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IBM SPSS Statistics 19.0
Yupaporn Siribut

 to provides some training in the use of a powerful software package to relieve students of computational drudgery
 to help you understand the concepts and techniques of statistical analysis
 to provide practice exercises on SPSS

The research process
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Contents
Session I: Introduction
1
The usefulness of SPSS/ PASW
2
What we need to prepare?
3
4
Introduction to descriptive statistics
Exploring data by Graphs

Contents
1 Session II: Practice exercises
2 Doing basic statistics on SPSS
3 Doing regression on SPSS
4 Interpreting the result
Cont.

Session I:
An Overview
 Statistical Package for the Social Sciences
(SPSS) software, since 2009 known as
Predictive Analysis Software (PASW)
 Statistical software used by commercial, government, and academic organizations around the world to solve business and research problems

An Overview
Session I:
Cont.
 Quickly and easily discover new insights from data, test hypotheses, and build powerful predictive models
 Even if you have little or no statistical or mathematical background, PASW Statistics will show you how to generate statistical support and decision-making information quickly and easily

Session I:
Usefulness of SPSS
 SPSS/ PASW provide followings;
 Descriptive statistics (Mean, Median, Mode, Standard deviation, Range)
 Discrete probability distributions (Binomial, Poisson,
Geometric, Hyper geometric)
 Continuous probability distributions (Normal, T, Chi
Square, F)
 Correlation (Rank correlation, Pearson’s correlation)
 Linear regression (Simple and Multiple linear regression)
 Logistic regression
 Market research

Applied research
Session I:
• Factors influencing the adoption of OVF ---Logistic Regression
• Factors influencing the extent of OVF by individual farm households---
Linear Regression

Applied research
Session I: t-tests for individual measures assessed attitudinal differences between participants and non-participants of each group

Applied research
Session I:
Simple linear regression model can be designed to analyze factors influencing adoption of land management

How the output of SPSS presents?
Session I:

How the output of SPSS presents?
Session I:

How the output of SPSS presents?
Session I:
Figure 1 Daily calories intake
(kcal/capita/day) compared with MDER
(1,850 Kcal) across lowland, upland and highland ecosystems.

How the output of SPSS presents?
Session I:

The research process
Session I:

What we need to prepare?
Session I:

Session I:
1.Preparing a codebook
 Preparing the codebook involves deciding about;
 defining and labeling each of the variables
 assigning numbers to each of the possible responses

1.Preparing a codebook
Session I:

1.Preparing a codebook

1.Preparing a codebook
Output

Session I:
2.Creating a data file
 To prepare a data file, three key steps are covered in;
 Step 1. The first step is to check and modify, where necessary, the options that SPSS uses to display the data and the output that is produced
 Step 2. The next step is to set up the structure of the data file by ‘defining’ the variables

Session I:
2.Creating a data file
 Step 3. The final step is to enter the data that is, the values obtained from each participant or respondent for each variable “ Data entry”

3.Data entry
Session I:

A First Look at SPSS Statistics 19
Session I:
Fig 2
If you start up SPSS for the first time, it presents a screen similar to Fig 2
Let everyone take look at program….

Data editor for entering data
Session I:

3.1 What to measure?
 a) Independent and dependent variables
 Independent --- Predictor variable
 Dependent variables--- outcome variable
Session I:
Variables
---Things to think about before entering data---

3.1What to measure?
Session I:
Cont.
Things to think about before entering data Cont.

3.1What to measure?
Session I:
Variables
 b) Levels of measurement
 The relationship between what is being measured and the numbers that represent what is being measured is known as the level of measurement .
 Variables can be split into categorical and continuous , and within these types there are different levels of measurement
Things to think about before entering data Cont.

3.1What to measure?
Session I:
Variables
 Categorical (entities are divided into distinct categories):
 Binary variable : There are only two categories (e.g. dead or alive)
 Nominal variable : There are more than two categories
(e.g. whether someone is an omnivore, vegetarian, vegan, or fruitarian)
 Ordinal variable : The same as a nominal variable but the categories have a logical order
(e.g. whether people got a fail, a pass, a merit or a distinction in their exam)
Things to think about before entering data Cont.

3.1What to measure?
Session I:
Variables
 Continuous (entities get a distinct score):
 Interval variable : Equal intervals on the variable represent equal differences in the property being measured (e.g. the difference between 6 and 8 is equivalent to the difference between 13 and 15)
 Ratio variable : The same as an interval variable, but the ratios of scores on the scale must also make sense (e.g. a score of 16 on an anxiety scale means that the person is, in reality, twice as anxious as someone scoring 8)
Things to think about before entering data Cont.

Session I:

4. Screen for errors
 Common sources of error are:
 missing data coded as “999 ”
 'not applicable' or 'blank' coded as “0”
 typing errors on data entry
 Column shift
 “made up”
 coding errors
 measurement and interview error
Session I:

Detection
 Most errors will be detected using three procedures:
 Descriptive statistics
(exp. Standard deviation higher than the mean value)
 Scatter plot
 Histograms

SPSS output –
Scatter plot

SPSS output -
Histogram

Detection
Session I:
3. Screen for errors
 Histogram
 Look at the tails of the distribution. Are there data points sitting on their own, out on the extremes?
 If so, these are potential outliers. If the scores drop away in a reasonably even slope, there is probably not too much to worry about.

Correction
 There are slightly different ways to deal with error in DEPENDENT and INDEPENDENT variables.
 Dependent Variables
• When there are a minimal number of errors, the values are generally recoded to "missing".
• Take a look then recoding a variable
 Independent variables
• set the error values to the data set mean or the group mean

5. Exploring Data
 a) Descriptive statistics
 describe the characteristics of your sample in the method section of your report
 check your variables for any violation of the assumptions underlying the statistical
 techniques that you will use to address your research questions
 address specific research questions

Descriptive statistics
The differences types of descriptive statistics (Mooi and Sarstedt , 2011)

Session I:
Descriptive statistics

Frequency Command
 The Frequency command allows you to analyses a full range of descriptive statistics including the measures of central tendency, percentile values, dispersion and distribution

Frequency Command

SPSS output

Session I:

5.Exploring Data
 Statistical tests
 t-test,
 ANOVA,
 correlation
Session I:

Correlation
 Pearson correlation or Spearman correlation is used when you want to explore the strength of the relationship between two continuous variables.
 This gives you an indication of both the direction
(positive or negative) and the strength of the relationship.

Correlation
 Example of research question:
 Is there a relationship between the amount of control people have over their internal states and their levels of perceived stress? Do people with high levels of perceived control experience lower levels of perceived stress?
 Total perceived stress: tpstress, Total
PCOISS: tpcoiss

Correlation

Interpretation
 In the example given here, the Pearson correlation coefficient ( –.58) is negative, indicating a negative correlation between perceived control and stress.
 The more control people feel they have, the less stress they experience.

Interpretation
 Pearson correlation is .581, which when squared indicates 33.76 per cent shared variance.
 Perceived control helps to explain nearly 34 per cent of the variance in respondents’ scores on the Perceived Stress Scale

Interpretation
The results of the above example using Pearson correlation could be presented in a research report as follows.

t-test
 T-tests are used when you have only two groups
(e.g. males/females) or two time points (e.g. preintervention, post-intervention)
 The rationale of the t test is to test for significant differences in the means of two samples, therefore choose Compare Means

t-test
 2 types of its;
 Independent-samples t-test , used when you want to compare the mean scores of two different groups of people or conditions
 paired-samples t-test , used when you want to compare the mean scores for the same group of people on two different occasions, or when you have matched pairs.

t-test
 Example of research question:
 Is there a significant difference in the mean self-esteem scores for males and females?
 What you need: Two variables:
 one categorical, independent variable (e.g. males/females)
 one continuous, dependent variable (e.g. selfesteem scores)

SPSS out put

t-test
 Are the N values for males and females correct?
 If your Sig. value for Levene’s test is larger than
.05 (e.g. .07, .10) you should use the first line in the table, which refers to Equal variances assumed.
 If the significance level of Levene’s test is p=.05 or less (e.g. .01, .001), this means that the variances for the two groups (males/females) are not the same.
 Therefore your data violate the assumption of equal variance.

ANOVA
 One way ANOVA
 Example of research question: What is the impact of age and gender on optimism?
 Does gender moderate the relationship between age and optimism?

Contents
1 Session II: Practice exercises
2 Doing basic statistics on SPSS
3 Doing regression on SPSS
4 Interpreting the result

Practice exercises
Part 1: Getting started

Practice exercises
Part 2: Preparing the data file

Practice exercises
Part 3: Preliminary analyses

References
 Carver, R. H., & Nash, J. G. (2011).
Doing data analysis with SPSS version 18.0
. Boston, MA: Brooks/Cole
Cengage Learning.
 Mooi, E., & Sarstedt, M. (2011).
A concise guide to market research: The process, data, and methods using
IBM SPSS statistics . Berlin: Springer.
 Pallant, J. (2010).
SPSS survival manual . Maidenhead:
McGraw Hill.