Download spss handbook by: erin l. robinson

1
SPSS Handbook
Erin L. Robinson
Longwood University
2
Table of Contents
3
Table of Contents
Title Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
SPSS Handbook Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Basics of SPSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15









When to fail to reject or reject the null . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Differences of between groups design, and within groups design including SPSS layout . . . . . . 4-5
Cohen’s d, confidence intervals, when to do what analysis, and where to look for df . . . . . . . . . . 5
How to insert x bar, chi square, and copy multiple things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
What each symbol means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5-6
What vocabulary to use in results section (affect vs. effect) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
How to reorder variables/ columns, sort cases, and exclude cases . . . . . . . . . . . . . . . . . . . . . . . . 6-8
How to transform/ recode variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8-12
The basics of SPSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-15
Description of Statistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16-20
Frequency Polygon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21-28
Histogram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29-35
Bar Graph. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36-42
Single Sample t. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43-51
Independent t. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52-66
Dependent t . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67-76
One-Way ANOVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77-88
One-Way ANOVA Post hocs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89-94
Repeated Measures ANOVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95-103
Repeated Measures ANOVA Post hocs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104-108
2-Way ANOVA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109-123
Mixed Model ANOVA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124-134
Pearson r. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135-142
Goodness of Fix 𝝌2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143-151
Contingency of Table 𝝌2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .152-160
4
A.
Basic Concepts of SPSS

Open SPSS

Var= variable

If you want to add a title you do not add spaces just capitalize the first letter of each word

Change to 0 decimals

Click under values if you put words

Click measures

Scale= interval and ratio

Under scale is ordinal and nominal

Most of the time it stays on scale

When we have a one-tailed test, divide sig level by 2

To increase the number of characters, click width

Value means you want to use words rather than numbers
o To go faster entering data press the number that corresponds with your data and
value labels and press enter
To reject the null or fail to reject the null:
o We compare sig level to alpha level
 If the sig level is bigger than the alpha level the we fail to reject null
 If the sig level is smaller than the alpha level we reject the null


Between groups design is: when each group is made up of separate people and two
groups are independent of each other
o Layout in SPSS:
 Each level will get its own column

Within groups design is: when each group is made up of the same people and the samples
are dependent on each other
o Layout in SPSS:
 Each IV will get its own column
5



 The DV will get its own column
Nominal and continuous data: will be either a t or ANOVA
All nominal data: will be a chi-square
All variable continuous: will be a Pearson r

Cohen’s d:
o Cohen's d is a measure of effect size. Simply put it indicates the amount of
different between two groups on a construct of interest in standard deviation units

Confidence Interval (CI)
o Types of estimates about performance of a sample
 Point estimate (NHST)
 Interval estimates
 Interval of a certain width, which we feel confident will contain 𝜇


To get the degree of freedom for the DV look under the column that says error
To get a Chi Square symbol:
o Click insert
o Symbol
o And change font to symbol
o Type 99 for character code
How to insert and x bar
o How to for: 𝑥
o Type X
o Hold Alt
o Enter 773
How to copy numerous things
o Hold Ctrl and P and click on the items you want to move over


Symbols:














𝜇 or mu = population mean
x̅ = sample mean
n = # in sample
N = # in population
∴ = therefore
𝜎 = population standard deviation
Sx = sample standard deviation
SS = sum of squares
M = mean
SD = standard deviation
df = degree of freedom
F = F value
t = t value
< = less than
6












> = greater than
≠ = no equal
= equal
α = alpha level
= eta squared
2
 = Chi Square
p = probability value
<= less than or equal to
>= greater than or equal to
Ho = null hypothesis
H1 or Ha = alternative hypothesis
Italicize all F values, t values, p values, r values


Affect = verb
Effect = noun

o Also italicize mean and standard deviation (M, SD)
In the results section place spaces after all equal signs and commas
Reordering variables/ columns


In data view highlight the column heading by holding the side of the mouse until a circle
with a slash through it appears
o And drag it to where you want to a red line will appear while doing it and will
show where the column will go
o Click it once to get it highlighted and the second time hold it a circle will come up
You do this when:
o You want to drop someone out of the data set for various reasons (the participant
was checking the same thing for every question)
Sort Cases:


In data view highlight the column heading you want to be ascending or descending order
and right click on the heading and then choose either ascending or descending
You do this when:
o You want your numbers to be sorted by number either smallest number to largest
or largest to smallest
Exclude Cases:

In data view highlight the column heading
o Go to data
o Select cases
7
o Click if condition is satisfied
o Click if and a box will come up arrow over IQ
8
o Then tell SPSS you want everyone equal to 100 and under 100

o It will then slash out all of the participants that had an IQ of 100 and under
 The results would exclude the ones that are slashed
You do this when:
o You want to exclude the individual from the results if they are under or over a
certain score
Transform/ Recode Variables:


Possible uses:
o If you have Likert Scale questions and you want to reverse some of the questions
o Take some data and change it someone way
Highlight Grade Percent in English Class
9

Go to transform → recode into different columns

A box will appear
o Original variable → move it over
10


Transform it into a new column
Name output variable as Letter_Grade and click change

Click Old and New values
o Use if you want to reverse it back
11




Click on range and type 90 and for through 100 which is an A
Click on value and change it to A
Click output variables are strings so that it will show
Click add





90-100 = A
80-89 = B
70-79 = C
60-69 = D
0-69 = F
12
Click Continue and ok

It will then add a column to the end of your data set with the corresponding letter grade
13
B. SPSS
1. Open SPSS
2. Data View (is where you enter your data and numbers)
3. Variable View (is where you can change the name, type, width, decimals, label, value, and
measure).
14
4. Choose decimals to 0 if the data set doesn’t include decimals
5. Choose values if you use words
15
6. Enter data (you do not need to enter data in any certain form just go
Down your list of data).
16
A.
Descriptive Statistics

Mean = mathematical average of a dataset
ΣX
N

Median = exact middle of dataset; half of the numbers fall above, and half fall
below the median

Mode = most frequently-occurring data point

Standard Deviation = the average distance of data points from the mean

Definitional formulas:
Population:
σ = Σ (X – μ)2
N
Sample:
sx = Σ (X – X)2
n–1

Computational formulas:
Population:
σ = ΣX2 - (ΣX)2
N
N
Sample:
sx = ΣX2 - (ΣX)2
n
n-1

Variance = the standard deviation squared
 Use standard deviation formulas without square root
17


To move from variance to standard deviation, take the square root
To move from standard deviation to variance, square the number
18
B. SPSS
1.
2.
Can get descriptives for one column of data at a time
Analyze  Descriptive Statistics  Frequencies
3.
4.
5.
Toggle your column header from left box to Variable(s) box
Check Display frequency tables if you want a frequency distribution
Click on the Statistics button
6.
Check off the descriptive that you want
19
7. Click the Continue button, then the OK button
20
C.
Results
An analysis of the groups’ variability showed that participants earned a higher number of
questions correct when they studied for the test (M = 46.25, SD = 4.83) than when they did not
study (M = 46.25, SD = 4.83). See Figure 1 for these results.
21
A.
Frequency Polygon

Displays same into as histogram

A line connects individual points

Close off shape by adding a data point lower than the lower and one higher than the
highest

Use for interval or ratio data

To compute a frequency polygon you need to use Word and Excel
22
B. SPSS
1. Go to Microsoft Word and click chart
2. For Frequency Polygon choose line and click ok
23
3.
Only want series 1 and category 1-7
4. Enter data for categories 1-7 and series 1
a. Make sure the blue line is extended across all of the data
b. The numbers must be categorized
c. For ex. If you have 1,1,1,1,1,1,1,1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4,6,6
d. You need to count up all number 1’s and all number 2’s etc.
i. 1-10
ii. 2-4
iii. 3-5
iv. 4-4
v. 6-2
e. You must include all numbers between the dataset
5. Go back to Microsoft Word
24
6. Click your mouse on the outside border and click format axis
7. Choose no fill
8. Go to border color and choose no line
25
9. Erase series one and the legend
10. Go to chart layout
11. Click axis titles primary horizonial axis titles and choose title below axis
26
12. Axis title should show at the bottom of graph
13. Click primary vertical axis title and choose rotated title
27
14. Should look like this
28
C.
Results
12
10
Frequency
8
6
4
2
0
0
1
2
3
4
Ages of Kids
Figure 1. The frequency of the number of ages of kids.
5
6
7
29
A.
Histogram

Bars reflect amount of frequency
o Bars touch each other


The bars touch each other because they are the same continuous data
Use to display interval or ratio data
30
B. SPSS
1. Open SPSS
1. You can choose variable view (this is where you can change your
Settings.
2. Or you can choose data view (this is where you enter your list of data).
31
3. For histogram choose choice variable view
4. Enter name (no spaces) and press enter
5. Change to 0 decimals (my preference)
6. Click under values of you put words, instead of numbers
7. Click measures (normally you will use scale
a. Scale= interval and ratio
b. Ordinal
c. Interval
32
8. Go back to data view and make title a little longer
9. Enter data (for a histogram you can enter the whole list of data in 1 column, no
certain
order).
33
10. Click legacy dialogs and choose from list of bars
11. Choose histogram
34
12. You must arrow over to specific box (title – to variable)
13. You can add title press continue and okay
14. The bars of a histogram will be together because they are all the same
category.
15. The graph will be computed
35
C.
Results
Figure 1. The frequency of ages of kids in a daycare center.
36
A.
Bar Graph

Typically reflects categorical data

Bars reflect amount of frequency

Spaces between bars

Nominal or ordinal data
37
B. SPSS
1. Go to Mircosoft Word
2. To insert a bar graph click chart
3.
Choose column
4.
Make sure the chart starts with 0
38
5. Delete the extra rows and columns that you do not need
6. Enter data
7. The bars are separate for a bar graph because they have different categories
a. For ex. Brown, blue, green, and hazel color eyes are all different categories
8. Delete Series 1
39
9. Make border color – no line
10. Add horizontal axis titles
40
11. Add vertical axis title and click rotated title
12. Click fill and select no fill
41
13. Go to border and click no line
14. Add figure 1 caption
15. Make sure Figure 1 is italicized
42
C.
Results
14
12
Frequency
10
8
6
4
2
0
Brown
Blue
Green
Hazel
Eye Color
Figure 1. The frequency of eye colors in the best Quantitative Methods class.
43
A.
Single Sample t-tests


When to use t tests in general:
o When examining 2 groups to see if there are significantly different from
one another
o When we don’t have both µ &σ to which to compare our sample
Assumption for all t – tests
o Amount of variability is similar in all groups being compared
 Homogeneity of variance assumption)
o Shape of distribution changes with n
 The larger your sample, the closer the distribution looks to a
standard normal distribution
o The more flat the curve is the less people you have in the population

It is harder to reject the null with the more people you have

The goal is: to determine if our sample mean is different from the general
population
o Compare our 1 sample with the general population

Why not use z tests?
o µ is known but σ is not

Procedure:
o 1. Formulate H0 and H1
Formula for single sample t test:

o 𝑡=
x̅−µ
Sx̅
deviation
Standard error of the mean (it is an estimate of standard
of the mean)

Example: Do teenagers chew different pieces of gum per day than the general
population?
o Sample data: 2,1,0,5,4,2,3,6,0
 µ= 1.1
 α= .05
 2 – tailed test

Information gathered from the output:
o t is how many standard deviations you are away from the mean
o Degree of freedom (df)- how many things in the formula that we are
estimating
o Sig. (2-tailed)- is the percent chance of type 1 error
44
B. SPSS
1. Open SPSS (choose type in data)
2. Go to variable view and name the column (GumChewing)
3. Change variable to zero decimals because our sample does not have decimals
45
4. Gum chewing is ratio. This means you click on measure and choose scale
5. Make sure under type it says numeric and nothing else
46
6. Change to data view
7. Enter data
47
8. Choose analyze
9. Then choose compare means
48
10. Next choose one sample t- test
11. Arrow over the IV which is GumChewing
49
12. Under test value change it to 1.1 because that was our population mean
13. Click okay
50
14. The graph will be computed
15. .05 is acceptable error and .074 is way above it so we fail to reject the null (
reject the null)
we fail to
51
C.
Results
A single sample t test revealed no significant difference between the amounts of gum
teenagers chewed (M = 2.56, SD = 2.1) from the general population (M = 1.1), t(8) = 2.05, p =
.074, 2- tailed.
52
A.
Independent t-test

Goal: are our two samples different from one another?
o Do the sample represent different populations?
 Use if no population info

With most research, you do not have knowledge about the population -- you don’t know
the population mean and standard deviation

n1 = # of participants in 1st sample (usually the experimental group)


n2 = # of participants in 2nd sample (usually the control group)
Each group is made up of separate people

Two groups are independent of each other
o Ex. Do scary movies really scare children?
 20 children total
 10 people in experimental condition (scary movie)
 10 people in control condition (happy movie)

Ho: are no differences
o x̄1 = x̄2
o x̄1 - x̄2 = 0
o IV will have no effect on DV
H1: are differences
o x̄1 ≠ x̄2
o x̄1 - x̄2 ≠ 0
o Iv will have an effect on DV
Formula:
↙ experimental mean vs. control group mean



⟵ number in sample
↑ standard error of the difference (pooled variance)
standard error of difference is the estimate of population standard deviation

N = total number of peope in study
53

degree of freedom (df) = N-2
o For ex. 20-2 = 18

Example I will use for B. Section
o 20 partcipants (10 watched scary movie, 10 watched happy movie)
o Will the type of movie (scary vs happy) have an effect on the number of
nights children sleep in their parents bed after watching the movie.
 Type of Movie:
 Poltergeist (scary movie) - experimental group
 Finding Nemo (happy movie) – control group
o IV: type of movie
o DV: number of nights sleeping in parents bed after watching the movie
54
B. SPSS
1. Open SPSS
2. Choose Type in Data
3. Go to variable view and name the column
i. The two conditions: scary movie and happy movie
1. Poltergeist and Finding Nemo
ii. Enter under row 1 enter the first independent variable- Participant Number
iii. We name it that to keep the person’s name confidential (it would be
unethical if we put the persons name)
55
iv. Change measure to scale
v. You can also change width to 10 which is the number of characters you
can enter for value
4. We then go to Data View and enter the number of participants
a. For this example we have 20 just list 1-20
5. Go back to Variable View and enter under row 2 enter the IV – Type of Movie
i. Change measure to scale because we use numbers
ii. You can also change width to 10 which is the number of characters you
can enter for value
56
iii. At this point you can change decimals to 0 because in our example it
doesn’t include decimals
6. Click on value and enter a 1 and label it has scary movie
a. You use value when you want to use words rather than numbers
b. For this example 1 is for scary movie
c. Every time a 1 is entered you are saying the number of nights stayed in parents
bed was because of the scary movie
7. Click Add
57
8. Change value and enter a 2 and label it is happy movie
a. Every time a 2 is entered you are saying the number of nights stayed in parents
bed was because of the happy movie
9. Click add and in the box it will show a legend for example 1= Scary Movie 2= Happy
Movie after press ok
58
10. When a 1 appears it means scary movie and when a 2 appears it means happy movie
11. Make sure you are in variable view and add your third row which is going to be the
dependent variable
a. For this example it would be the number of nights in parents bed, also change
width to 10, decimals to 0, and measure to scale
12. We have 20 participants in our study and 10 were in the experimental group which was
the scary movie so for 1-10 enter in 1 because it represents the scary movie and the
number of nights stayed in parents bed because of scary movie
59
13. We have 20 participants in our study and 10 were in the control group which was the
happy movie so for 11-20 enter 2 because it represents the happy movie and the number
of nights stayed in parents bed because of scary movie
14. In data view click view and check value labels
60
15. At this point you can see that 1-10 are the scary movie condition and 11-20 are the happy
movie condition
16. Now enter under column “Number of Nights Spends in Parents Bed” the corresponding
numbers spent in parents bed according to the data
17. Now click on Analyze
61
18. After you click on Analyze
a. Click on compare means
b. Then click on Independent Samples T Test
19. Arrow over “Number Of Nights In Parents Bed” to the test variable because it is your
dependent variable
62
20. Arrow over “Type of Movie” to the Grouping Variable because it is your independent
variable
21. We have?? after “Type Of Movie” because we need to define the groups, so we click on
Define Groups
63
22. Once you click on Define Groups a box will come up and you need to tell SPSS that
group 1 represents the scary movie so you enter a 1 and then tell SPSS that group 2
represents the happy movie so you enter a 2
23. Then click Continue and Ok
64
24. Once you click Ok the graph will be computed
25. Displayed are the results
a. The N= # in study which was 20
b. The degree of freedom is 18 (20-2=18)
c. The mean for the scary movie = 3.00
d. The mean for the happy movie =. 70
e. We assume equal variance
f. We ignore anything under “Levene’s Test”
65
26. In a sig 2-tailed we look for signifigant differences. We have a 5% chance of error and
we are doing a 2-tail with α = .05. (
we reject the null, it did increase the nights stayed
in parents room when the children watched a scary movie.
66
C.
Results
An independent t- test showed that the group that watched the scary movie spent more
nights sleeping in parent’s bed after watching movie (M = 3.00, SD = 1.7) than the group that
watched the happy movie (M = .70, SD = 1.06). The two group’s results were statistically
different from one another t(18) = 3.63, p = .002, 95% CI[.969, 3.63] (two-tailed).