James and other TIPSters,
In my statistics course, I cover everything on your list in 10 weeks,
although the nonparametrics tend to be neglected at the end (something
I'm going to fix, next time around). I highly recommend SPSS for
Windows as an element of your course. I use it, but I do not introduce
it to my students until around the 4th week or so.
My course is very conceptual--i.e., what do these numbers tell us about
human (or nonhuman) behavior?--but during those first 4 or 5 weeks,
students do crank out the computations "by hand" (with the aid of
calculators). IMHO, students' conceptual understanding of statistics,
and their ability to communicate about statistics, is greatly
facilitated by some knowledge of how the stats are computed.
For example, a "mean squared deviation" is just an abstract term until
students see what it tells us about a person (or an animal) in a sample
and about the sample as a whole. I can see the "lightbulbs go on above
students' heads" when I compare computational formulas, and show
students the commonalities. I am pleasantly surprised at the
sophisticated nature of our discussions as the term progresses.
Dr. Barbara Watters
Mercyhurst College
Erie, PA
[EMAIL PROTECTED]
James S. MacDonall wrote:
>
> 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
