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Research methodology - Analysis of Data

The document outlines data processing and analysis methods used in management research, detailing the steps of data editing, coding, classification, tabulation, and graphical representation. It emphasizes the importance of accurate data handling using software like MS Excel and SPSS, and explains various methods of statistical analysis including univariate, bivariate, and multivariate techniques. Additionally, it covers inferential analysis, highlighting key parametric and non-parametric tests for hypothesis testing.

Economy & Finance◦
Research Methods in Management Module IV : Analysis of Data
Processing & Analysis of Data  After the collection of data from primary or secondary sources, arrangement is done so that the same may be analyzed & interpreted with the help of statistical tools.  Software packages used:  MS Excel  SPSS (Software Packages for Social Sciences)  Google Docs etc.
Processing the data Editing Field Editing Central Editing Coding Classification Tabulation Graphing
Data editing  Data editing is a process by which collected data is examined to detect any errors or omissions and further these are corrected as much as possible before proceeding further.  Editing is of two types: 1. Field Editing 2. Central Editing.
Data editing FIELD EDITING:  This is a type of editing that relates to abbreviated or illegible written form of gathered data. Such editing is more effective when done on same day or the very next day after the interview. The investigator must not jump to conclusion while doing field editing. CENTRAL EDITING:  Such type of editing relates to the time when all data collection process has been completed. Here a single or common editor corrects the errors like entry in the wrong place, entry in wrong unit etc. As a rule all the wrong answers should be dropped from the final results.
Benefits of data editing The data obtained is complete in all respects. It is accurate in terms of information recorded and responses sought. The response format is in the form that was instructed. The data is structured in a manner that entering the information will not be a problem.
Data coding  The process of identifying and denoting a numeral to the responses given by the respondent is called coding
Data coding example  Sample record: Excel sheet for two-wheeler owners Unit Column 1 occupation Column 2 Vehicle Column 3 Km/day Column 4 Marital status Column 5 Family size Column 6 1 4 1 20 1 3 2 3 2 25 2 1 3 5 1 25 1 4 4 2 1 15 2 2 5 4 2 20 2 4 6 5 2 35 2 6 7 1 1 40 1 3 8 5 2 20 2 4
Classification of data  Classification of the data implies that the collected raw data is categorized into common group having common feature.  Data having common characteristics are placed in a common group.  The entire data collected is categorized into various groups or classes, which convey a meaning to the researcher.  Classification is done in two ways: 1. Classification according to attributes. 2. Classification according to the class intervals.
CLASSIFICATION ACCORDING THE ATTRIBUTES  Here the data is classified on the basis of common characteristics that can be descriptive like literacy, sex, honesty, marital status etc.  Descriptive features are qualitative in nature and cannot be measured quantitatively but are kindly considered while making an analysis.
CLASSIFICATION ON THE BASIS OF INTERVAL  The numerical feature of data can be measured quantitatively and analyzed with the help of some statistical unit like the data relating to income, production, age, weight etc. come under this category. This type of data is known as statistics of variables and the data is classified by way of intervals.
TABULATION of data  Tabulation is an orderly arrangement of data in rows and columns.  Tabulation summarizes the raw data and displays data in form of some statistical tables.
Types of tables  Vertical Tables  Horizontal Tables
Graphing of data  Visual representation of data  Data are presented as absolute numbers or percentages  The most informative are simple and self-explanatory
Bar chart  In a bar chart, a bar shows each category, the length of which represents the amount, frequency or percentage of values falling into a category. How Do You Spend the Holidays? 45% 38% 5% 5% 7% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% At home w ith family Travel to visit family Vacation Catching up on w ork Other
Pie chart  The pie chart is a circle broken up into slices that represent categories. The size of each slice of the pie varies according to the percentage in each category. How Do You Spend the Holiday's 45% 38% 5% 5% 7% At home with family Travel to visit family Vacation Catching up on work Other
Line graph  Displays trends over time Number of Clinicians Working in Each Clinic During Years 1–4 0 1 2 3 4 5 6 Year 1 Year 2 Year 3 Year 4 Numberofclinicians Clinic 1 Clinic 2 Clinic 3
histogram  A graph of the data in a frequency distribution is called a histogram. Histogram: Daily High Temperature 0 1 2 3 4 5 6 7 5 15 25 35 45 55 More Frequency
Polygon / ogive  A percentage polygon is formed by having the midpoint of each class represent the data in that class and then connecting the sequence of midpoints at their respective class percentages. Frequency Polygon: Daily High Temperature 0 1 2 3 4 5 6 7 5 15 25 35 45 55 More Frequency
Analysis of Data  Analysis means computation of certain indices or measures along with searching for patterns of relationships that exists among the data groups.
Analysis of Data Descriptive & Causal Analysis Inferential or Statistical Analysis Uni-Variate Analysis Bivariate Analysis Multi Variate Analysis Estimation of Parameter Values Testing Hypothesis Point Estimate Interval Estimate Parametric Tests Non -Parametri c Tests
Descriptive Analysis  The study of distribution of variables is termed as a descriptive analysis. If we are studying one variable then it will be termed as a uni-variate analysis, in the case of two variables bi-variate analysis & multi-variate analysis in the case of three & more then three variables
Uni-Variate Analysis  Frequency tables  Diagrams:  Bar charts  Pie charts  Histograms  Measures of central tendency:  Arithmetic mean  Median  Mode  Measures of dispersion:  Range  Mean deviation  Standard deviation Univariate analysis refers to the analysis of one variable at a time. The commonest approaches are as follows:
Bivariate Analysis  Bivariate analysis is concerned with the analysis of two variables at a time in order to uncover whether the two variables are related  Main types: 1. Simple Correlation 2. Simple Regression 3. Two-Way ANOVA
Multi-Variate Analysis  Mutivariate analysis entails the simultaneous analysis of three or more variables  Main Types 1. Multiple Correlation 2. Multiple Regression 3. Multi- ANOVA
Causal Analysis  Causal analysis is concerned with the study of how one or more variables affect changes in another variables
Inferential Analysis  Inferential analysis is concerned with the testing the hypothesis and estimating the population values based on the sample values.
PARAMETRIC TESTS  These tests depends upon assumptions typically that the population(s) from which data are randomly sampled have a normal distribution. Types of parametric tests are: 1. t- test 2. z- test 3. F- test 4. χ2- test
Non parametric Test Do Not Involve Population Parameters Example: Probability Distributions, Independence Data Measured on Any Scale (Ratio or Interval, Ordinal or Nominal)
presented by Dr. Dharmesh Motwani. Visit us online at www.TheStockker.com