Psychology research methods– Analysis Portfolio Taylor Rodgers B

Slides:



Advertisements
Similar presentations
Educational Research: Causal-Comparative Studies
Advertisements

Analysis of variance (ANOVA)-the General Linear Model (GLM)
Chapter Fourteen The Two-Way Analysis of Variance.
LINEAR REGRESSION: Evaluating Regression Models Overview Assumptions for Linear Regression Evaluating a Regression Model.
LINEAR REGRESSION: Evaluating Regression Models. Overview Assumptions for Linear Regression Evaluating a Regression Model.
LINEAR REGRESSION: Evaluating Regression Models. Overview Standard Error of the Estimate Goodness of Fit Coefficient of Determination Regression Coefficients.
Matching level of measurement to statistical procedures
Correlations and T-tests
Analysis of Variance & Multivariate Analysis of Variance
Chapter 14 Inferential Data Analysis
Richard M. Jacobs, OSA, Ph.D.
Relationships Among Variables
Smith/Davis (c) 2005 Prentice Hall Chapter Eight Correlation and Prediction PowerPoint Presentation created by Dr. Susan R. Burns Morningside College.
Understanding the Two-Way Analysis of Variance
Two-Way Analysis of Variance STAT E-150 Statistical Methods.
Chapter 12 Inferential Statistics Gay, Mills, and Airasian
Inferential Statistics
Example of Simple and Multiple Regression
Quantitative Research Methods Project 3 Group 4A Valerie Bryan Emily Leak Lori Moore UWG Fall 2011.
Understanding Research Results
Background Info Opportunity for people to visually see their brainwave patterns through visual and audio cues on the computer Games and activities are.
Chapter 13: Inference in Regression
Basic Statistics Michael Hylin. Scientific Method Start w/ a question Gather information and resources (observe) Form hypothesis Perform experiment and.
Chapter 15 Correlation and Regression
Understanding Statistics
Statistics Definition Methods of organizing and analyzing quantitative data Types Descriptive statistics –Central tendency, variability, etc. Inferential.
بسم الله الرحمن الرحیم.. Multivariate Analysis of Variance.
Chapter 11 HYPOTHESIS TESTING USING THE ONE-WAY ANALYSIS OF VARIANCE.
UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION © 2012 The McGraw-Hill Companies, Inc.
METHODS IN BEHAVIORAL RESEARCH NINTH EDITION PAUL C. COZBY Copyright © 2007 The McGraw-Hill Companies, Inc.
Educational Research: Competencies for Analysis and Application, 9 th edition. Gay, Mills, & Airasian © 2009 Pearson Education, Inc. All rights reserved.
Chapter 10: Analyzing Experimental Data Inferential statistics are used to determine whether the independent variable had an effect on the dependent variance.
Educational Research Chapter 13 Inferential Statistics Gay, Mills, and Airasian 10 th Edition.
ITEC6310 Research Methods in Information Technology Instructor: Prof. Z. Yang Course Website: c6310.htm Office:
Chapter 16 Data Analysis: Testing for Associations.
© 2006 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Chapter 12 Testing for Relationships Tests of linear relationships –Correlation 2 continuous.
Three Broad Purposes of Quantitative Research 1. Description 2. Theory Testing 3. Theory Generation.
ANCOVA. What is Analysis of Covariance? When you think of Ancova, you should think of sequential regression, because really that’s all it is Covariate(s)
Chapter 13 Repeated-Measures and Two-Factor Analysis of Variance
Data Analysis.
NIH and IRB Purpose and Method M.Ed Session 2.
Final Test Information The final test is Monday, April 13 at 8:30 am The final test is Monday, April 13 at 8:30 am GRH102: Last name begins with A - I.
Research Methods and Data Analysis in Psychology Spring 2015 Kyle Stephenson.
Kin 304 Inferential Statistics Probability Level for Acceptance Type I and II Errors One and Two-Tailed tests Critical value of the test statistic “Statistics.
Research Methods and Data Analysis in Psychology Spring 2015 Kyle Stephenson.
© 2006 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Chapter 11 Testing for Differences Differences betweens groups or categories of the independent.
Week of March 23 Partial correlations Semipartial correlations
Educational Research Inferential Statistics Chapter th Chapter 12- 8th Gay and Airasian.
Analysis, Interpretation and Reporting Portfolio
Choosing and using your statistic. Steps of hypothesis testing 1. Establish the null hypothesis, H 0. 2.Establish the alternate hypothesis: H 1. 3.Decide.
Chapter 11: Test for Comparing Group Means: Part I.
SPSS Port Folio SEAN MCBRIDE B UNIVERSITY OF THE WEST OF SCOTLAND.
NURS 306, Nursing Research Lisa Broughton, MSN, RN, CCRN RESEARCH STATISTICS.
Exercises Causal Comparative Research Assoc. Prof. Dr. Şehnaz Şahinkarakaş.
Inferential Statistics Psych 231: Research Methods in Psychology.
CHAPTER 15: THE NUTS AND BOLTS OF USING STATISTICS.
BUS 308 Week 2 Quiz Check this A+ tutorial guideline at 1. How is the sum of squares unlike.
Data measurement, probability and Spearman’s Rho
Chapter 12 Understanding Research Results: Description and Correlation
Dr. Siti Nor Binti Yaacob
Inferential Statistics
Analysis of Covariance (ANCOVA)
Understanding Research Results: Description and Correlation
Kin 304 Inferential Statistics
Formation of relationships Matching Hypothesis
Chapter 9: Differences among Groups
Inferential Statistics
15.1 The Role of Statistics in the Research Process
Understanding Statistical Inferences
Exercises Causal Comparative Research
Presentation transcript:

Psychology research methods– Analysis Portfolio Taylor Rodgers B00257487

Study 1: Data entry Uses a one way between groups design Each participant is subjected to only 1 condition Hypothesis: this study hypothesises that there will be a significant difference in the identification score of those in the Task specific rehearsal (TSR) group when compared to the other techniques. Independent variable: Identification technique group, 3 levels: General mnemonic imagery (GMI), Situation perspective (SP), Task-specific rehearsal (TSR) Dependant variable: identification score A one way between groups ANOVA carried out This statistical test is used to compare differences in means from two or more groups within a single variable design. http://moodle.uws.ac.uk/course/view.php?id=6497 Week 8 – Slide 13

Study 1: Descriptive statistics Highest mean is TSR (83.58), middle mean is GMI (56.58) and the lowest mean is 34.00 for the SP group. The standard deviations suggest that the data spread is fairly steady across each group. These mean differences suggest that the participants in the TSR group may perform significantly better than those in the other conditions. http://moodle.uws.ac.uk/course/view.php?id=6497 Week 8 – Slide 14

Study 1: Inferential analysis One way ANOVA with Tukey Post hoc comparison was conducted. A one way between subjects ANOVA was used to test for differences in identification score between the different techniques of identification (GMI, SP and TSR). There was a significant main effect of identification score across the three groups, F(2,33)= 179.46, p<0.001. Tukey post hoc comparisons of the groups indicate that TSR resulted in a significantly higher identification score than GMI (27, p<0.001) and SP (49.6, p<0.001). GMI was also significantly higher in identification score than SP (22.58, p<0.001). These results suggest that the null hypothesis can be rejected and that task specific rehearsal (TSR) is the most effective memory technique. http://moodle.uws.ac.uk/course/view.php?id=6497 Week 8 – Slides 15-17

Study 2: Data entry Utilises a two way between groups design Each participant is subjected to only 1 condition Hypotheses: 1. There will be a significant main effect of the credibility of the type of witness on the amount of misinformation recalled 2. There will a significant main effect for the type of witness statement on the amount of misinformation recalled. 3. There will be a significant interaction between the credibility of the witness and the statement accuracy on the amount of misinformation recalled. Independent variables: Accuracy, (2 levels: accurate, inaccurate) and credibility, (3 levels: high, low, neutral) Dependent variable: Recall score A 3x2 two way between groups ANOVA was carried out http://moodle.uws.ac.uk/course/view.php?id=6497 Week 9 – Slide 3

Study 2: Descriptive statistics Highest mean score for accurate group is high credibility (88.8), the highest mean score for inaccurate group is neutral credibility (53.6). The lowest mean score for accurate group is neutral credibility (55.7). The lowest mean score for inaccurate group is high credibility (45.9). The standard deviation for the accurate group (2.2) and inaccurate group (3.6) within the neutral credibility condition is lowest when compared with the accuracy of the high and low credibility conditions. The line graph indicates that the difference between the accurate and inaccurate groups for the high credibility condition is significantly higher than the difference between the accurate and inaccurate groups for the low credibility condition. The means for both accurate and inaccurate groups within the neutral credibility condition have the lowest difference when compared. These results suggest that the accuracy of recall scores are heavily influenced by credibility as the neutral credibility condition produced the lowest standard deviation and are therefore more likely to be reliable whereas both high and low credibility groups showed significant variance in accuracy. http://moodle.uws.ac.uk/course/view.php?id=6497 Week 9 – Slide 13

Study 2: Inferential analysis Two Way between subjects ANOVA was conducted A 3x2 two-way between groups ANOVA was conducted to explore the impact of credibility and accuracy on recall scores. Subjects were divided into three credibility groups (high, low, neutral) and were given either an accurate or inaccurate statement, with six experimental conditions in total. The interaction effect between credibility and accuracy were statistically significant, F(2,54) = 66.5, p<0.001. There was a statistically significant main effect for credibility F(2,54) = 25.7, p<0.001 and for accuracy, F(2,54) = 256.6, p<0.001. These results support the hypothesis that there will be a statistically significant main effect of credibility and for accuracy. Also the hypothesis that there will be a significant interaction between accuracy and credibility is supported by these results. http://moodle.uws.ac.uk/course/view.php?id=6497 Week 9 – Slide 15 & 17

Study 3: Data entry Hypothesis: Age, extroversion and number of selfies will be significant correlated, age and extroversion will significantly predict a proportion of the variance in the number of selfies posted. Uses a standard multiple regression technique. Regression is used in this study to test the predictive power of age and extroversion on the number of selfies posted and to investigate the correlation between these three variables. Variables investigated: Age, extroversion score, number of selfies. Independent variables: Age, extroversion score. Dependent variable: Number of selfies. http://moodle.uws.ac.uk/course/view.php?id=6497 Week 10 – Slide 14 & 15

Study 3: Descriptive statistics The scatterplot matrix suggests that a significant positive correlation exists between all three variables of age, extroversion score and selfie count. A weakly significant, positive correlation exists between age and selfie count (R=.234, p>0.05); and a strong significant, positive relationship also exists between extroversion score and selfie count (R=.671, p<0.001). http://moodle.uws.ac.uk/course/view.php?id=6497 Week 10 – Slide 16

Study 3: Inferential analysis Number of selfies was regressed on two independent variables (age, extroversion score) using the enter method. The results were significant F(2,37)= 15.2, p<0.001. Multiple R was found to be 0.672 with adjusted R square = 0.422. This indicated that 42.2% of the variance in number of selfies was accounted for by the two independent variables of age and extroversion score. As you can see from Table 1, age and extroversion score each independently predict number of selfies taken. The results of this study suggest that all three variables of age, extroversion score and number of selfies are significantly correlated, however age cannot be used as a strongly reliable predictor of selfie count. The hypothesis for this study is partially supported. http://moodle.uws.ac.uk/course/view.php?id=6497 Week 10 – Slides 17 & 18 Table 1 Variable Beta t p Age -.039 -.296 .769 Extroversion Score .687 5.177 .000