1 UNIT 13: DATA ANALYSIS. 2 A. Editing, Coding and Computer Entry Editing in field i.e after...
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Transcript of 1 UNIT 13: DATA ANALYSIS. 2 A. Editing, Coding and Computer Entry Editing in field i.e after...
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UNIT 13:DATA ANALYSIS
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A. Editing, Coding and Computer Entry
• Editing in field i.e after completion of each interview/questionnaire.
• Editing again for any errors not detected in the field.
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• Coding of questionnaires i.e. converting data to numerical codes. This will allow for statistical analysis.
• Entering data into the computer. Research variables and their numeric values are entered into the computer.
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B. Research Variables• To carry out statistical analysis, concepts
have to be operationalized. These concepts have to be defined as variables.
• It is important to explicitly set out the variables of your study and how they will be measured. In any one study, some variables will be nominal, ordinal, interval and ratio.
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• The level at which variables are measured is a very important determinant of how the research data will be analyzed i.e. statistical analysis techniques.
• It is useful to identify research variables at the proposal preparation stage
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C. Descriptive Statistics• Summarizing data using descriptive
statistics. This enables a researcher to describe the distribution of the research variables using a few indices or statistics.
• Descriptive statistics give a good quick picture of how the variables behaved i.e. their distribution.
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• In starting your analysis, compute descriptive statistics for all your variables. This will serve as yet another quality check on your data.
• Descriptive statistics are of various types
- Measures of central tendencyMode, median, mean.
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- Measures of variability: This is the
dispersion of measures around the central
score e.g. the mean. Typical measures of
variability are the range and standard
deviation.
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- Frequency distribution: This gives a picture of the shape of a variable i.e. how it is distributed.
* Frequency tables
* Histograms
* Frequency polygrams
* Bar charts
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- Percentages: Proportions of sub-groups
to the total group. They range from 0% to
100%. Percentages are important where
there is need to compare groups that differ
in size.
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D. Relationships• There are statistics that measure the
relationships between two or more variables.
• Correlation co-efficientCo-movement of variables
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• Chi-square statisticsdependence or independence of variables.
• Regression co-efficientDetermining an equation that explains how variables are related.
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E. Inferential Statistics• The ultimate purpose of research is to be
able to generalize the results from samples to populations.
• Hypothesis testing techniques are used to generalize from the sample to the population. Generalizing from a sample to a population is statistical inference.
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• Commonly used statistical procedures in
statistical inference include correlation,
regression (simple and multiple),
chi-square test and analysis of variance
(for experimental data).