Data analysis

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					            Data analysis

Data analysis is a process of gathering and
transforming data with the goal of highlighting
useful information, suggesting conclusions and
supporting decision making
          Steps in processing of data
1.   Preparing raw data : data collection is a significant part of market research. Even
     more significant is to filter out the relevant data from the mass of data collected.
     Data continues to be in raw form, unless they are processed and analysed. The
     information so collected by field staff is called raw data
2.   Editing : the main purpose of editing is to eliminate errors and confusion. It
     means the activity of inspecting, correcting and modifying the correct data. This
     can be done in two stages
a.   Field editing : to make sure that proper procedure is followed in selecting the
     respondent, interview them and record their response
•    Inappropriate respondents
•    Incomplete interviews
•    Improper understanding
•    Lack of consistency
•    Legibility
•    Fictitious interview
b. Office editing : it is more thorough than field editing. The job of an office editor is
    more difficult than that of the field work. E.g.
• Inconsistency
• Positive & negative
• Open – ended question
3.Coding : coding refers to these activities which helps in transforming edited
    questionnaires into a form that is ready for analysis. Coding speeds up the
    tabulation while editing eliminates errors. Coding involves assigning numbers or
    other symbols to answer so that the responses can be grouped into limited number
    of classes or categories. E.g. 1 is used for male, 2 is used for female
Some guidelines to be followed in coding
• Establishment of appropriate category
• Mutually exclusive
4.Tabulation :It refers to counting the number of cases that fall into various categories.
     The results are summarized in the form of statistical tables. The raw data is divided
     into groups and sub-groups. The tabulation involves a. sorting and counting, b.
     summarizing of data
Kinds of Tabulation
 i. simple or one way tabulation : the multiple choice question which allow only one
     answer may use one way tabulation or univariate. There may be two types of

a.    Question with only one response : it consists of only one answer
b.    Question with multiple response : respondents may give more than one answer to
      a given question
ii. Cross tabulation or two-way tabulation : his is know as bivariate tabulation. The data
      may include two or more variables. Cross tabulation is very commonly used in
      market research
5.Summarising the data : before taking up summarizing , the data should be
   classified into relevant and irrelevant. Summarizing the data includes
a. Classification of data :
•    Number of groups : the number of groups should be sufficient to record
     all possible data. The classification should not be too narrow
•    Width of the class interval : class interval should be equal width. This will
     provide consistency in the data distribution
•    Exclusive categories :the classification should be done in such a way that
     the response can be placed in only one category
•    Exhaustive categories : this should be made to include all responses
     including “ don’t know” answers. Sometime this will influence the
     ultimate answer to the research problem
•    Avoid extremes : avoid open ended class interval
6.Usage of statistical tools
a. Frequency distribution : It is simply reports the number of responses that
    each question receives. Frequency distribution organizes the data into
    classes or groups. It shows the number of data that falls into particular
    class. The three most common ways to measure central tendency are
•   Mean : – group of observation is obtained by dividing the sum of all the
    observations by their number. it is a tool to identify the average
•   Median : if a group of N observation is arranged in ascending or
    descending order of magnitude, then the middle value is called median
    and is denoted by M
•   Mode : – mode is the value of occurring most frequently in a group of
    items and around which other items are distributed most closely
             Phases in data analysis
1.   Data cleaning :during data cleaning, erroneous entries are
     inspected and corrected where possible.
2. Initial data analysis : it answer for following questions
I. Quality of the data : it can be assessed in different ways such as
      through observations etc
II. Quality of the measurements : it can be checked during initial
      data analysis. One way to assess the quality of a measurement
      instrument is to perform an analysis of homogeneity.
III. Implementation of the design : in many cases, a check to see
      whether the randomization procedure has worked, will be the
      stating point for analyzing the implementation of the design
IV. Characteristics of the data sample : in this step, the findings of the
      initial data analysis are documented and possible corrective
      actions are taken
 Key consideration in data analysis
1. Identify the Purpose of the analysis or project
2. Understand the samples under study
3. Understand the Instruments being used to collect
4. Be Cognizant of data layouts and formats
5. Plan the work and work out the plan
 Components of data analysis plan
1.   Statement of research Questions
2.   Timeline
3.   Budget
4.   File restructuring Procedures
5.   Data cleaning procedures
6.   Quality control procedures at every step in the

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