STAGES OF DATA ANALYSIS

STAGES OF DATA ANALYSIS

1. IDENTIFY THE NEED 1

   • In your business data analysis, you must begin with the right questions.
     • Questions should be measurable, clear and concise.
       • Design your questions to your specific problem or opportunity.
         • e.g. start with a clearly defined problem.
           • then define what the information gathered is going to be used for and how it will benefit the company.
             • What exactly do you need to know in order to research the information.

2. DEFINE SCOPE( content, detail, timescale) 2

   • Decide what kind of data you'd need to answer your key question.
     • Finally, in the decision on what to measure, be sure to include any reasonable objections any stakeholders might have.
       • Thinking of how to measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredit your analysis later on.
         • key questions include: what is your time frame?
           • What is your unit of measure? and what factors should be included?

3. IDENTIFY POTENTIAL SOURCES ( SALES FIGURES) 3

   • Before developing plans to collect new data, identify what data sets are already available.
     • While the research plan should be guided by the evaluation questions rather than available data, there are often pre-existing data sets that can be utilised or adapted.
       • these include internal agency and program information and data sets collected by the state and federal governments.
         • However it is important to clarify early if these data sets are available in the format required for evaluation.
           • Data sets might not be able to be disaggregated down to the geographic level or time period required.
             • These data sets will most likely be relevant to the collection of background information and baseline data
               • For example, information on the demographic features of communities or past3 responses to surveys

4. SOURCE AND SELECT INFORMATION 4

   • As you collect and organise your data keep these important points.
     • Before you collect new data, determine what information could be collected from existing databases or sources on hand.
       • verify the trustworthiness and validity of the sources.
         • Determine a file storing and naming system ahead of time to help all tasked team members collaborate.
           • This process saves time and prevents team members form collecting the same information twice.
             • If you need to gather data via observation or interviews, then develop an interview template ahead of time to ensure consistency and save time.
               • Keep your collected data organised in a log with collection dates and add any source notes as you go.
                 • this practice validates your conclusions down the road.

5. SELECT THE MOST APPROPRIATE TOOLS 5

   • There are many well-developed methods available for conceptually or statistically analysing the different kinds of data that can be gathered.
     • When analysing qualitative data, one can develop taxonomies or rubrics to group student comments collected by questionnaires and/or made in classroom discussions.
       • The frequency of certain types of comments can be described, compared between categories and investigated for change across time or differences between classes.
         • Frequency data and chi-square analysis can supplement the narrative interpretation of such comments.
           • For the analysis of quantitative data, a variety of statistical tests are available, ranging from the simple to the more complex.
             • Alternatively using ERM, MIS, CMS, an AL based interpreter or KMS tools will analyse the data with the right instructions to produce results.

6. PROCESS AND ANALYSE DATA 6

   • Begin by manipulation your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in Exel
     • A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum and standard deviation of your data.
       • As you manipulate data, you may find you have the exact data you need, but more likely, you might need to revise your original question or collect more data.
         • Either way, this initial analysis of trend, correlations, variations and outliers helps you focus your data analysis on better answering your question and any objections others might have.
           • During this step, data analysis tools and software are extremely helpful.
             • Visio, Minitab and stata are all good software packages for advanced statistical data analysis.
               • however, in most cases, nothing quite compares to Microsoft Excel in terms of decision- making tools.

7. RECORD AND STORE INFORMATION

   • As you interpret your analysis, keep in mind that you cannot ever prove a hypothesis true: rather, you can only fail to reject the hypothesis.
     • Meaning that no matter how much data you collect, chance could always interfere with your results.
       • Does the data answer your original question?
         • Does the data help you defend against any objections
           • Are there any limitation on your conclusions, any angles you haven't considered?

8. SHARE RESULTS

   • There are two main approaches to writing up the findings of any form of research.
     • The first is to simply report key findings under each main theme.
       • This then accompanied by a lining, separate discussed in relation to existing research.