1 / 20

Quantitative data analysis Lecture 6

Quantitative data analysis Lecture 6. E 45 Johan Brink, IIE 24 November. Agenda. Chapter 14 Univariate analysis Bivariate analysis Multivariate analysis Contingency Pearson’s correlation t-test Chi square Factor & cluster analysis. Univariate analysis. One variable at a time

keelie-hays
Download Presentation

Quantitative data analysis Lecture 6

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Quantitative data analysisLecture 6 E 45 Johan Brink, IIE 24 November

  2. Agenda Chapter 14 Univariate analysis Bivariate analysis Multivariate analysis Contingency Pearson’s correlation t-test Chi square Factor & cluster analysis

  3. Univariate analysis One variable at a time • Frequency tables – Bar charts • Grouping of ration & interval variables: 20-29, 30-39… - Histograms Arithmetic mean = Sum of all values/ # Values =33.6 Median = Mid point of distribution of values Mode =the most frequent value in the distribution

  4. Dispersion Range: Min to Max (7 & 3) Variance =standard deviation² s²= Σ (x-M)²/ (n-1) = 1,5 ->Standard deviation =1,22 Variance = 12/(9-1) =1,5

  5. Multivariate analysis Spurious correlation Relationship between two variables are caused by a thirds, underlying factor Intervening variable Chain of relationships Moderating variable The relationship between A & B only exist if C is percent C A B A B C C A B

  6. Analyzing data Correlation & relationship Between variables (questions, groups, items) Does the answers on question 1 correlate with answers on question 2? - Different questions/items for the same construct or does it capture a relationship Test – significant differences Between variables -Questions, groups items -Across time/treatments Is the mean different enough given the standard deviations? t-test ( Chi-squared (nominal scales) Differences from the expected value?

  7. Constructs & Items Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 • Cronbach α is a measure of how well variables measures the same underlying phenomena Construct 1 Construct 2 Item Question 1 Item Question 2 Item Question 3 Item Question 4 Item Question 5 Item Question 6 Org. Culture Performance

  8. Hypothesis testing H0 There is no difference between group A and group B H1 There is a difference between group A and B H0 There is no connection between variable X and Y H1 There is a connection between variable X and Y In order to reduce the risk of type 1 error, by increasing the level of significance from 5% to 1%,the risk of committing type 2 error increases!

  9. Bivariate analysis

  10. Contingency tables

  11. Pearson's correlation For interval/ratio variables Measure of the strength of association between two variables r = Between -1 and +1, 0= no correlation & 1= perfect correlation r²*100% = Variation caused /explained R=0,969

  12. Pearson's correlation

  13. Pearson's correlation

  14. Pearson's correlation

  15. t-test: A statistical test to see if there is a difference between two samples n1=25 n2=24 Mean1=64 Mean2=56 S1=10 S2=8 Df=n1+n2-2=47 t= (Mean1-Mean2)/√[((n1-1)s1²+ (n2-1)s2²)/(n1+n2-2))*(1/n1+1/n2))] t= (641-56)/√[((25-1)10²+ (24-1)8²)/(25+24-2))*(1/25+1/24))] =3,08 Statistic table t(df=47, 0,05)=2,012 3,08>2,012 thus reject H0, there is a difference! Hypothesis: H0, the true means is equal Alternative, H1, there is a difference

  16. Chi square Is there a difference between age groups (young, middle and old) and preference for A or B? • Nominal scales! • 40 respondents Chi²= Σ (observed-expected)²/expected

  17. Chi square

  18. Chi square

  19. Chi square Chi²= (20-15,625)²/15,625+ (5-6,25)²/6,25+ (0-3,125)²/3,125+ (5-9,375)²/9,375+ (5-3,75)²/3,75+ (5-1,875)²/1,875=12,27 Df= (r-1)*(c-1)= 2-1*3-1=2 Chi²0,05, df=2 =>5,99 12,27> 5,99 Thus reject H0, there is a difference between A and B and age group.

  20. Factor analysis and Cluster analysis • Reduce the data • Variables which measures the same thing • Underlying factors

More Related