Research Tools and Techniques The Research Process: Step 7 (Data Analysis Part A) Lecture 28

Lecture Topics Covered Previously in the Last Lecture Non-Probability Sampling Techniques What Should be an Ideal Sample Size Introduction to Data Analysis Process

What we are going to Cover in this Lecture Introduction to Descriptive Statistics Measures of Central Tendency Measures of Dispersion

THE RESEARCH PROCESS (1). Observation The Broad Problem Area (2). Preliminary Data Gathering Interviews and Library Search (3). Problem Definition (4). Theoretical Framework Variables Identification (5) Generation of Hypothesis (6). Scientific Research Design (7). Data Collection and Analysis (8) Deduction (9). Report Writing (10). Report Presentation (11). Managerial Decision Making

Data Analysis Process Data Collection Data Analysis Getting Data Ready for Analysis Editing Data 1.Incompleteness /omissions 2.Inconsistencies 3.Legibility 4.Coding Data 5.Categorizing 6.Creating a Data File Feel for Data 1.Mean 2.Median 3.Mode 4.Variance 5.Frequency Distribution Goodness of Data 1.Reliability 2.Validity Hypotheses Testing Appropriate Statistical Manipulation (Inferential Statistics) Interpretation of Results Discussion Recommendations Introduction to Data Analysis Process

STATISTICAL DATA ANALYSIS UNIVARIATE ANALYSIS/Descriptive Statistics: The univariate analysis refers to the analysis of one variable at a time. This analysis describes a single variable or phenomena of interest BIVARIATE ANALYSIS/Inferential Statistics: In this statistical analysis, the two variables are analyzed at a time in order to understand whether or not they are related. The hypotheses are tested applying this technique.

Descriptive Statistics Frequencies: Occurrence of number of times of a phenomena ----  %ages Reasonn% Relaxation910 Maintain or Improve Fitness 3134 Lose Weight3337 Build Strength 1719 Total90100 Frequency Table Showing Reasons of Visiting Gym Bar Chart --- For a Variable Caught on a Nominal Scale

Gendern% Male6067 Female3033 Total90100 I am satisfied by the level of cleanliness in Gym n% Strongly Disagree 45 Disagree1213 Neither Agree nor Disagree 1213 Agree5258 Strongly Agree 1011 Total90100 Next Variable - Gender Next Variable Caught on an Interval Scale

Measures of Central Tendency The Mean  Average  We can calculate averages for interval scale and ratio scale data only i.e. average age is 33.6 years or nearly 34 years. The Median  Midpoint  Arrange all values in ascending or descending order and find the midpoint i.e. 31. Inflation or deflation by extreme members is controlled. It can be employed for interval, ratio and ordinal scale variables. Mode  Value occurring most frequently i.e. 28 Can be utilized for all types of variables.

Skew ness  The skew ness of a distribution is measured by comparing the relative positions of the mean, median and mode. Distribution is symmetrical Mean = Median = Mode Distribution skewed right (Right tail longer than left) Median lies between mode and mean, and mode is less than mean Distribution skewed left (Left tail longer than right) Median lies between mode and mean, and mode is greater than mean Kurtosis

Measures of Dispersion (Variability in a set of observations) Range  Extreme Values Difference between the maximum and minimum value i.e. time spent on cv equipment 25 min 50 min Range = 25 min weight machines 10 min 60 min Range = 50 min (It means more variability on the time spent on weight machines)

Variance  Spread of data around mean Formula = (n1-u) 2 +(n2-u) 2 +(n3-u) 2 N Company A Product (Sales) = 30, 40, 50 Company B Product (Sales) = 10, 40, 70 Variance for company A = 66.7 Variance for company B = 600 Standard Deviation  Variance Under Root In our case for Company A 66.7 Under root = Company B 600 Under root = All observations fall within 3 standard deviations of mean 40+3*8.167 = 15 – 65 products 40+3* = 0 – 114 products 90% observations fall within 2 standard deviations of mean >50% observations fall within 1 standard deviation of mean

Summary Introduction to Descriptive Statistics Measures of Central Tendency Measures of Dispersion