Course Identification

Quantitative Methods in Educational Research

Lecturers and Teaching Assistants

Prof. David Fortus, Dr. Giora Alexandron

Course Schedule and Location

Full Year
Monday, 12:30 - 14:00

Field of Study, Course Type and Credit Points

Science Teaching: Lecture; Obligatory; Regular; 2.00 points


Once every two weeks.

All assignments, including the mid-term test, are to be done at home, in groups of three or less. Each group of students should turn in a single copy of the group homework or mid-term test with all names listed; all group members will receive the same grade. The final exam is individual. You can use books, calculators or computers. The homework will contain tasks and questions that will require you to use a statistical computer program. All assignments must be submitted electronically by the beginning of class, two weeks after they were distributed.





Language of Instruction


Attendance and participation

Required in at least 80% of the lectures

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)


Evaluation Type

Take-home exam

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



  • Introduction: Collecting Data - Slide Show #1
  • Graphical & Numerical Descriptive Statistics - Slide Show #2
  • Probability & Probability Distribution - Slide Show #3
  • Parameter Estimation & Hypothesis Testing - Slide Show  #4
  • t-tests & Different Types of Errors - Slide Show #5
  • Comparing 2 Population Means - Slide Show #6
  • Comparing variances - Slide Show #7
  • Comparing more than 2 Populations: ANOVA - Slide Show #8
  • Categorical Data: Chi-square tests - Slide Show #9
  • Correlation & Regression - Slide Show #10
  • IRT (Rasch Analysis) - Slide Show #11
  • Factor Analysis - Slide Show #12

Learning Outcomes

  • Collecting data
    • Specify the objective of a published study or survey
    • Identify the variables of interest
    • Choose an appropriate design
    • Design data collection procedures
  • Descriptives
    • Use appropriate software to:
      • Prepare various graphical representations of data (histograms, bar charts, scatterplots, boxplots, etc.).
      • Calculate measures of central tendency, variability, and normality (mean, mode, median, standard deviation, skewness, and kurtosis)
    • Compare the advantages and disadvantages of the various representation of data
    • Critique the representations of data chosen by existing studies.
  • Probability
    • Define what the probability of an event is.
    • Calculate and estimate probabilities under various conditions
    • Distinguish between different types of variables – nominal, ordinal, and interval.
    • Analyze and appraise the normal, chi-square, and F distributions
  • Parameter estimation
    • Provide intuitive justifications for and against the Central Limit Theorem.
    • Estimate the mean of a population from a sample.
    • Estimate the variance of a population from a sample
    • Determine the required sample size for estimating µ.
    • Calculate confidence intervals for various levels of significance
  • Hypothesis testing
    • Compare various methods for testing hypotheses
    • Perform t-tests for independent samples, both by hand and using statistical software.
    • Perform t-tests for paired samples, both by hand and using statistical software.
    • Perform the Wilcoxon rank-sum test for independent samples
    • Perform the Wilcoxon signed-rank test for paired samples
    • Analyze whether required conditions are met for the various statistical tests
    • Argue for and against t-tests
    • Describe the need for power analyses
    • Compare type I and type II errors
    • Perform power analyses
    • Compare the means of more than two populations using ANOVA and the Kruskal-Wallis test
    • Compare variances of two or more populations
    • Make inferences regarding the difference between population proportions using the goodness-of-fit test
    • Use contingency tables to test for independence and homogeneity
  • Regression
    • Estimate model parameters
    • Test inferences about regression parameters
    • Predict new values using regression
  • Factor Analysis
    • Distinguish between exploratory facto analysis (EFA) and confirmatory factor analysis (CFA)
    • Determine whether data can be factorized
    • Decide on the appropriate number of factors
    • Argue for the need to rotate factors orthogonally or obliquely
  • Rasch Analysis
    • Describe under which situation Rasch analysis is appropriate and required
    • Construct Wright maps of transformed data
    • Evaluate the infit of items and individuals

Reading List

  1. Ott, R.L. (1993). An Introduction to Statistical Methods and Data Analysis (4th ed.). Bekmont, CA: Wadsworth, Inc. This is the best textbook to be found in Weizmann's library. A newer edition of it (2001) is excellent.
  2. Ott, R.L. and Longnecker, M. (2001). An Introduction to Statistical Methods and Data Analysis (5th ed.). Pacific Grove, CA: Duxbury.
  3. Computer Software: You may wish to use a statistical package to complete some assignments. It is your responsibility to decide which package you wish to use and to purchase it, if you so decide. We will use either SPSS or R in our examples.