Course Contents

Statistics and Computer Application in Education Class: MS Education Course Title: Statistics and Computer Application in Education Course Code: EDU-611 Introduction The aim of this course is to provide students with an introduction to basic statistical tools and quantitative methods that are useful in understanding the type of data encountered in Education. Importantly, it will provide a framework for approaching statistical problems, and experience in learning from associated data. Topics covered include: understanding data, examining relationships, finding difference between groups, and regression, introduction to inference, and nonparametric test. The course also aims to provide familiarity with the use of Excel spreadsheet software for statistical data analysis and problem solving. Learning Outcomes On successful completion of the course, you should be able to: 1. Explain the concepts, theories and techniques of statistical analysis 2. Use statistical skills to present data and apply all relevant statistical tools. 3. Use the standard statistical techniques to interpret and analyze real problems encountered in the discipline. 4. Construct written work which is logically and professionally presented. PART ONE Designing a study (Theory + Practice) • Planning the study • Choosing appropriate scales and measures • Preparing a questionnaire • Preparing a codebook • Variable names • Coding responses • Coding open-ended questions Getting to know SPSS (practice) • Starting SPSS • Opening an existing data file • Working with data files • SPSS windows • Menus • Dialogue boxes PART TWO (Practice) • Preparing the data file • Creating a data file and entering data • Changing the SPSS ‘Options’ • Defining the variables • Entering data • Modifying the data file • Data entry using Excel • Screening and cleaning the data i. Step 1: Checking for errors ii. Step 2: Finding the error in the data file iii. Step 3: Correcting the error in the data file PART THREE (Theory + Practice) • Preliminary analyses • Descriptive statistics • Categorical variables • Continuous variables • Assessing normality • Checking for outliers Using graphs to describe and explore the data (Theory + Practice) • Histograms • Bar graphs • Scatter plots • Box plots • Line graphs • Editing a chart/graph • Importing charts/graphs into Word documents Manipulating the data (Practice) • Calculating total scale scores • Transforming variables • Collapsing a continuous variable into groups • Collapsing the number of categories of a categorical variable Checking the reliability of a scale (Theory + Practice) • Interpreting the output from reliability • Presenting the results from reliability Choosing the right statistic (Theory) • Overview of the different statistical techniques • The decision-making process • Key features of the major statistical techniques PART FOUR (Theory + Practice) • Statistical techniques to explore relationships among variables Correlation (Theory + Practice) • Assumptions • Preliminary analyses for correlation • Interpretation of output from correlation • Presenting the results from correlation • Obtaining correlation coefficients between groups of variables • Comparing the correlation coefficients for two groups • Testing the statistical significance of the difference between correlation coefficients • Partial correlation • Interpretation of output from partial correlation • Presenting the results from partial correlation Multiple regressions (Theory + Practice) • Major types of multiple regressions • Assumptions of multiple regressions • Standard multiple regression • Hierarchical multiple regression • Interpretation of output from hierarchical multiple regression • Presenting the results from multiple regression Factor analysis (Theory + Practice) • Assumptions of Factor Analysis • Steps involved in factor analysis • Procedure for factor analysis • Presenting the results from factor analysis PART FIVE Statistical techniques to compare groups (Theory + Practice) • Type 1 error, Type 2 error and power • Planned comparisons/Post-hoc analyses • Effect size T-tests (Theory + Practice) • Independent-samples t-test • Paired-samples t-test One-way analysis of variance (Theory + Practice) • One-way between-groups ANOVA with post-hoc tests • One-way between-groups ANOVA with planned comparisons • One-way repeated measures ANOVA Non-parametric statistics (Theory + Practice) • Chi-square • Mann-Whitney U Test • Wilcoxon Signed Rank Test • Kruskal-Wallis Test • Friedman Test • Spearman’s Rank Order Correlation

Course Synopsis

The aim of this course is to provide students with an introduction to basic statistical tools and quantitative methods that are useful in understanding the type of data encountered in Education. Importantly, it will provide a framework for approaching statistical problems, and experience in learning from associated data. Topics covered include: understanding data, examining relationships, finding difference between groups, and regression, introduction to inference, and nonparametric test. The course also aims to provide familiarity with the use of Excel spreadsheet software for statistical data analysis and problem solving.

Course Learning Outcomes

On successful completion of the course, you should be able to: 1. Explain the concepts, theories and techniques of statistical analysis 2. Use statistical skills to present data and apply all relevant statistical tools. 3. Use the standard statistical techniques to interpret and analyze real problems encountered in the discipline. 4. Construct written work which is logically and professionally presented.