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Data (Secondary Data
The data that have been collected by other…
Data
Secondary Data
The data that have been collected by other researchers.
In business, there are two broad classifications of secondary data – internal and external data.
Evaluating Secondary Data:
- Purpose
- Scope
- Authority
- Audience
- Format
Advantages of secondary data:
- Less resource-intensive
- Allows comparative analysis
- Ideal for longitudinal studies
- Easily accesible
Disadvantages:
- Access is difficult and costly
- May not math the research problem
- Difficult to verify reliability
- Not in manageable form
Classification of secondary data:
- Electronic format (commercial and academic audience)
- Written format
Sampling
Types of sampling:
- Probability (random)
Stages for Sampling:
- Define target population
- Select sampling frame
- Choose sample technique
- Determine size sample
- Collect Data
- Assess your response rate
Data Collection Methods
a) Response equivalence - uniform procedures
b) Timing
c) Status and psychological issues
d) Longitudinal versus cross-sectional
Primary Data
In primary data collection, you collect data yourself using a range of collection tools such as:
- Interviews: face to face, phone interviews, focus groups interviews, elite interviewing
- Observation: associated with qualitative research strategy
- Disguised observation
- Undisguised observation
- Participant observation
- Non-Participant observation
- Recording observation data
- Questionnaires
Data triangulation
Where data are collected at different times or from different sources in the study of a phenomenon.
Investigator triangulation, where different researchers independently collect data on the same phenomenon and compare the results. Methodological triangulation, where both quantitative and qualitative methods of data collection are used.
Triangulation of theories, where a theory is taken from one discipline and used to explain a phenomenon in another discipline.
Analyzing:
Mean arithmetical average of a frequency distribution.
Median (average)
Mode value that occurs the most often in your set of data.
Standard deviation: measures the spread of data around the mean value.
Range: subtracting the lowest value from the highest value in a set of data.
Interquartile Range: measuring the spread between the upper and lower quartiles of a set of data (the middle 50%).
Quantitative
Analyzing QuantitativeStatistical Package for Social Sciences (SPSS).Statistics is a branch of mathematics that is applied to quantitative data in order to draw conclusions and make predictions.
To analyze quantitative data can be divided into: descriptive statistics and inferential statistics.
Presenting Quantitative Data:
- Tables and graphs
Types of data:
- Nominal - cannot be measured numerically
- Ordinal - rank-ordered
- Interval - when the distance between the numbers are equal accross the range
- Ratio - similar to interval data but has a fixed zero point
Qualitative
Qualitative is non-numerical data, is more exploratory
Common approaches to qualitative analysis used by researchers, namely: visual analysis, grounded theory, narrative analysis, discourse analysis and content analysis.
Four analytical steps:
- transcribing your data
- reading and generating categories, themes and patterns
- interpreting your findings
- writing the report.
Approaches to coding data – emergent coding (inductive) and a priori coding (deductive).
Grounded theory is a method in which the theory is developed from the data, as opposed to applying theory from the outset.
Narrative analysis is the study of stories or a chronological series of events. There are two types of narrative: personal narrative and ‘life story’ narrative.
Discourse analysis examines both.