# Quantitative Analysis Using by fsd65350

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‫جامعة جنوب الوادي‬
‫مركز تنمية قدرات أعضاء هيئة التدريس‬

Quantitative Analysis Using

Curricula & Instruction, Faculty of Education
South Valley University, Qena 11183, Egypt
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
 Replace missing values
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.
Replace missing values

 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 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 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.
Data   Analysis
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
Descriptive Statistics (Summarising Data)

   Central Tendency
   The mean
   The median
   The mode
Types of Analysis
Univariate Analysis
Descriptive Statistics (Summarising Data)

   Central Tendency
   The mean the arithmetic average

m = (S X) / N
identifies the balance point in a distribution of scores.
Types of Analysis
Univariate Analysis
Descriptive Statistics (Summarising Data)

   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
   Exploring differences between two variables
   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
   Exploring differences between two variables
1. Non-parametric tests
 Categorical variables
 Non-categorical variables
2. Parametric tests
 Non-categorical variables
Types of Analysis
Bivariate Analysis
   Exploring differences between two variables
 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
   Exploring differences between two variables
 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
   Exploring differences between two variables
 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
   Exploring differences between two variables
 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
   Exploring differences between two variables
 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
   Exploring differences between two variables
 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)

t
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