < or = 3 5.813 5.373 6.253
3 1103 8.052
Involvement >3 and < or = 4 5.863 5.393 6.334
>4 6.293 5.859 6.727
1 or less 25.146 24.519 25.773
IB – Intercultural >1 and < or = 3 25.967 24.997 26.936
3 1103 5.233
Curiosity and Involvement >3 and < or = 4 26.005 24.969 27.042
>4 26.377 25.421 27.333
1 or less 13.114 12.705 13.523
>1 and < or = 3 13.876 13.242 14.509
IB – Political Involvement 3 1103 18.919
>3 and < or = 4 14.022 13.345 14.699
>4 14.758 14.134 15.383
Gender. Gender was entered into a MANOVA with the DVs. Significant univariate effects
were found for two scales and four subscales (see Table 30) such that males scored
significantly higher than females on the scales of Knowledge of a Specific Region or Country
and International Knowledge and females scored significantly higher than males on the four
affective subscales.
9
11. Reporting MANOVA: Four examples (not necessarily definitive)
Table 29. Significant Univariate Effects for Age (at p<.001 level)
df 99.9% Confidence Interval
Dependent Variable df F Age Means
error Lower Bound Upper Bound
16-18 yrs 3.827 2.471 5.183
Knowledge of a Specific Region
2 1104 16.540 19-22 yrs 5.881 4.529 7.233
or Country
23+ yrs 7.083 5.777 8.389
16-18 yrs .444 .415 .473
International Knowledge 2 1104 60.226 19-22 yrs .495 .466 .524
23+ yrs .578 .550 .606
16-18 yrs 15.276 14.801 15.751
IA&P – Cultural Pluralism 2 1104 12.895 19-22 yrs 15.476 15.003 15.950
23+ yrs 16.233 15.775 16.690
16-18 yrs 21.646 21.068 22.224
CC Skills – Intercultural
2 1104 9.971 19-22 yrs 21.898 21.321 22.474
Communication and Teamwork
23+ yrs 22.682 22.125 23.239
16-18 yrs 24.997 24.259 25.736
IB – Intercultural Curiosity and
2 1104 9.554 19-22 yrs 25.643 24.906 26.379
Involvement
23+ yrs 26.354 25.643 27.065
16-18 yrs 13.103 12.617 13.589
IB – Political Involvement 2 1104 21.297 19-22 yrs 13.604 13.119 14.088
23+ yrs 14.421 13.953 14.889
Table 30.Significant Univariate Effects for Gender (at p<.001 level)
df 99.9% Confidence Interval
Dependent Variable df F Gender Means
error Lower Bound Upper Bound
Knowledge of a Specific Region or Male 7.042 5.630 8.454
1 1105 15.510
Country Female 5.022 4.090 5.954
Male .565 .534 .596
International Knowledge 1 1105 53.407
Female .482 .462 .503
IA&P – Global Interdependence and Male 32.223 31.250 33.196
1 1105 14.324
Cooperation Female 33.560 32.918 34.202
Male 15.036 14.545 15.526
IA&P – Cultural Pluralism 1 1105 26.617
Female 15.955 15.631 16.278
IB – Intercultural Curiosity and Male 24.792 24.029 25.554
1 1105 21.324
Involvement Female 26.070 25.567 26.573
Male 5.491 5.169 5.813
IB – Charitable Involvement 1 1105 24.706
Female 6.073 5.860 6.285
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12. Reporting MANOVA: Four examples (not necessarily definitive)
Table 31. Significant Univariate Effects for Race/Ethnic Group (at p<.001 level)
df Race/Ethnic 99.9% Confidence Interval
Dependent Variable df error F Group Means Lower Bound Upper Bound
Whitea 18.723 16.658 20.788
b
Foreign Language Proficiency 2 1102 46.943 Black 24.677 16.844 32.511
c
Other 34.470 29.451 39.490
a
White 5.080 4.217 5.942
Knowledge of a Specific Region b
2 1102 13.589 Black 6.210 2.940 9.480
or Country
Otherc 8.636 6.540 10.731
a
White .517 .497 .536
b
International Knowledge 2 1102 6.847 Black .453 .379 .527
c
Other .475 .427 .522
a
White 15.511 15.209 15.813
IA&P – Cultural Pluralism 2 1102 8.884 Blackb 16.758 15.612 17.904
c
Other 16.166 15.431 16.900
a
White 21.864 21.498 22.231
CC Skills – Intercultural b
2 1102 10.604 Black 23.274 21.885 24.664
Communication and Teamwork
c
Other 22.907 22.017 23.798
a
White 5.684 5.470 5.897
b
IB – Academic Involvement 2 1102 7.502 Black 6.565 5.754 7.375
c
Other 6.033 5.514 6.552
a b
White, European American/Australian, Non-Hispanic Aboriginal, Torres Strait Islander, Black, or African-
American (all or part) cOther or Multiracial (White + Other)
Race/ethnic group. Race/Ethnic Group was entered into a MANOVA with the DVs.
Significant univariate effects were found on three scales and three subscales (see Table 31).
Pairwise comparisons revealed that the differences on the scale of Foreign Language
Proficiency were due to others scoring significantly higher than both other groups. On the
scales of Knowledge of a Specific Region or Country and CC Skills – Intercultural
Communication and Teamwork, others scored significantly higher than Whites. On the scales
of IA&P - Cultural Pluralism and IB – Academic Involvement, Blacks scored significantly
higher than Whites. On the scale of International Knowledge, none of the pairwise
comparisons were significant at the p<.001 level.
Table 32. Significant Univariate Effects for Born out of the Country (at p<.001 level)
df Born out of the 99.9% Confidence Interval
Dependent Variable df F Means
error Country Lower Bound Upper Bound
Yes 34.168 28.591 39.745
Foreign Language Proficiency 1 1105 66.062
No 19.581 17.592 21.571
Knowledge of a Specific Yes 8.888 6.582 11.194
1 1105 24.422
Region or Country No 5.221 4.398 6.044
Born out of the country. Born out of the country was entered into the MANOVA with the
DVs. Significant univariate effects were found on two scales such that those who were born
abroad had significantly higher scores (see Table 32).
11