Transcript Lecture 28
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 (3). (4). Theoretical Framework Problem Definition Variables Identification (5) (6). Scientific Research Design Generation of Hypothesis (2). Preliminary Data Gathering Interviews and Library Search (7). Data Collection and Analysis (8) Deduction (9). (10). (11). Report Writing Report Presentation Managerial Decision Making Introduction to Data Analysis Process Data Analysis Process Interpretation of Results Data Collection Data Analysis Discussion Getting Data Ready for Analysis Feel for Data 1. Mean Editing Data 2. Median 1. Incompleteness /omissions 3. Mode 2. Inconsistencies 5. Frequency Distribution 3. Legibility 4. Coding Data 5. Categorizing 6. Creating a Data File 4. Variance Recommendations Hypotheses Testing Appropriate Statistical Manipulation (Inferential Statistics) Goodness of Data 1. Reliability 2. Validity 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 Bar Chart --- For a Variable Caught on a Nominal Scale Reason n % Relaxation 9 10 Maintain or Improve Fitness 31 34 Lose Weight 33 37 Build Strength 17 19 Total 90 100 35 30 25 20 15 n 10 5 0 Relaxation Fitness Lose Weight Build Strength Frequency Table Showing Reasons of Visiting Gym Next Variable - Gender Gender n % Male 60 67 Female 30 33 Total 90 100 Male Female Next Variable Caught on an Interval Scale I am satisfied by the level of cleanliness in Gym n % Strongly Disagree 4 5 Neither Agree Nor Disagree Agree Disagree 12 13 Neither Agree nor Disagree 12 13 Strongly Aagree Agree 52 58 Strongly Agree 10 11 Total 90 100 Strongly Disagree Disagree 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 = 8.167 Company B 600 Under root = 24.495 All observations fall within 3 standard deviations of mean 40+3*8.167 = 15 – 65 products 40+3*24.495 = 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