Study Guide for Test 2.Mht

Dr. Gass

HCOM 308

Fall 2009

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Study Guide fo= r Test 2

Chapter 8 =

*parameter (whole population), p.144

**sample (subset of a population), p. 145

*data (records, observations, or measurements), p. 146

***descriptive statistics, (a.k.a. summary statistics), p. 146

single numerical indicators that describe or summarize data sets

***measures of central tendency (averages), pp. 147-157

****mean or arithmetic average, pp. 14= 7-148

= ***most sensitive, precise measure of central tendency

= ***affected by extreme scores, e.g., outliers

= ***may skew data

= ***most useful for statistical analysis

= ***median, pp. 148-149

= ***physical middle point of a distribution, middle value in a set of scores=

= ***as many scores above as below the median

= ***often reported along with the mean

= ***less subject to skewing by extreme scores

= **mode pp. 149-150

= *used infrequently in social science research

= *bimodal and multimodal distributions

= *unaffected by extreme scores, e.g., outliers

***measures of variability (dispersion), pp. 157-165

= **range, p. 158

= difference between highest and lowest scores

= **variance (just the concept, not the formula), pp. 159-160

= ****standard deviation (just the concept, not the formula), pp. 160-162

= *average amount of deviation in a distribution

= *most sensitive, precise measure of variability

***displaying descriptive statistics in SPSS, pp. 151-154

Chapter 13=

****factorial designs, pp. 264-265

= ****number of variables versus number of conditions

= ****2 X 2 design, 2 X 3 X 2 design, etc., p. 264

= **sample size

rule<= /span> of thumb: 3= 0-50 subjects per condition

minimum cell size 5= -10 subjects, p. 265

= **as the number of conditions increase, demand for subjects increases exponenti= ally

**measurement of the dependent variable

= **scales and diagnostic instruments, p. 265

= **behavioral measures, p. 265

= **physiological measures, p. 265

***common experimental designs

= ***Campbell and Stanley’s 3 types of experiments

= *pre-experiments, pp. 273-274

= &n= bsp; one-shot case study

= &n= bsp; one group, pretest-posttest design<= /p>

= &n= bsp; static group comparison = =

**quasi-experiments, pp. 274-276<= /o:p>

= &n= bsp; two group, pretest-posttest design<= /p>

= &n= bsp; time-series design

true<= /span> experiments=

= &n= bsp; **= *pretest, posttest, control group design, p. 276

= &n= bsp; **= *posttest only, control group design, p. 277

= &n= bsp; **= *Solomon four-group design, p. 278

standard= normal distribution

= ; **know the underlying assumptions of inferential statistics

= ; **con= tinuously distributed variable

= ; **pro= bability theory

= ; ****a= reas under the normal curve

= ****know the proportion of cases that are +/- 1 SD, +/- 2 SD, +/- 3 SD from the mea= n

= ****know where 68%, 95%, and 99% of cases would be

= **** significance level, p < .05, p < .01,

difference tests, Ch. 16, 17, 18

****know the symbols for each test we covered in class

****know when and under what circumsta= nces each test should be used

****chi-square test, Ch. 16=

***cross-tabs or contingency tables

****independent samples t-test, Ch. 17=

***one-way ANOVA, Ch. 18

***two-way ANOVA

***factorial analysis of variance (2 X= 2, 3 X 3, etc.)

****know the difference between the nu= mber of variables and the number of conditions

****main effects and interaction effec= ts

= **statistical artifact (chance result)

correlation tests, Ch. 19

****what correlation tests prove<= /o:p>

= &n= bsp; ***direction of correlation

= &n= bsp; ***degree or strength of association

= &n= bsp; ***statistical significance

***know the r values for a weak, moder= ate, or strong correlation

****bivariate correlations, e.g., two variables

****scatterplot or scattergram

****positive versus negative correlati= ons

***linear, nonlinear, curvilinear relationships

= ****correlation coefficient (know what an “r” value represents)

= ****Pearson r

= **Spearman rho and Kendall’s tau-b

= *Cramer’s V

**correlation doesn’t prove caus= ation

**coefficient of determination (just k= now the basic concept)

**spurious relationship

Interpreting Gr= aphs and Charts

= X/Y graph, i= dentify the independent versus the dependent variable

= Be able to interpret a frequency table

Be able to interpret a histogram<= /o:p>

= Be able to compare two histograms (their means and standard deviations)<= /o:p>

Be able to interpret a scattergram (or scatterplot)

Be able to distinguish a positive corr= elation, negative correlation, or curvilinear correlation

Be able to assess main effects or interaction effects on an X/Y graph

SPSS=

= ***creating a variable

= ***values and value labels

= ****columns versus rows

= ****levels of measurements in SPSS (nominal, ordinal, scale)

= entering and saving data

= ***select cases; “select if” condition

= ***creating a scattergram

= ***creating a histogram

= ***interpreting various SPSS results, e.g. p < .05, p < .01, p < .001<= /span>

= **performing a chi-square

= ***performing a t-test

= **performing a one-way ANOVA

= ***performing a Pearson r

= **if you see columns of data, you should be able to tell which significance test applies

**if you see a results table, you shou= ld be able to interpret it

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