Quantitative Analysis Using

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Quantitative Analysis Using
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• Alaa Sadik, Ph.D.
• Curricula Instruction, Faculty of Education
• South Valley University, Qena 11183, Egypt
• e-mail alaasadik_at_hotmail.com
• http//www.freewebs.com/alaasadik

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Quantitative AnalysisUsing SPSS
Manipulation of DataData Analysis
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ManipulationandTransformation ofData
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Manipulation and Transformation of Data

• Recode
• Compute
• Replace missing values
• Select cases
• Sort cases
• Merge files
• Aggregate data

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Methods for transforming data

• Computing a new variable
• Recode
• into same variable
• different variable
• Select subset of cases
• Random sample
• Replace missing values

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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.

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Recode into same variable

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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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 ().

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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.

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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.

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Analysis
Data
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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

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Types of Analysis

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

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Types of Analysis

• Univariate Analysis
• Descriptive Statistics (Summarising Data)
• Central Tendency
• The mean
• The median
• The mode

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Types of Analysis

• Univariate Analysis
• Descriptive Statistics (Summarising Data)
• Central Tendency
• The mean the arithmetic average

• identifies the balance point in a distribution of
  scores.

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Types of Analysis

• Univariate Analysis
• Descriptive Statistics (Summarising Data)
• Variance
• spread of data around the mean
• The range
• Standard deviation

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Types of Analysis

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

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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.

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Types of Analysis
Skewness Skewness refers to the degree and
direction of asymmetry in a distribution.
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Types of Analysis

• Bivariate Analysis
• Exploring
• differences
• relationships
• between two variables

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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)

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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.

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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

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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

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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 didnt attend
  the three meetings.

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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

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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.

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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

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Types of Analysis

• Bivariate Analysis
• Exploring differences between two variables

• Parametric tests - Non-categorical variables
• - Levenes 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

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Types of Analysis

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

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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)

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Types of Analysis

• Bivariate Analysis
• Exploring relationships between two variables
• Rank correlation
• for ordinal variables and non-parametric samples
• Spearmans rho
• Kendalls tau

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Types of Analysis

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

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www.spss.com
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Thank UQuantitative Analysis Using SPSSby
Alaa Sadik For more examples and information
about this presentation visit my site
belowwww.freewebs.com/alaasadik