Hi Miguel: I paste for you the guidelines we provide to adjuncts as to what should be covered in our one and only required stats course. I abbreviated it a bit in terms of some subcategories.
II Descriptive Techniques A. Distributions and graphs B. Measures of Central Tendency C. Measures of Variablity III Hypothesis Testing A. Probability – normal distribution B. Sampling distributions C. Logic of hypothesis testing D. Estimation and confidence intervals. IV Inferential Techniques: Testing Differences Between Two Groups A. z-test B. t-test 1. independent samples 2. paired samples V Inferential Techniques: Testing Differences with More than Two Groups: Analysis of variance A. Main effects B. Interactions VI Testing Correlational Hypotheses A. Correlational statistics B. Regression analysis VII Nonparametric Tests Clearly things like structural equations, multidimensional scaling or factor analysis are not the things they will learn in our one and only required stats class. Our only other course is optional and is devoted to teaching students to learning how to use SPSS but doesn't really take them beyond what they get in the one semester of stats. Annette Annette Kujawski Taylor, Ph.D. Professor of Psychology University of San Diego 5998 Alcala Park San Diego, CA 92110 619-260-4006 [EMAIL PROTECTED] ---- Original message ---- >Date: Sun, 17 Jun 2007 18:17:03 -0400 >From: "Miguel Roig" <[EMAIL PROTECTED]> >Subject: [tips] Knowledge of statistics in our best students >To: "Teaching in the Psychological Sciences (TIPS)" <[email protected]> > >Tipsters, over the years, I have reviewed a number of papers that are >submitted to local and national conferences, as well as to other outlets of >student research (e.g., student journals, competitive awards). In many cases >you can 'hear' the student 'voice' in their writing with respect to the >various stock phrases (e.g., "many experiments show ..."), inappropriate use >of terms (e.g., utilize, prove) that we often find in student papers. You >also get a general idea that the paper is, indeed, the student's own >research by, for example, the type of topic chosen (e.g., eating disorders >is a fairly popular one), the general design of the study, and even in type >of the data analyses used (e.g., correlations, t-tests simple ANOVAs). In >sum, in those cases I have no doubts that the project was the students' own >even if I suspect that the student has received considerable assistance from >a mentor. However, in other cases, a student submission is written at a >professional or near-professional level, the literature review shows a >fairly thorough grasp of relevant issues, and the data are analyzed with >fairly sophisticated statistical techniques (e.g., hierarchal regression, >MANOVAs, ANCOVAS, structural equations). Some of these papers are of such >high quality that, frankly, one begins to wonder the extent of the student's >contribution to the paper. > >I actually have some evidence indicating that some students are given >unmerited authorship (see >http://facpub.stjohns.edu/%7Eroigm/presentations/student%20authorship%20in%2 >0EPA%2006.ppt). However, it is with respect to students' knowledge of >advanced statistical techniques that I now want to pick your brains. So, >here are my questions for the group: What are the most advanced data >analysis techniques that you are covered in the Statistics course offered in >your department? Does your department offer an advanced statistics course >and what areas do you cover in those courses? For both questions, a link to >a syllabus or course description will be sufficient. > >TIA > >Miguel > > > >--- >To make changes to your subscription go to: >http://acsun.frostburg.edu/cgi-bin/lyris.pl?enter=tips&text_mode=0&lang=english > N����!jx ��2��칻 �*m�����!����,�w���[�� v�܂&���r�+)��
