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Victor A. Montemurro
Comprehensive Digital Portfolio
St. John's University School of Education

EDU 9211: Statistics II
Professor Paul Miller, Ph.D.
February/March 2003

SJU Graduate Bulletin 2000-2002

Continues the study the study of inferential statistics begun
in EDU 5655 (Data Analysis) and serves as a tool subject for the prospective producer of research. Provides the student with opportunities to become knowledgeable about and to practice parametric and non-parametric statistical methods for testing hypotheses about population means, variances, coefficients of correlation and ranks; also includes procedures for estimating population parameters and for performing simple and multiple regression analyses. The interpretation and reporting of statistical analyses are emphasized.




Click on the myrtle flower to jump to
course artifacts.

Basic Glossary

tatistical Procedures Chart



In this course, basic research and design issues for experimental and non-experimental research were considered. Extension of inferential procedures to the general linear model, analysis of variance, regression, and chi-square procedures were emphasized. Analysis and interpretation of data using SPSS was required. Practice reading and interpreting research results was provided in the form of class exercises and homework assignments. Communication technology via the Internet system was used in this course to facilitate the discussion and dissemination of assignments, readings and submission of assignments. The cohort worked together through its group e-mail listserv to complete assignments and deliver them to Professor Miller. The data derived from the practical projects of the course will allow students to make application to existing research in the field of education and to perform similar analyses.

Course Syllabus Spring 2003
Web Resources

Selecting a Descriptive Statistical Procedure
Selecting an Inferential Statistical Procedure

Statistical Procedural Steps in SPSS
Survey: Life Orientation Test-Revised (LOT-R)

Text and Supplemental Texts:

Kerlinger, F. W. (1979). Behavioral research: A conceptual approach. London: Holt, Rinehart, &Winston, Inc.

Witte, R. S. (1993).  Statistics. (4th ed.). San Diego, CA: Harcourt Brace College Publishers.

Course Products:

Example 1
An administrator wants to compare the performance of students from the district's two high schools on the SAT math test. Random samples of 50 students taking the SAT were selected from each school. SAT Math scores were recorded.

Homework 1
A sample of 24 college students in Calculus I took a 10-point quiz. Each student reported how many hours they had studied for the quiz. Students also responded to the LOT, a self-report instrument that measures optimism.

Descriptive statistics such as means and standard deviations of all the variables were produced. Bivariate correlations between the variables and an independent-samples t test was conducted also. Example 1 include a one-way ANOVA as well.

Homework 2 - Analysis of a research article

Homework 3
A researcher studied the standardized gains in reading for a random sample of 60 elementary school
students at one school. 10 children were selected randomly from each grade. The researcher wanted to know if gains were the same across all grade levels.

Homework 4
An assistant superintendent wants to compare the improvement in reading of 5th graders in the 3
elementary schools in the district to determine whether the schools yield equal levels of improvement. To take into account differences in ability level across schools, random samples of 14 5th grade students are selected from each school. At the beginning of the year students are tested (pretest) using an appropriate level standardized reading test. Students are tested again at the end of the year using an alternate form of the same test (posttest).

Final Exam
The president of Snake River U. has a problem.  A number of female faculty have complained of unfair differences in salary with their male colleagues. SRU has three colleges: (1) humanities and arts; (2) professional studies; and (3) sciences and engineering. The women cite an analysis of salaries at SRU in which a statistically significant difference in mean salaries between men and women was found (see EXAM.SPO).


Coefficient of correlation: A number that represents the direction and strength of a correlation.

Coefficient of determination: The percentage of variation accounted for in one variable by knowing the value of another variable.

Correlational statistics: Statistics that determine the relationship between two variables.

Dependent variable: The variable assessed by the experimenter to determine whether there is a difference due to the independent variable.

Descriptive statistics: Statistics that summarize research data.

Frequency distribution: A list of the frequency of each score or group of scores in a set of scores.

Frequency histogram: A graph that displays the frequency of scores as bars.

Frequency polygon: A graph that displays the frequency of scores by connecting points representing them above each score.

Independent variable: Typically a variable of interest which the experimenter manipulates.

Inferential statistics: Statistics used to determine whether changes in a dependent variable are caused by an independent variable.

Line graph: A graph used to plot data showing the relationship between independent and dependent variables in an experiment.

Mean: The arithmetic average of a set of scores.

Median: The middle score in a set of scores that have been ordered from lowest to highest.

Mode: The score that occurs most frequently in a set of scores.

Negative skew: A graph that has scores bunching up toward the positive end of the abscissa.

Normal curve: A bell-shaped graph representing a hypothetical frequency distribution for a given characteristic.

Normal distribution (bell-shaped curve): This graph shows the normal distribution of IQ scores as measured by the Wechsler Adult Intelligence Scale. The normal distribution is a type of bell-shaped frequency polygon in which most of the scores are clustered around the mean. The scores become less frequent the farther they appear above or below the mean.

Null hypothesis: The prediction that the independent variable will have no effect on the dependent variable in an experiment.

Pearsonís product-moment correlation: Perhaps the most commonly used correlational statistic.

Percentile: The score at or below which a particular percentage of scores fall.

Pie graph: A graph that represents data as percentages of a pie.

Positive skew: A graph that has scores bunching up toward the negative end of the abscissa.

Range: A statistic representing the difference between the highest and lowest scores in a set of scores.

Scatter plot: A graph of a correlational relationship.

Standard deviation: A statistic representing the degree of dispersion of a set of scores around their mean.

Statistical significance: A low probability (usually less than 5 percent) that the results of a research study are due to chance factors rather than to the independent variable.

Statistics: Mathematical techniques used to summarize research data or to determine whether the data support the researcherís hypothesis.

Variance: A measure of variability indicating the average of the squared deviations from the mean.


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