Analysis Method

Analysis Method
Learning Outcomes
• Students should be able to design analysis
method used
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:
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
– 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
• 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
• 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
Testing Hypotheses
• Using an appropriate statistical analysis to test
• 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