1. Statistical Package for the Social Sciences (SPSS)
IBM SPSS Statistics 19.0
Yupaporn Siribut

2. Objectives
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

3. The research process
MINZAS ??

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

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

6. 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

7. Session I:
An Overview
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

8. Usefulness of SPSS SPSS/ PASW provide followings; Session I:
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

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

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

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

12. How the output of SPSS presents?
Session I:
How the output of SPSS presents?

13. How the output of SPSS presents?
Session I:
How the output of SPSS presents?

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

15. How the output of SPSS presents?
Session I:
How the output of SPSS presents?

16. Session I:
The research process

17. Session I:
What we need to prepare?

18. Preparing the codebook involves deciding about;
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

19. Session I:
1.Preparing a codebook

20. 1.Preparing a codebook

21. 1.Preparing a codebook
Output

22. To prepare a data file, three key steps are covered in;
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

23. 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”

24. Session I:
3.Data entry

25. A First Look at SPSS Statistics 19
Session I:
A First Look at SPSS Statistics 19
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….

26. Data editor for entering data
Session I:
Data editor for entering data

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

28. 3.1What to measure? Session I: Cont.
Things to think about before entering data Cont.

29. b) Levels of measurement
Session I:
3.1What to measure?
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.

30. Categorical (entities are divided into distinct categories):
Session I:
3.1What to measure?
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.

31. Continuous (entities get a distinct score):
Session I:
3.1What to measure?
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.

32. Session I:
Time to Break !!! ^__^

33. Common sources of error are: missing data coded as “999”
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

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

35. SPSS output – Scatter plot

36. SPSS output - Histogram

37. Session I:
Detection
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.

38. 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

39. a) Descriptive statistics
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

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

41. Descriptive statistics
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

42. Frequency Command

43. SPSS output

44. Time to have a Lunch !!! ^__^
Session I:
Time to have a Lunch !!!
^__^

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

46. 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.

47. Example of research question:
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

48. Correlation

49. 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.

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

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

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

53. 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.

54. 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. self-esteem scores)

55. SPSS out put

56. 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.

57. 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?

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

59. Practice exercises
Part 1: Getting started

60. Practice exercises
Part 2: Preparing the data file

61. Practice exercises
Part 3: Preliminary analyses

62. References
Carver, R. H., & Nash, J. G. (2011). Doing data analysis with SPSS version 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.

63. Thank You !