Quantitative Analysis Using by fsd65350

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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
     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

   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.
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

by Alaa Sadik

For more examples and information about this presentation visit
my site below
www.freewebs.com/alaasadik

								
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