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SPSS Handbook

Erin L. Robinson

Longwood University

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Table of Contents

SPSS HANDBOOKBY: ERIN L. ROBINSON

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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

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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 numberso 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 The DV will get its own column

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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 inserto Symbol o And change font to symbol o Type 99 for character code

How to insert and x bar o How to for: xo 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 ¿=greater than ≠ = no equal

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¿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

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o Click if condition is satisfied

o Click if and a box will come up arrow over IQ

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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

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Go to transform → recode into different columns

A box will appear o Original variable → move it over

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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

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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

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Click Continue and ok

It will then add a column to the end of your data set with the corresponding letter grade

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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).

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4. Choose decimals to 0 if the data set doesn’t include decimals

5. Choose values if you use words

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6. Enter data (you do not need to enter data in any certain form just go

Down your list of data).

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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 - ( Σ X) 2 N

N

Sample:

sx = ΣX2 - ( Σ X) 2 n

n - 1

Variance = the standard deviation squared Use standard deviation formulas without square root

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To move from variance to standard deviation, take the square root To move from standard deviation to variance, square the number

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B. SPSS

1. Can get descriptives for one column of data at a time2. Analyze Descriptive Statistics Frequencies

3. Toggle your column header from left box to Variable(s) box4. Check Display frequency tables if you want a frequency distribution5. Click on the Statistics button

6. Check off the descriptive that you want

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7. Click the Continue button, then the OK button

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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.

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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

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B. SPSS

1. Go to Microsoft Word and click chart

2. For Frequency Polygon choose line and click ok

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3. Only want series 1 and category 1-7

4. Enter data for categories 1-7 and series 1a. Make sure the blue line is extended across all of the datab. The numbers must be categorizedc. For ex. If you have 1,1,1,1,1,1,1,1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4,6,6d. You need to count up all number 1’s and all number 2’s etc.

i. 1-10ii. 2-4

iii. 3-5iv. 4-4v. 6-2

e. You must include all numbers between the dataset

5. Go back to Microsoft Word

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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

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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

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12. Axis title should show at the bottom of graph

13. Click primary vertical axis title and choose rotated title

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14. Should look like this

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C.

Results

0 1 2 3 4 5 6 70

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Ages of Kids

Freq

uenc

y

Figure 1. The frequency of the number of ages of kids.

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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

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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).

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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 numbers7. Click measures (normally you will use scale

a. Scale= interval and ratio b. Ordinal c. Interval

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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 certainorder).

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10. Click legacy dialogs and choose from list of bars

11. Choose histogram

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12. You must arrow over to specific box (title – to variable)13. You can add title press continue and okay14. The bars of a histogram will be together because they are all the same

category.

15. The graph will be computed

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C.

Results

Figure 1. The frequency of ages of kids in a daycare center.

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A.

Bar Graph

Typically reflects categorical data

Bars reflect amount of frequency

Spaces between bars

Nominal or ordinal data

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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

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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

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9. Make border color – no line

10. Add horizontal axis titles

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11. Add vertical axis title and click rotated title

12. Click fill and select no fill

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13. Go to border and click no line

14. Add figure 1 caption 15. Make sure Figure 1 is italicized

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C.

Results

Brown Blue Green Hazel 0

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Eye Color

Freq

uenc

y

Figure 1. The frequency of eye colors in the best Quantitative Methods class.

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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= x−µS x

Standard error of the mean (it is an estimate of standard

deviation 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 meano Degree of freedom (df)- how many things in the formula that we are

estimating

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o Sig. (2-tailed)- is the percent chance of type 1 error

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

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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

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6. Change to data view

7. Enter data

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8. Choose analyze

9. Then choose compare means

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10. Next choose one sample t- test

11. Arrow over the IV which is GumChewing

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12. Under test value change it to 1.1 because that was our population mean

13. Click okay

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14. The graph will be computed

15. .05 is acceptable error and .074 is way above it so we fail to reject the null ( we fail to reject the null)

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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.

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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 = 0o IV will have no effect on DV

H1: are differenceso x̄1 ≠ x̄2

o x̄1 - x̄2 ≠ 0o Iv will have an effect on DV

Formula: ↙experimental mean vs . control group mean

⟵number∈sample

↑standard error of the difference ( pooled variance ) standarderror of difference isthe estimate of population standarddeviation

N = total number of peope in study

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degree of freedom (df) = N-2o 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

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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 Nemoii. 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)

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iv. Change measure to scalev. 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 participantsa. 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

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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 numbersb. 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

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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

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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

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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

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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

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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

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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

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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

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24. Once you click Ok the graph will be computed

25. Displayed are the results a. The N= # in study which was 20b. The degree of freedom is 18 (20-2=18)c. The mean for the scary movie = 3.00d. The mean for the happy movie =. 70 e. We assume equal variance f. We ignore anything under “Levene’s Test”

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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.

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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).