RES500 Saudi Electronic University Hypothesis Testing & Measures of Association Paper Assignment Questions: Describe the purpose of data analysis. Why rese

RES500 Saudi Electronic University Hypothesis Testing & Measures of Association Paper Assignment Questions: Describe the purpose of data analysis. Why researchers need to interpret their results? What does hypothesis mean in research? Discuss the importance of hypothesis in a research. Discuss the steps involved in hypothesis testing. What are the different types of statistical analysis? Assignment-6
Academic writing and research skills
1st Semester (2019-2020)
Learning Questions of Module-14
Analysis and Presentation of Data – Hypothesis Testing & Measures of Association
Instructions:
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Assignment-6 covers Modules 14
Your assignment is required to be four to five pages in length, which does not include the
title page and reference pages, which are never a part of the content minimum requirements.
Support your submission with course material concepts, principles from the textbook and
at least three scholarly, peer-reviewed journal articles.
Use academic writing standards and follow APA style guidelines.
Assignment Questions:
1. Describe the purpose of data analysis.
2. Why researchers need to interpret their results?
3. What does hypothesis mean in research? Discuss the importance of hypothesis in a
research.
4. Discuss the steps involved in hypothesis testing.
5. What are the different types of statistical analysis?
RES 500 Academic Writing and Research Skills
1
WEEK 14
ANALYSIS AND
PRESENTATION OF DATA HYPOTHESIS TESTING &
MEASURES OF ASSOCIATION
Hypothesis Testing vs. Theory
2
“Don’t confuse “hypothesis” and “theory.”
The former is a possible explanation; the
latter, the correct one. The establishment
of theory is the very purpose of science.”
Hypothesis Testing
3
Inductive
Reasoning
Deductive
Reasoning
Statistical Procedures
4
Inferential
Statistics
Descriptive
Statistics
Hypothesis Testing and the Research Process
5
Approaches to Hypothesis Testing
6
Classical statistics Bayesian statistics
• Objective view of
• Extension of classical
probability
• Established
hypothesis is rejected
or fails to be rejected
• Analysis based on
sample data
approach
• Analysis based on
sample data
• Also considers
established subjective
probability estimates
Types of Hypotheses
7
Null
H0:  = 50 mpg
 H0:  < 50 mpg  H0:  > 50 mpg

Alternate
HA:  = 50 mpg
 HA:  > 50 mpg
 HA:  < 50 mpg  Two-Tailed Test of Significance 8 One-Tailed Test of Significance 9 Statistical Decisions 10 Critical Values 11 Factors Affecting Probability of Committing a  Error 12 True value of parameter Alpha level selected One or two-tailed test used Sample standard deviation Sample size Statistical Testing Procedures 13 State null hypothesis Interpret the test Obtain critical test value Stages Compute difference value Choose statistical test Select level of significance Tests of Significance 14 Parametric Nonparametric How to Select a Test 15 How many samples are involved? If two or more samples: are the individual cases independent or related? Is the measurement scale nominal, ordinal, interval, or ratio? Parametric Tests 16 Z-test t-test One-Sample t-Test Example 17 Null Statistical test Significance level Calculated value Critical test value Ho: = 50 mpg t-test .05, n=100 1.786 1.66 (from Appendix C, Exhibit C-2) One Sample Chi-Square Test Example 18 Intend to Join Number Interviewed Percent (no. interviewed/200) Expected Frequencies (percent x 60) Dorm/fraternity 16 90 45 27 Apartment/rooming house, nearby 13 40 20 12 Apartment/rooming house, distant 16 40 20 12 Living Arrangement Live at home Total 15 30 15 _____ _____ _____ _____ 9 60 200 100 60 Two-Sample Parametric Tests 19 k-Independent-Samples Tests: ANOVA 20 Tests the null hypothesis that the means of three or more populations are equal One-way: Uses a single-factor, fixed-effects model to compare the effects of a treatment or factor on a continuous dependent variable ANOVA Example 21 __________________________________________Model Summary_________________________________________ d.f. Sum of Squares Mean Square F Value p Value Model (airline) 2 11644.033 5822.017 28.304 0.0001 Residual (error) 57 11724.550 205.694 59 23368.583 Source Total _______________________Means Table________________________ Count Mean Std. Dev. Std. Error Lufthansa 20 38.950 14.006 3.132 Malaysia Airlines 20 58.900 15.089 3.374 Cathay Pacific 20 72.900 13.902 3.108 All data are hypothetical 22 Measures of Association Measures of Association: Interval/Ratio Data 23 Pearson correlation coefficient For continuous linearly related variables Correlation ratio (eta) For nonlinear data or relating a main effect to a continuous dependent variable Biserial One continuous and one dichotomous variable with an underlying normal distribution Partial correlation Three variables; relating two with the third’s effect taken out Multiple correlation Three variables; relating one variable with two others Bivariate linear regression Predicting one variable from another’s scores 18-23 Measures of Association: Ordinal Data 24 Gamma Based on concordant-discordant pairs; proportional reduction in error (PRE) interpretation Kendall’s tau b P-Q based; adjustment for tied ranks Kendall’s tau c P-Q based; adjustment for table dimensions Somers’s d P-Q based; asymmetrical extension of gamma Spearman’s rho Product moment correlation for ranked data Measures of Association: Nominal Data 25 Phi Chi-square based for 2*2 tables Cramer’s V CS based; adjustment when one table dimension >2
Contingency coefficient C
CS based; flexible data and distribution
assumptions
Lambda
PRE based interpretation
Goodman & Kruskal’s tau
PRE based with table marginals
emphasis
Uncertainty coefficient
Useful for multidimensional tables
Kappa
Agreement measure
Pearson’s Product Moment Correlation r
26
Is there a relationship between X and Y?
What is the magnitude of the relationship?
What is the direction of the relationship?
Scatterplots of Relationships
27
Diagram of Common Variance
28
Interpretation of Correlations
29
X causes Y
Y causes X
X and Y are activated by one
or more other variables
X and Y influence each
other reciprocally
Comparison of Bivariate Linear Correlation
and Regression
30
Testing Goodness of Fit
31
Y is completely unrelated to X
and no systematic pattern is evident
There are constant values of
Y for every value of X
The data are related but
represented by a nonlinear function
Components of Variation
32
References
33
Business Research Methods by Donald R Cooper,
12th Edition.
1.

Chapter 17 – Hypothesis Testing, PP 428-465

Chapter 18 – Measurement of Association, PP 466-501

Appendix c – Nonparametric Significance Tests, PP 612-618

Appendix d – Selected statistical Tables, PP 619-629

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