Basics of Data Analysis

Basics of Data Analysis

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  Basics Of DataAnalysis Presented By: Ankur Jain Swati Biraj Choudhary Abhijeet Prateek Rajpal
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  Data Analysis • Turningraw data into useful information. • Purpose is to provide answers to questions being asked at a program site or research questions. • Even the greatest amount and best quality data mean nothing if not properly analyzed—or if not analyzed at all. • Analysis is looking at the data in light of the questions you need to answer: – How would you analyze data to determine: “Is my program/research meeting its objectives?”
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  Answering Programmatic Questions • Question:Is my program meeting its objectives? • Analysis: Compare program targets and actual program performance to learn how far you are from target. • Interpretation: Why you have or have not achieved the target and what this means for your program. • May require more information.
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  Data Preparation Process Preparepreliminary plan of data analysis  Check questionnaires  Edit  Code  Transcribe  Clean data  Select a data analysis strategy
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  Types of StatisticalAnalyses Used in Marketing Research • Data summarization: the process of describing a data matrix by computing a small number of measures that characterize the data set. • Four functions of data summarization: – Summarizes the data – Applies understandable conceptualizations – Communicates underlying patterns – Generalizes sample findings to the population
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  Coding • Coding –process of translating information gathered from questionnaires or other sources into something that can be analyzed. • Involves assigning a value to the information given— often value is given a label. • Coding can make data more consistent: – Example: Question = Sex – Answers = Male, Female, M, or F – Coding will avoid inconsistencies
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  Coding System • Commoncoding systems (code and label) for variables: – 0=No 1=Yes (1 = value assigned, Yes= label of value) – OR: 1=No 2=Yes • When you assign a value you must also make it clear what that value means. – In first example above, 1=Yes but in second example 1=No – As long as it is clear how the data are coded, either is fine • You can make it clear by creating a data dictionary to accompany the dataset.
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  Coding: Dummy Variable •A “dummy” variable is any variable that is coded to have 2 levels (yes/no, male/female, etc.) • Dummy variables may be used to represent more complicated variables – Example: No. of cigarettes smoked per week-- answers total 75 different responses ranging from 0 cigarettes to 3 packs per week. – Can be recoded as a dummy variable: 1=smokes (at all) 0=non-smoker • This type of coding is useful in later stages of analysis.
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   Attaching Labelsto values: • Many analysis software packages allow you to attach a label to the variable values Example: Label 0’s as male and 1’s as female • Makes reading data output easier: Without label: Variable SEX Frequency Percent 0 21 60% 1 14 40% With label: Variable SEX Frequency Percent Male 21 60% Female 14 40%
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  Coding – OriginalVariables • Coding process is similar with other categorical variables. • Example: Variable EDUCATION, possible coding: 0 = Did not graduate from high school 1 = High school graduate 2 = Some college or post-high school education 3 = College graduate • Could be coded in reverse order (0=college graduate, 3=did not graduate high school). • For this ordinal categorical variable we want to be consistent with numbering because the value of the code assigned has significance.
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  • Example ofbad coding: 0 = Some college or post-high school education 1 = High school graduate 2 = College graduate 3 = Did not graduate from high school • Data has an inherent order but coding does not follow that order—NOT appropriate coding for an ordinal categorical variable.
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  Basic Terminology and Concepts •Statistical terms – Ratio – Mean – Median – Mode – Frequency Distribution – Standard Deviation
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  Conclusion • Purpose ofanalysis is to provide answers to programmatic questions. • Data analysis describe the sample/target population. • Analysis of a data is a process of inspecting, cleaning, transforming and modeling data with a goal of highlighting useful information, suggesting conclusion and supporting decision making.
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  Thank You