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Introduction to SPSS

Data types and SPSSdata entry and analysis

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In this session

What does SPSS look like? Types of data (revision) Data Entry in SPSS Simple charts in SPSS Summary statistics Contingency tables and crosstabulations Scatterplots and correlations Tests of differences of means

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SPSS/PASW

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Aspects of SPSS

Menus - Analyse and Charts esp. Spreadsheet view of data

Rows are cases (people, respondents etc.) Columns are Variables

Variable view of data Shows detail of each variable type

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Questionnaire Data Coding

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In SPSS

We change ticks etc. on a questionnaire into numbers

One number for each variable for each case How we do this depends on the type of

variable/data

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Types of data

Nominal Ranked Scales/measures Mixed types Text answers (open ended questions)

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Nominal (categorical)

order is arbitrary e.g. sex, country of birth, personality type, yes or no. Use numeric in SPSS and give value labels.

(e.g. 1=Female, 2=Male, 99=Missing)

(e.g. 1=Yes, 2=No, 99=Missing)

(e.g. 1=UK, 2=Ireland, 3=Pakistan, 4=India, 5=other, 99=Missing)

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Ranks or Ordinal

in order, 1st, 2nd, 3rd etc. e.g. status, social class Use numeric in SPSS with value labels

E.g. 1=Working class, 2=Middle class, 3=Upper class

E.g. Class of degree, 1=First, 2=Upper second, 3=Lower second, 4=Third, 5=Ordinary, 99=Missing

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Measures, scales

1. Interval - equal units e.g. IQ

2. Ratio - equal units, zero on scale e.g. height, income, family size, age Makes sense to say one value is twice another

Use numeric (or comma, dot or scientific) in SPSS

E.g. family size, 1, 2, 3, 4 etc. E.g. income per year, 25000, 14500, 18650 etc.

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Mixed type

Categorised data Actually ranked, but used to identify

categories or groups e.g. age groups = ratio data put into groups

Use numeric in SPSS and use value labels. E.g. Age group, 1=‘Under 18’, 2=‘18-24’, 3=‘25-

34’, 4=‘35-44’, 5=‘45-54’, 6=‘55 or greater’

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Text answers

E.g. answers to open-ended questions Either enter text as given (Use String in SPSS) Or Code or classify answers into one of a small number

types. (Use numeric/nominal in SPSS)

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Data Entry in SPSS

Video by Andy Field

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Frequency counts

Used with categorical and ranked variables e.g. gender of students taking Health and

Illness option

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e.g. Number of GCSEs passed by students taking Health and Illness option

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Central Tendency

Mean = average value sum of all the values divided by the number of values

Mode = the most frequent value in a distribution (N.B. it is possible to have 2 or more modes, e.g. bimodal

distribution) Median

= the half-way value, or the value that divides the ordered distribution in the middle

The middle score when scores are ordered N.B. need to put values into order first

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Dispersion and variability

Quartiles The three values that split the sorted data into

four equal parts. Second Quartile = median. Lower quartile = median of lower half of the data Upper quartile = median of upper half of the data Need to order the individuals first One quarter of the individuals are in each inter-

quartile range

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Used on Box Plot

Upper quartile

Lower quartile

Median

Age of Health and Illness students

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Variance Average deviation from the mean, squared

5.20 is the Sum of Squares This depends on number of individuals so we divide by n (5) Gives 1.04 which is the variance

Score Mean DeviationSquared Deviation

1 2.6 -1.6 2.56

2 2.6 -0.6 0.36

3 2.6 0.4 0.16

3 2.6 0.4 0.16

4 2.6 1.4 1.96

Total 5.20

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Standard Deviation

The variance has one problem: it is measured in units squared.

This isn’t a very meaningful metric so we take the square root value.

This is the Standard Deviation

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Using SPSS

‘Analyse>Descriptive>Explore’ menu. Gives mean, median, SD, variance, min,

max, range, skew and kurtosis. Can also produce stem and leaf, and

histogram.

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Charts in SPSS

Use ‘Chart Builder’ from ‘Graph’ menu or the Legacy menu

And/or double click chart to edit it. E.g. double click to edit bars (e.g. to change

from colour to fill pattern). Do this in SPSS first before cut and paste to

Word Label the chart (in SPSS or in Word)

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Stem and leaf plots

e.g. age of students taking Health and Illness option

good at showing distribution of data outliers range

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Stem and leaf plots e.g.

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Box Plot

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Box Plot

Fill colour changed.N.B. numbers refer to case numbers.

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Histograms and bar charts

Length/height of bar indicates frequency

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Histogram

Fill pattern suitable for black and white printing

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Changing the bin size

Bin size made smaller to show more bars

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Pie chart

angle of segment indicates proportion of the whole

Pie Chart

Shadow and one slice moved out for emphasis

Analysing relationships

Contingency tables or crosstabulations Compares nominal/categorical variables

But can include ordinal variables N.B. table contains counts (= frequency data) One variable on horizontal axis One variable on vertical axis Row and column total counts known as marginals

Example

In the Health and Illness class, are women more likely to be under 21 than men?

Crosstabulations

e.g.

Use column and row percentages to look for relationships

SPSS output

Chi-square ²

Cross tabulations and Chi-square are tests that can be used to look for a relationship between two variables:

When the variables are categorical so the data are nominal (or frequency).

For example, if we wanted to look at the relationship between gender and age.

There are several different types of Chi-square (²), we will be using the 2 x 2 Chi-square

2x2 Chi-square results in SPSS

Another example

The Bank employees data

Bank EmployeesChi-Square tests

Chi-Square analysis on SPSS

http://www.youtube.com/watch?v=Ahs8jS5mJKk 4m15s

http://www.youtube.com/watch?v=IRCzOD27NQU

From 6m:30s to 9m:50s

http://www.youtube.com/watch?v=532QXt1PM-Q&feature=plcp&context=C3ba91a4UDOEgsToPDskJ-ABupdp-Yfvuf4j4fJGzV 12m30s

Low values in cells

Get SPSS to output expected values Look where these are <5 Consider recoding to combine cols or rows

Tabulating questionnaire responses

Categorical survey data often “collapsed” for purposes of data analysis

Original category Frequency Collapsed category Frequency

White British 284 White 304

White Irish 7

Other White 13

Indian 40 South Asian 105

Pakistani 32

Bangladeshi 33

Chinese 16 Chinese 16

Black British 30 Black 44

Afro-Caribbean 12

African 2

An analysis on a sample of 2 (e.g. Black African) would not have been very meaningful!

Recoding variables

http://www.youtube.com/watch?v=uzQ_522F2SM&feature=related

Ignore t-test for now 6m11s

http://www.youtube.com/watch?v=FUoYZ_f6Lxc

Uses old version of SPSS, no submenu now. 6m

Scatterplots and correlations

Looks for association between variables, e.g. Population size and GDP crime and unemployment rates height and weight

Both variables must be rank, interval or ratio (scale or ordinal in SPSS).

Thus cannot use variables like, gender, ethnicity, town of birth, occupation.

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Scatterplots

e.g. age (in years) versus Number of GCSEs

Interpretation

As Y increases X increases

Called correlation

Regression line model in red

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Correlation measures association not causation

The older the child the better s/he is at reading The less your income the greater the risk of schizophrenia

Height correlates with weight But weight does not cause height Height is one of the causes of weight (also body shape,

diet, fitness level etc.) Numbers of ice creams sold is correlated with the

rate of drowning Ice creams do not cause drowning (nor vice versa) Third variable involved – people swim more and buy more

ice creams when it’s warm

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Scatterplot in SPSS

Use Graph menu http://www.youtube.com/watch?

v=74BjgPQvIEg 8m34s

http://www.youtube.com/watch?v=blfflA-34pQ&feature=related 4m04s

http://www.youtube.com/watch?v=UVylQoG4hZM 1m50s, ignore polynomial regression

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Modifying the Scatterplot

http://www.youtube.com/watch?v=803YCYA2AoQ&feature=related 4m04s

http://www.youtube.com/watch?v=vPzvuMuVXk8&feature=related 3m40s

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If mixed data sets

Change point icon and/or colour to see different subsets.

Overall data may have no relationship but subsets might.

E.g. show male and female respondents. Use Chart builder

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Correlation

Correlation coefficient = measure of strength of relationship, e.g. Pearson’s r

varies from 0 to 1 with a plus or minus sign

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Positive correlation

as x increases, y increases

r = 0.7

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Negative correlation

as x increases, y decreases

r = -0.7

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Strong correlation (i.e. close to 1)

r = 0.9

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Weak correlation (i.e. close to 0)

r = 0.2

Interpretation cont.

r2 is a measure of degree of variation in one variable accounted for by variation in the other.

E.g. If r=0.7 then r2=.49 i.e. just under half the variation is accounted for (rest accounted for by other factors).

If r=0.3 then r2=0.09 so 91% of the variation is explained by other things.

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Significance of r

SPSS reports if r is significant at α=0.05 N.B. this is dependent on sample size to a

large extent. Other things being equal, larger samples

more likely to be significant. Usually, size of r is more important than

its significance

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Pearson’s r in SPSS

http://www.youtube.com/watch?v=loFLqZmvfzU 6m57s

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Parametric and non-parametric

Some statistics rely on the variables being investigated following a normal distribution. – Called Parametric statistics

Others can be used if variables are not distributed normally – called Non-parametric statistics.

Pearson’s r is a parametric statistic Kendal’s tau and Spearman’s rho (rank

correlation) are non-parametric.59

Assessing normality

Produce histogram and normal plot

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Use statistical test

SPSS provides two formal tests for normality : Kolmogorov-Smirnov (K-S) and Shapiro-Wilks (S-W) But, there is debate about KS Extremely sensitive to departure from normality May erroneously imply parametric test not

suitable – especially in small sample So, always use a histogram as well.

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Often can use parametric tests

Parametric tests (e.g. Pearson’s r) are robust to departures from normality

Small, non-normal samples OK But use non-parametric if

Data are skewed (questionnaire data often is) Data are bimodal

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Spearmans’s rho

http://www.youtube.com/watch?v=r_WQe2c-ISU From 4.14 to 4.56

http://www.youtube.com/watch?v=POkFi5vKvI8&feature=fvwrel 6m16s

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So far…

Looked at relationships between nominal variables

Gender vs age group

Looked at relationships between scale variables

Height vs. Weight

Now combine the two Groups vs a scale variable

E.g. Gender vs income

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Reminder – IV vs DV

IV = independent variable What makes a difference, causes effects, is responsible

for differences.

DV = dependent variable What is affected by things, what is changed by the IV.

Gender vs income. Gender = IV, income = DV So we investigate the effect of gender on income

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Example 1Age group vs. no. of GCSEs

Using the Health and Illness class data Age group defines 2 groups

Under 21 21 and over

Just two groups Can use independent samples t-test Independent because the two groups consist of

different people. t-test compares the means of the 2 groups.

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Difference of means

Do under 21s have more or fewer GCSEs than 21 and overs?

Means are different (6.44 & 4.28) but is that significant?

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No significant difference therefore assume equal variances

Means are statistically significantly different

Parametric vs non-parametric

Just as in the case of correlations, there are both kinds of tests.

Need to check if DV is normally distributed. Do this visually Also use statistical tests

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Tests for normality

Kolmogorov-Smirnov and Shapiro-Wilk If n>50 use KS If n≤50 use SW Null hypothesis is ‘data are normally distributed’. So if p<0.05 then data are significantly different

from a normal distribution – use non-parametric tests

If p≥0.05 then no significant difference – use parametric tests

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Checking normality

Produce histogram of DV Tick box to undertake statistical test Interpret results.

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t-test

Identify your two groups. Determine what values in the data indicate

those two groups (e.g. 1=female, 2=male) Select Analyze:Compare Means:Independent

samples t-test http://www.youtube.com/watch?

v=_KHI3ScO8sc 9m40s

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Mann-Whitney U test

Use this when comparing two groups and the DV is not normally distributed

http://www.youtube.com/watch?v=7iTvv3m9d_g 3m45s

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Comparing 3 or more groups

ANOVA = Analysis of Variance Analyze: Compare Means: One-way ANOVA http://www.youtube.com/watch?

v=wFq1b3QjI1U 4m04s

Useful to get table of means (descriptives) and means plots from ANOVA options.

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ANOVA Means and F value

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ANOVA Means Plot

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