Quantitative Research Methods Project 3 Group 4A Valerie Bryan Emily Leak Lori Moore UWG Fall 2011.

Slides:



Advertisements
Similar presentations
Chapter 9: Simple Regression Continued
Advertisements

Class 14 Testing Hypotheses about Means Paired samples 10.3 p
Statistical Analysis Regression & Correlation Psyc 250 Winter, 2013.
By: Jacob Kemble Matt Kelly Taylor Shannon  We realize that alcohol can play a huge role on the performance of a college student. We conducted a survey.
Quantitative Techniques
Abby Owens Sarah Peek Rachael Robinson Joseph Rogers Race and Relations.
Multiple Regression Analysis
LECTURE 3 Introduction to Linear Regression and Correlation Analysis
DATA ANALYSIS I MKT525. Plan of analysis What decision must be made? What are research objectives? What do you have to know to reach those objectives?
Descriptive Statistics Primer
Chapter 13 Analyzing Quantitative data. LEVELS OF MEASUREMENT Nominal Measurement Ordinal Measurement Interval Measurement Ratio Measurement.
Chapter 14 Analyzing Quantitative Data. LEVELS OF MEASUREMENT Nominal Measurement Nominal Measurement Ordinal Measurement Ordinal Measurement Interval.
Topics: Regression Simple Linear Regression: one dependent variable and one independent variable Multiple Regression: one dependent variable and two or.
Lecture 23 Multiple Regression (Sections )
Educational Research by John W. Creswell. Copyright © 2002 by Pearson Education. All rights reserved. Slide 1 Chapter 8 Analyzing and Interpreting Quantitative.
Today Concepts underlying inferential statistics
Statistics for CS 312. Descriptive vs. inferential statistics Descriptive – used to describe an existing population Inferential – used to draw conclusions.
Summary of Quantitative Analysis Neuman and Robson Ch. 11
Simple Linear Regression Analysis
Linear Regression.  Uses correlations  Predicts value of one variable from the value of another  ***computes UKNOWN outcomes from present, known outcomes.
Multiple Regression continued… STAT E-150 Statistical Methods.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 12 Analyzing the Association Between Quantitative Variables: Regression Analysis Section.
Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey All rights reserved. John W. Creswell Educational Research: Planning,
Statistics Definition Methods of organizing and analyzing quantitative data Types Descriptive statistics –Central tendency, variability, etc. Inferential.
Regression. Correlation and regression are closely related in use and in math. Correlation summarizes the relations b/t 2 variables. Regression is used.
Analyzing and Interpreting Quantitative Data
Describing Behavior Chapter 4. Data Analysis Two basic types  Descriptive Summarizes and describes the nature and properties of the data  Inferential.
Statistical Analysis Regression & Correlation Psyc 250 Winter, 2008.
Mapping A Strategy to Attract the Politically Engaged Student to East Evergreen University Consultants: Elizabeth Goff Scott Gravitt Kim Huett Carolyn.
Elementary Statistics Correlation and Regression.
Choosing the Appropriate Statistics Dr. Erin Devers October 17, 2012.
Regression Models Residuals and Diagnosing the Quality of a Model.
Research Questions 1.How do college students who are socially and politically engaged, especially in environmental issues, characterize their political.
EDSI 9961 Project 3 November 13, 2011 Justin Castile Ross Diener Donna Eskut Larke Lanier.
Inferential Statistics Body of statistical computations relevant to making inferences from findings based on sample observations to some larger population.
Research Questions and Hypotheses  How do White males compare to non-White and female peers with respect to their perspectives on race?  H1: There is.
Regression & Correlation. Review: Types of Variables & Steps in Analysis.
Descriptive & Inferential Statistics Adopted from ;Merryellen Towey Schulz, Ph.D. College of Saint Mary EDU 496.
Three Broad Purposes of Quantitative Research 1. Description 2. Theory Testing 3. Theory Generation.
Analyzing and Interpreting Quantitative Data
Appendix B: Statistical Methods. Statistical Methods: Graphing Data Frequency distribution Histogram Frequency polygon.
Chapter 6: Analyzing and Interpreting Quantitative Data
Introducing Communication Research 2e © 2014 SAGE Publications Chapter Seven Generalizing From Research Results: Inferential Statistics.
Regression Analysis. 1. To comprehend the nature of correlation analysis. 2. To understand bivariate regression analysis. 3. To become aware of the coefficient.
1 Correlation and Regression Analysis Lecture 11.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
THE EFFECTS OF ENGLISH SENTENCE PATTERNS AND VOCABULARY MASTERY TOWARDS STUDENT’S DESCRIPTIVE WRITING SKILL OF PRIVATE SENIOR HIGH SCHOOL IN EAST JAKARTA.
Research Methodology Lecture No :26 (Hypothesis Testing – Relationship)
Power Point Slides by Ronald J. Shope in collaboration with John W. Creswell Chapter 7 Analyzing and Interpreting Quantitative Data.
Term Project Math 1040-SU13-Intro to Stats SLCC McGrade-Group 4.
Correlation and Regression Elementary Statistics Larson Farber Chapter 9 Hours of Training Accidents.
Educational Research Inferential Statistics Chapter th Chapter 12- 8th Gay and Airasian.
PSY 325 AID Education Expert/psy325aid.com FOR MORE CLASSES VISIT
Analysis, Interpretation and Reporting Portfolio
Psychology research methods– Analysis Portfolio Taylor Rodgers B
SPSS Port Folio SEAN MCBRIDE B UNIVERSITY OF THE WEST OF SCOTLAND.
NURS 306, Nursing Research Lisa Broughton, MSN, RN, CCRN RESEARCH STATISTICS.
Appendix I A Refresher on some Statistical Terms and Tests.
Correlation and Linear Regression
Lecture 9-I Data Analysis: Bivariate Analysis and Hypothesis Testing
Dr. Siti Nor Binti Yaacob
APPROACHES TO QUANTITATIVE DATA ANALYSIS
Analyzing and Interpreting Quantitative Data
Abby Owens Sarah Peek Rachael Robinson Joseph Rogers
Part Three. Data Analysis
SPSS OUTPUT & INTERPRETATION
SPSS OUTPUT & INTERPRETATION
David Pieper, Ph.D. STATISTICS David Pieper, Ph.D.
Simple Linear Regression
One way ANOVA One way Analysis of Variance (ANOVA) is used to test the significance difference of mean of one dependent variable across more than two.
Presentation transcript:

Quantitative Research Methods Project 3 Group 4A Valerie Bryan Emily Leak Lori Moore UWG Fall 2011

Research Questions & Hypotheses 0 What relationship exists between surveyed students’ self- reported rating for self-confidence and the students’ GPA?  There is no significant correlation between surveyed students’ self-reported rating for self-confidence and the students’ GPA. 0 What relationship exists between surveyed students’ self reported rating for an understanding of others and the students’ GPA?  There is no significant correlation between surveyed students’ self reported rating for an understanding of others and the students’ GPA. 0 What relationship exists between surveyed students’ self reported rating for cooperativeness and the students’ GPA?  There is no significant correlation between surveyed students’ self reported rating for cooperativeness and the students’ GPA.

Descriptive Statistics Under- graduate GPA Self Rating: Cooperativeness Self Rating: Understanding of others Self Rating: Overall Self- Confidence N Valid Missing Mean Median Mode Std. Deviation Skewness Std. Error of Skewness.012 Kurtosis Std. Error of Kurtosis Range Minimum Maximum

Inferential Statistics Coefficients a Model Unstandardized Coefficients Standardized Coefficients tSig. BStd. ErrorBeta 1(Constant) Self Rating: Overall Self- Confidence Self Rating: Understanding of others Self Rating: Cooperativeness a. Dependent Variable: Undergraduate GPA Regression Analysis ANOVA

Results 0 Multiple linear regression analysis was computed for students’ undergraduate GPAs based on self-ratings for self-confidence, understanding of others and cooperativeness. This analysis did not produce a significant regression equation (R 2 < 0.01). However, it revealed a statistically significant variance explained by self- confidence levels (B = 0.09), with no significant variances explained by understanding of others or cooperativeness. 0 The data was further analyzed by computing a one-way ANOVA comparing college students’ undergraduate GPA by self-rated reports of self-confidence levels. A significant difference was found for different levels of self-confidence ratings (F(8, 39379) = 67.54, p < 0.01). Tukey’s HSD was used to determine the nature of the differences for students’ reporting different levels of self- confidence. The analysis revealed that students reporting the highest level of self-confidence had significantly higher GPAs (M = 4.49, SD =.97) than students reporting the lowest level of self- confidence (M = 3.79, SD = 1.40).

Findings Based on the results of statistical analysis… 0 we accept the following hypotheses:  There is no significant correlation between surveyed students’ self reported rating for an understanding of others and the students’ GPA.  There is no significant correlation between surveyed students’ self reported rating for cooperativeness and the students’ GPA. 0 we reject the following null hypotheses:  There is no significant correlation between surveyed students’ self-reported rating for self-confidence and the students’ GPA. 0 and for this case we accept the alternative hypothesis:  There is a significant correlation between surveyed students’ self-reported rating for self-confidence and the students’ GPA.

Implications and Recommendations 0 These results suggest that colleges should accept applicants exhibiting higher levels of self-confidence if it is the school’s goal to enroll students that are likely to be strong academic performers. 0 Further research may be conducted to explore the effect on students’ GPAs resulting from introducing self-confidence boosting programs.