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

COURSE DESCRIPTION: |

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**course artifacts.**

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.

**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.

**Pearson’s 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 researcher’s hypothesis.

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

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Copyright 2004 by Victor A. Montemurro. All right reserved. |