Fundamentals of Data Analysis

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• Fundamentals of Data Analysis
• Structure, Coding
• Frequency Distributions, Cross Tabulation

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Data Analysis

• A set of methods and techniques used to obtain
  information and insights from data
• Helps avoid erroneous judgements and conclusions
• Can constructively influence the research
  objectives and the research design

3
Preparing the Data for Analysis

• Data Editing
• Identifies omissions, ambiguities, and errors in
  responses
• Conducted in the field by interviewer and field
  supervisor and by the analyst prior to data
  analysis

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Preparing the Data for Analysis

• Problems Identified With Data Editing
• Interviewer Error
• Omissions
• Ambiguity
• Inconsistencies
• Lack of Cooperation
• Ineligible Respondent

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Preparing the Data for Analysis

• Statistically Adjusting the Data Variable
  Re-specification
• Existing data is modified to create new variables
• Large number of variables collapsed into fewer
  variables
• Creates variables that are consistent with study
  objectives
• Dummy variables are used (binary, dichotomous,
  instrumental, quantitative variables)

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Preparing the Data for Analysis

• Scale transformation
• Scale values are manipulated to ensure
  comparability with other scales
• Standardization allows the researcher to compare
  variables that have been measured using different
  types of scales
• Variables are forced to have a mean of zero and a
  standard deviation of one
• Can be done only on interval or ratio scaled data

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Simple Tabulation

• Consists of counting the number of cases that
  fall into various categories
• Determine empirical distribution (frequency
  distribution) of the variable in question
• Calculate summary statistics, particularly the
  mean or percentages
• Aid in "data cleaning" aspects

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Frequency Distribution

• Reports the number of responses that each
  question received
• Organizes data into classes or groups of values
• Shows number of observations that fall into each
  class
• Can be illustrated simply as a number or as a
  percentage or histogram
• Response categories may be combined for many
  questions
• Should result in categories with worthwhile
  number of respondents

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Descriptive Statistics

• Statistics normally associated with a frequency
  distribution to help summarize information in the
  frequency table
• Measures of central tendency mean, median and
  mode
• Measures of dispersion (range, standard
  deviation, and coefficient of variation)
• Measures of shape (skewness and kurtosis)

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Analysis for Various Population Subgroups

• Differences between means or percentages of two
  subgroup responses can provide insights
• Difference between means is concerned with the
  association between two questions
• Question upon which means are based are
  intervally scaled

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Cross Tabulations

• Statistical analysis technique to study the
  relationships among and between variables
• Sample is divided to learn how the dependent
  variable varies from subgroup to subgroup
• Frequency distribution for each subgroup is
  compared to the frequency distribution for the
  total sample
• The two variables that are analyzed must be
  nominally scaled

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Factors Influencing the Choice of Statistical
Technique

• Type of Data
• nominal, ordinal, interval and ratio scales of
  measurement
• Nominal scaling is restricted to the mode as the
  only measure of central tendency
• Both median and mode can be used for ordinal
  scale
• Non-parametric tests can only be run on ordinal
  data
• Mean, median and mode can all be used to measure
  central tendency for interval and ratio scaled
  data

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Overview of Statistical Techniques Univariate
techniques

• Appropriate when there is a single measurement of
  each of the 'n' sample objects or there are
  several measurements of each of the n'
  observations but each variable is analyzed in
  isolation
• Non-metric - measured on nominal or ordinal scale
• Metric-measured on interval or ratio scale
• Determine whether single or multiple samples are
  involved
• For multiple samples, choice of statistical test
  depends on whether the samples are independent or
  dependent

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Overview of Statistical TechniquesBi-variate
techniques

• Two interval variables
• Correlation, regression, t-test
• Two nominal variables
• Contingency coefficient, Chi-square test
• Two ordinal variables
• Rank correlation, MW U-test, KS test

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Overview of Statistical TechniquesMultivariate
techniques

• Dependence Techniques
• One or more variables can be identified as
  dependent others are independent
• Interdependence Techniques
• Examines a set of interdependent relationships

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Why use Multivariate Analysis?

• To group variables or people or objects
• To improve the ability to predict variables (such
  as usage)
• To understand relationships between variables
  (such as advertising and sales)

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Summary

• Understand the purpose of data analysis
• Preparing date involves editing correcting
  numerous problems
• variable re-specification, scale transformation
• Analysis process moves from simple tabultions to
  descriptive statistics, to cross-tabulations and
  analysis of sub-groups
• Choice of analysis technique is made from
  univariate, bi-variate, or multi-variate
  techniques

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Notes on the SPSS assignment 2