Analysis Method
Learning Outcomes
• Students should be able to design analysis
method used
Outlines
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Getting data ready for analyses
Data analysis and interpretation
The appropriate statistical test
The fittest of data analysis to the purpose of
the study
Data Analysis
• Four steps in data analysis:
1.
2.
3.
4.
Getting data ready for analysis
Getting a feel for the data
Testing the goodness of data
Testing the hypotheses
Getting Data Ready
• After data are obtained through
questionnaires, interviews, observation, or
through secondary sources, the next steps are:
– Editing data
– Handling blank responses
– Coding data
– Setting up a categorization scheme
– The data have to be keyed in
– Using software program to analyze them
• Editing data:
– Data have to be edited when they relate to responses to
open-ended questions of interviews and questionnaires, or
unstructured observations to see the incompleteness and
inconsistencies of the answers
• Handling blank responses:
– If a substantial number of questions have been left
unanswered, it is good to throw out the questionnaire and
not include it in the data set for analysis.
– If only two or three left blank, the ways to handle are:
• Using an interval-scaled item with mid point as the response to
particular item
• Allow the computer to ignore the blank responses when the analyses
are done
• To assign the mean value of all those who have responded to the
particular item
• To give the missing response a random number within the range for
that scale
• Coding data:
– Using scanner sheets
– Using coding sheets
• Categorization:
– Set up a scheme for categorizing the variables such
that several items measuring a concept are all
grouped together
• Entering data:
– If questionnaires data are not collected on answer
sheet, then the raw data will have to be keyed into the
computer
– After that, the data are ready for analysis
Feel for Data
• Get mean, variance, standard deviation on each variable
• See if for all item, responses range all over the scale and
not restricted to one end of the scale alone
• Obtain Pearson Correlation among the variables under
study
• Get frequency distribution for all the variables
• Tabulate your data
• Describe your sample’s key characteristics (demographic
details of sex composition, education, age, length of
service, etc)
• See histogram, frequency polygons, etc
Testing Goodness of Data
• Reliability means testing for both consistency
and stability
– Consistency indicates how well the items measuring a
concept hang together as a set. Cronbach’s alpha is a
reliability coefficient that indicates how well the
items in a set are positively correlated to one another.
The closer Cronbach’s alpha is to 1, the higher the
internal consistency reliability
– Split-half reliability coefficient can be used to reflect
the correlation between two halves of a set of items.
• Validity
– Factorial validity can be established by submitting the
data for factor analysis. The results of factor analysis
will conform whether or not the theorized dimensions
emerged
• Criterion-related validity can be established by testing for
the power of the measure to differentiate individuals who
are known to be different
• Convergent validity can be established when there is
correlation between two different sources responding to the
same measure (supervisors and subordinates respond
similarly to a perceived reward system)
• Discriminant validity can be established when two distinctly
different concepts are not correlated to each other (courage
and honesty, leadership and motivation, attitudes and
behavior)
Testing Hypotheses
• Using an appropriate statistical analysis to test
hypotheses
• Examples:
– T-test can be used to test the significance of
differences of the means of two groups
– Analysis of variance to test the significance of
differences among the means of more than two
different groups using the F test
– Using simple regression and multiple regression
analysis to establish the variance explained in the DV
through independent variables