Quantitative analysis using SPSS

Quantitative Analysis Using Alaa Sadik, Ph.D. Curricula & Instruction, Faculty of Education South Valley University, Qena 11183, Egypt e-mail: alaasadik@hotmail.com http://www.freewebs.com/alaasadik جامعة جنوب الوادي مركز تنمية قدرات أعضاء هيئة التدريس

Quantitative Analysis Using SPSS Manipulation of Data Data Analysis

Manipulation and Transformation of Data

Manipulation and Transformation of Data
Recode
Compute
Replace missing values
Select cases
Sort cases
Merge files
Aggregate data

Methods for transforming data
Computing a new variable
Recode
into same variable
different variable
Select subset of cases
Random sample

Compute a new variable
You can calculate different variables from the existing variables.
For this you need to know the way to compute your target variable from the existing variables.
You can perform operations like addition, subtraction, division and multiplication of variables to create a new variable.

Recode into same variable
Using SPSS you can recode a variable into the same variable?

Recode into different variable
You can Recode existing variable into a different variable.
Recode into Different Variables reassigns the values of existing variables or collapses ranges of existing values into new values for a new variable.
For example, you could collapse salaries into a new variable containing salary-range categories.

Select subset of cases
You can select subset of cases for your analysis using SPSS.
For example, you can use select procedure if you want to do analysis of the relation between education of females and their income from the data set that has information of both males and females.

Missing observations can be problematic in analysis, and some time series measures cannot be computed if there are missing values in the series.
Replace Missing Values creates new time series variables from existing ones, replacing missing values with estimates computed with one of several methods.

Aggregate data
Aggregate Data combines groups of cases into single summary cases and creates a new aggregated data file.
Cases are aggregated based on the value of one or more grouping variables.
The new data file contains one case for each group.

Create time series
Create Time Series creates new variables based on functions of existing numeric time series variables.
These transformed values are useful in many time series analysis procedures.
Available functions for creating time series variables include differences, moving averages.

Sort cases
You can sort cases of the data file based on the values of one or more sorting variables.
You can sort cases in ascending or descending order.
If you select multiple sort variables, cases are sorted by each variable within categories of the prior variable on the Sort list.

Merge files
There are two types of merging:
Adding new cases for the same variables.
Adding new variables for the same cases.
Depending on what you want to add you select this option.

Add cases
Add Cases merges the working data file with a second data file that contains the same variables but different cases.
For example, you might record the same information for customers in two different sales regions and maintain the data for each region in separate files.
Variables from the working data file are identified with an asterisk (*). Variables from the external data file are identified with a plus sign (+).

Add variables
Add Variables merges the working data file with an external data file that contains the same cases but different variables.
For example, you might want to merge a data file that contains pre-test results with one that contains post-test results.
You can save this new file with a new name after merging.

Before merging…
Cases must be sorted in the same order in both data files.
If one or more key variables are used to match cases, the two data files must be sorted by ascending order of the key variable(s).
Variable names in the second data file that duplicate variable names in the working data file are excluded by default because Add Variables assumes that these variables contain duplicate information.

Analysis Data

Types of Variables
Nominal
example : nationality, race, gender…
based on a concept (two categories variable called “dichotomous nominal”)
Ordinal
example: knowledge, skill... (more than, equal, less than)
rank-ordered in terms of a criterion from highest to lowest
Interval/Ratio
example: age, income, speed...
based on arithmetic qualities and have a fixed zero point

Types of Analysis
Univariate Analysis
Descriptive Statistics (Summarising Data)
Frequency Distributions
Frequency tables
Histograms

Types of Analysis
Univariate Analysis
Central Tendency
The mean
The median
The mode

Types of Analysis
Univariate Analysis
Central Tendency
The mean the arithmetic average
identifies the balance point in a distribution of scores.
 = (  X ) / N

Types of Analysis
Univariate Analysis
Variance
spread of data around the mean
The range
Standard deviation

Types of Analysis
Univariate Analysis
The Range
The range is the difference between the highest and lowest scores.
= Range = Highest Score - Lowest Score

Types of Analysis
Univariate Analysis
Standard Deviation
The standard deviation is the average amount of deviation from the mean within a group of scores.
The greater the spread of scores, the greater the standard deviation.

Types of Analysis Skewness Skewness refers to the degree and direction of asymmetry in a distribution. No Skew Positively Skewed Negatively Skewed

Types of Analysis
Bivariate Analysis
Exploring
differences
relationships
between two variables

Types of Analysis
Bivariate Analysis
Exploring differences between two variables
Criteria for selecting bivariate tests of differences
Type of data (nominal/ordinal/interval)
Purpose of investigation (means/varience)
Relationship between groups (independent/dependent)
Number of groups (two/more)

Types of Analysis
Bivariate Analysis
Parametric vs non-parametric tests
The scale of measurment is of equal interval .
The distribution is normal .
The variences of both variables are homogenous .

Types of Analysis
Bivariate Analysis
1. Non-parametric tests
Categorical variables
Non-categorical variables
2. Parametric tests
Non-categorical variables

Types of Analysis
Bivariate Analysis
Non-parametric tests - Categorical variables
- Binomial test: to compare frequencies, two categories, one sample
Example: Ratio of male to female in specific industry compared to industry in general.
- Chi-square test: to compare frequencies, more than two categories, one sample
Example: Number of workers from four different ethnic groups

Types of Analysis
Bivariate Analysis
Non-parametric tests - Categorical variables
- Crosstabulation : two or more categories, unrelated samples
Example: The proportion of male to female workers in both white and black workers.
- Q test: three or more categories, related samples
Example: The number of people who didn’t attend the three meetings.

Types of Analysis
Bivariate Analysis
Non-parametric tests - Non-categorical variables
- Kolmogorov-Smirnov test: one sample & two unrelated samples
- Median test: two or more unrelated samples
- Mann-Whitney U test: two unrelated samples
- Kruskal-Wallis H test: three or more unrelated samples
- Wilcoxon test: two related samples
- Friedman test: three or more related samples

Types of Analysis
Bivariate Analysis
Non-parametric tests - Non-categorical variables
- Mann-Whitney U test: two unrelated samples
Example: Rated quality of work for men and women.
- Wilcoxon test: two related samples
Example: Rated quality of work is the same in the first and second month.

Types of Analysis
Bivariate Analysis
Parametric tests - Non-categorical variables
- t test: one sample
Example: The mean of a sample to that of the population
- t test: two unrelated samples
Example: Job satisfaction between men and women
- One-way ANOVA (analysis of variance): three or more unrelated means
Example: Job satisfaction of four ethnic groups

Types of Analysis
Bivariate Analysis
Parametric tests - Non-categorical variables
- Levene’s test: three or more unrelated variances
Example: The variances of job satisfaction across four ethnic groups
- t test: two related means
Example: Means of the same subject s in two conditions

Types of Analysis
Bivariate Analysis
Exploring relationships between
two variables: Crosstabulation
To demonstrate the presence or absence of a
relationship ( nominal and ordinal variables)

Types of Analysis
Bivariate Analysis
Exploring relationships between two variables:
Correlation
To show the strength and the direction of a relationship
( ordinal and interval variables)
1. Rank correlation (ordinal variables)
2. Linear correlation (interval variables)

Types of Analysis
Bivariate Analysis
Exploring relationships between two variables
Rank correlation
for ordinal variables and non-parametric samples
Spearman’s rho
Kendall’s tau

Types of Analysis
Bivariate Analysis
Exploring relationships between two variables
Linear correlation
for interval variables and parametric samples
Pearson’s r
Regression (for making predications of likely values of the dependent variable)

www.spss.com

Thank U Quantitative Analysis Using SPSS by Alaa Sadik For more examples and information about this presentation visit my site below www.freewebs.com/alaasadik

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Quantitative analysis using SPSS

by Alaa Sadik

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