TIPS mates,
My department is considering revamping our current underegraduate
statistics course. I am interested in your opinion as to how much can
be covered in a one semester course. We are considering going to an
SPSS based, non-computational approach. There is considerable
disagreement concerning how much statistics can be covered in one 14
week semester, with 3 50 min classes per week. The following topics are
covered in our current two semester statistics and research methods
courses. We are omitting methods from the proposed course. (That will
be a separate lab-based course). Please let me know any topics you cover
but are omitted from this list. Also, any topics on the list that you
do not cover in a one semestrer course. I will compile the results and
share it with the list. Finally, how satisfied are you with the course
you offer.
Any other thoughts are also appreciated.
Thanks for your help.
Jim
Statistics Curriculum
Introduction
Introduction to course and syllabus
Descriptive and inferential
Relationship and prediction
Role of applied statistics
Preliminary Concepts
Types of variables
Scales of measurement
Randomization
Frequency Distributions and Percentiles
Organization of data
Types of frequency distributions
Percentiles and percentile ranks
Graphical Representations of Frequency Distributions
Basic types of graphical representations
Analysis of shape, and factors affecting shape
Central Tendency
Different measures of central tendency
Variability
Different measures of variability
Transformed scores, introduction
The normal curve
Nature of normal curve
Standard scores
Finding areas under the curve
Normal curve as a model of sampling distributions
Derived scores
Standardization
Comparability of scores
Correlation
Conceptual presentation
Graphical presentation
Prediction
Conceptual presentation
Graphical presentation
Interpretative aspects of correlation and regression
Conceptual issues
Probability
Conceptual presentation
Mathematical presentation
Introduction to the binomial distribution
Introduction to statistical inference
Conceptual link to probability
Random sampling procedures
Random sampling distributions
Testing Hypotheses about Single Means
Understanding the null hypothesis
Z tests
T distribution and T test
Further Considerations of Hypothesis Testing
One-tailed versus two-tailed tests
Types of errors
Significance levels
Hypothesis testing of 2 independent means
Conceptual presentation
Hypothesis testing of 2 dependent means
Conceptual presentation
Estimation
Interval estimations of population parameters
Interval estimation as a method of hypothesis testing
Application of estimation to single sample means, Two independent
means, and dependent means
Power and Effect Size
Review of type I and type II errors
Conceptualization of Power
Factors which influence power
Implications to experimental design
One-Way Analysis of Variance
Conceptualization
Computational concerns
Post hoc procedures
Repeated measures
Factorial Analysis of Variance
Conceptualization
Inference about Correlational Coefficients
Conceptualization
Estimation and hypothesis testing
Chi-Square
Conceptualization
Goodness of fit, and contingency
Non-Parametric Tests
Conceptualization
Different types of tests
Applied Statistics
Choosing the correct statistical procedure
--
James S. MacDonall, Ph.D.
Associate Professor of Psychology
Department of Psychology
Fordham University
Bronx, NY 10458
718 817 3880
