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unit_5_data_analysis_notes2.docUnit 5:
DATA ANALYSIS
We put a great deal of faith in scientific methods of data collection. Yet, the process can be riddled with errors. Before moving to how we should analyze data collected during an intervention it would be helpful to consider some common research errors.
These include:
mistakes in recording and categorizing data
mistakes in sampling (perhaps drawing a small sample or one that is not representative of the larger workforce — for example, having the poorest 10% in terms of performance and designing a training program around the information they provide)
subject misrepresentation — or the possibility that people may provide inaccurate information
investigator bias that limits what one will find — only looking for and finding what we believe to be the problem
faulty instrumentation or the use of data collection techniques that are neither valid nor reliable
Validity refers to measuring what you actually intend to measure, whereas reliability concerns measuring with consistency and accuracy.
We can also experience errors when interpreting data. Common mistakes involve:
making too much out of limited data (drawing conclusions that may not be warranted)
making too many decisions based on limited data
ignoring important findings because of the commitment to established systems
Effective diagnosis, then, is a function of two factors. Remaining objective and providing helpful feedback.
Objectivity involves:
being aware of any biases in the data collected
questioning and confirming findings before drawing conclusions
looking beyond symptoms, to recognize actual problems
recognizing patterns that emerge
considering the uniqueness of each intervention
understanding how one’s presence can influence the data collection process
Providing Feedback involves:
converting feedback into usable information for the client
clarifying findings and offering assistance in the interpretation of the data
translating findings into an action plans (solutions) for how to address the identified problems.
To develop your skills in providing useful feedback and action plans, complete the Providing Useful Feedback Activity in this unit.
There are many ways to analyze data collected depending on the type of data collection techniques used.
Data can be numerical (quantitative) or textual (qualitative).
Numerical/quantitative data derive from questionnaires or interviews and require statistical analysis. A review of basic statistical techniques is provided in the associated power points. You can find these under the supplemental materials for this unit.
Descriptive
Statistics
Unit 5
Advanced
Statistics
Unit 5
Descriptive Statistics Advanced Statistics
Textual/qualitative data comes from many sources, any which ask for people to report in their own words. This may include open-ended responses on questionnaires, interviews, focus groups, or organizational documents. Regardless of the source, textual/qualitative data require the use of textual analysis. More information about textual analysis is available in the associated power point, which can be found in the supplemental materials for this unit.
Textual
Analysis
Unit 5
Textual Analysis
Analyzing Results
When reporting the results of a needs assessment it is important to include information about the data analysis, but not so much information that the findings are overshadowed by the analysis.
Thus, it is important to streamline the information presented. Only provide the information that is essential. Just because you collected data does not mean you need to report about it necessarily. If it tells us little it will only complicate the process and can be left out of the final report. Or it can be part of a more detailed report when a shorter concise report is also made available.
Final written reports should include:
a general overview
objectives and scope
methods of data collection
methods for data analysis
findings and conclusions
recommendations based on findings
expected benefits
implementation guidelines
