Chapter 7 Section D
The t Test for Two Independent Sample Means
To perform a two-sample t test in SPSS, select Compare Means from the ANALYZE menu, and
then choose Independent-Samples T Test In the dialog box that opens, move the variable that
distinguish
Chapter 6 Section D
Interval Estimation and the t Distribution
The One-sample t test. The procedure for performing a one-sample t test was described in the
previous chapter under the heading of a one-sample z test, so it will not be repeated here. In this
Chapter 5 Section D
Introduction to Hypothesis Testing: The One-Sample z Test
SPSS does not have a module for performing one-sample z tests; your only option is to perform a
one-sample t test instead. The larger the sample, the smaller the difference betw
Chapter 4 Section D
Standardized Scores and the Normal Distribution
After SPSS has calculated the mean and Standard Deviation (SD) for one of the variables in your
spreadsheet, you could find the corresponding z scores by creating a new variable in the
Co
If Factor levels together is selected in the Plots box, you will get side-by-side boxplots of men
and women for base_HR, and then a separate set of men/women boxplots for preqz_HR.
If Dependents together is selected instead, you will get side-by-side boxp
Chapter 3 Section D
Measures of Central Tendency and Variability
The three measures of central tendency discussed in the text can be obtained from SPSS by
opening the Frequencies: Statistics box described in the previous chapter (click on Analyze,
Descrip
off to correspond with the fact that the frequencies are being divided by the valid N and
multiplied by 100.
If your variable has been measured on a scale that can be considered quantitative (interval
or ratio), you will most likely want to choose Histogr
Chapter 2 Section D
Frequency Tables, Graphs, and Distributions
Frequency distributions can be obtained from SPSS by clicking on Analyze, Descriptive
Statistics, and Frequencies
For whichever Dependent Variabless (DVs) you move into the Variable(s) box,
Recoding. To create a new, two-valued variable from an existing multi-valued one:
1. Select Recode from the TRANSFORM menu
2. Then choose Into Different Variables (this preserves the original multi-valued
variable, which is what you will usually want to d
Chapter 1 Section D
SPSS (an acronym for the Statistical Package for the Social Sciences) is certainly not the only
statpack available, and your instructor may prefer to teach you how to use SAS, Minitab, or
another of SPSSs many competitors. However, bec
in part a appears to be more powerful, as it leads to a higher z score.
2. z = (.55 .50) / (.5)(.5) / 320 = .05/.028 = 1.79, one-tailed p = .0367;
Reject H0 at .05 level; two-tailed p = .0367*2 = .0734; Retain H0 at .05 level.
3. a)
No Imagery
8
11
7
10
9
b)
Estimated Marginal Means of MEASURE_1
12
Estimated Marginal Means
10
8
6
drug group
4
placebo
caffeine
2
1
2
3
4
DISTRACT
You can see some interaction in the graph, but it is not dramatic, so it is not surprising that the
interaction is not near signif
1. a)
Source
Subject
Treatment
Interaction
Total
SS
230.75
25
30
285.75
df
7
1
7
15
MS
F
p
25
4.286
5.83
< .05
The matched t was 2.41 for these data; 2.412 = 5.81. The slight discrepancy with the F found
above is due to intermediate rounding off.
2
RM
=
d
Because 7.0 > 4.08, the contrast is significant at the .05 level.
b) The planned contrast was significant, even though the omnibus ANOVA was not. This contrast
takes advantage of the fact that the means being averaged together are relatively close togethe
4
16
+6
5
9
9
0
6
15
16
+1
7
7
8
+1
8
a)
10
16
20
+4
D
8
D
20
8
2.5; s D
2.93; t
2.5
2.93
2.5
1.036
2.41
8
t.05 (7) = 2.365; t.01 (7) = 3.499; 2.365 < 2.41 < 3.499, therefore the results
are significant at the .05 level (two-tailed), but not the .01 level
b)
Xi 96.08 ; X = +1.5 (2.7) + 96.08 = 4.05 + 96.08 = 100.13;
i
2.7
1.5
Xi 96.08 ; X = -.8 (2.7) + 96.08 = -2.16 + 96.08 = 93.92
i
2.7
.8
2. a) area above z = -.4 = .1544 + .5 = .6544, so 65.44%;
area above z = .93 = .1762, so 17.62%
b) area beyond z = .4