Change in schedule… Website currently says…  August 5 th – first draft  August 19 th – second draft Lets have instead…  August 19 th – first draft.

Slides:



Advertisements
Similar presentations
How to calculate: - Average Effect Size (step 4) - Moderators (step 5)
Advertisements

Analysis of Variance The contents in this chapter are from Chapter 15 and Chapter 16 of the textbook. One-Way Analysis of Variance Multiple Comparisons.
Control Charts  Control Charts allow a company’s performance over time to be analyzed by combining performance data, average, range and standard deviation.
Chapter 5 Time Series Analysis
Statistics: Data Analysis and Presentation Fr Clinic II.
Measures of Center and Variation
Correlation A correlation exists between two variables when one of them is related to the other in some way. A scatterplot is a graph in which the paired.
Social Research Methods
Students in a Grade 7 class measured their pulse rates. Here are their results in beats per minute. 97, 69, 83, 66, 78, 8, 55, 82, 47, 52, 67, 76, 84,
Descriptive Statistics  Summarizing, Simplifying  Useful for comprehending data, and thus making meaningful interpretations, particularly in medium to.
The Data Analysis Plan. The Overall Data Analysis Plan Purpose: To tell a story. To construct a coherent narrative that explains findings, argues against.
Overview of Meta-Analytic Data Analysis
CHAPTER 20: Total Quality Management to accompany Introduction to Business Statistics fourth edition, by Ronald M. Weiers Presentation by Priscilla Chaffe-Stengel.
Answering questions about life with statistics ! The results of many investigations in biology are collected as numbers known as _____________________.
Think of a topic to study Review the previous literature and research Develop research questions and hypotheses Specify how to measure the variables in.
Descriptive Statistics  Summarizing, Simplifying  Useful for comprehending data, and thus making meaningful interpretations, particularly in medium to.
Daniel Acuña Outline What is it? Statistical significance, sample size, hypothesis support and publication Evidence for publication bias: Due.
Biostatistics: Measures of Central Tendency and Variance in Medical Laboratory Settings Module 5 1.
Basic Meta-Analyses Transformations Adjustments Outliers The Inverse Variance Weight Fixed v. Random Effect Models The Mean Effect Size and Associated.
Rationale / value of using statistics statistics is a powerful tool to objectively compare experimental data uncover relationships among variables experience.
How to calculate: - Average Effect Size (step 4) - Moderators (step 5)
CHAPTER 7: Exploring Data: Part I Review
How to Evaluate the Effects of Potential Bias in Meta-analysis in R.
Lab 5 instruction.  a collection of statistical methods to compare several groups according to their means on a quantitative response variable  Two-Way.
The Campbell Collaborationwww.campbellcollaboration.org C2 Training: May 9 – 10, 2011 Introduction to meta-analysis.
DAP 1- Read, create, & interpret graphs when appropriate DAP 3- Analyze a set of data by using & comparing combinations of measures of central tendancy.
Correlation & Regression Correlation does not specify which variable is the IV & which is the DV.  Simply states that two variables are correlated. Hr:There.
Essential Statistics Chapter 11 Picturing Distributions with Graphs.
Statistical Analysis. Variability of data All living things vary, even two peas in the same pod, so how do we measure this variation? We plot data usually.
Analysis Overheads1 Analyzing Heterogeneous Distributions: Multiple Regression Analysis Analog to the ANOVA is restricted to a single categorical between.
APA Results Section Results.
Sampling distributions rule of thumb…. Some important points about sample distributions… If we obtain a sample that meets the rules of thumb, then…
Lecture 5 EPSY 642 Victor Willson Fall EFFECT SIZE DISTRIBUTION Hypothesis: All effects come from the same distribution What does this look like.
CHAPTER 1 Picturing Distributions with Graphs BPS - 5TH ED. CHAPTER 1 1.
An article on peanut butter reported the following scores (quality ratings on a scale of 0 to 100) for various brands. Construct a comparative stem-and-leaf.
Why do we analyze data?  It is important to analyze data because you need to determine the extent to which the hypothesized relationship does or does.
Chapter 4 Exploring Chemical Analysis, Harris
Why do we analyze data?  To determine the extent to which the hypothesized relationship does or does not exist.  You need to find both the central tendency.
Biostatistics Regression and Correlation Methods Class #10 April 4, 2000.
Term Project Math 1040-SU13-Intro to Stats SLCC McGrade-Group 4.
Wim Van den Noortgate Katholieke Universiteit Leuven, Belgium Belgian Campbell Group Workshop systematic reviews.
Insert name of presentation on Master Slide HCAI Charts HCAI Information for Action, November 2010 Presenter: Mari Morgan, Wendy Harrison.
Week Seven.  The systematic and rigorous integration and synthesis of evidence is a cornerstone of EBP  Impossible to develop “best practice” guidelines,
Midterm Review IN CLASS. Chapter 1: The Art and Science of Data 1.Recognize individuals and variables in a statistical study. 2.Distinguish between categorical.
5-Analyzing trends in categorical data Dot plots and frequency tables 1-Ways to represent data Data 4.
EXPLORATORY DATA ANALYSIS and DESCRIPTIVE STATISTICS
Chapter 24 Comparing Means.
Testing for moderators
X AND R CHART EXAMPLE IN-CLASS EXERCISE
Lecture 4: Meta-analysis
Social Research Methods
Gerald - P&R Chapter 7 (to 217) and TEXT Chapters 15 & 16
Effect of Measurement Error on SPC
AP Statistics Day 5 Objective: Students will be able to understand and calculate variances and standard deviations.
McLeod 2007.
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Quantitative Data Who? Cans of cola. What? Weight (g) of contents.
Fleminger.
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Warm up Honors Algebra 2 3/14/19
Chapter 1: Exploring Data
Multiplying Impact Team
Meta-analysis in R: An introductory guide
Presentation transcript:

Change in schedule… Website currently says…  August 5 th – first draft  August 19 th – second draft Lets have instead…  August 19 th – first draft

(5) Other calculations and tables/graphs

Overall Strategy (1) Average ES  In-text: Average, range, total number Heterogeneity Fail-safe N Unweighted (and difference test to weighted) Outliers (and difference test to weighted after removing outliers)  Charts/Tables: Descending order Stem-and-leaf Funnel Plot Boxplot

(1) Average ES: in-text The average weighted effect size was.1221 (CI =.1139,.1302, z = 29.07, p <.001). The range of effect sizes is.78 to -.61 across 296 total effect sizes. The heterogeneity test for the weighted effect size was significant (Qw (293) = , p <.001), indicating that there was substantial variation within the weighted effect sizes.

Table: Descending order of ES

Chart: Stem-and-leaf

(1) Average ES: in-text A fail-safe N was calculated to ascertain the number of new, unpublished, or unretrieved studies required to reduce the significance of this averaged effect size to non-signifcance (Rosenthal, 1991), fail-safe N = 108,195. page for Rosenthal, 1991

(1) Average ES: in-text A fail-safe N can also be calculated to ascertain the number of new, unpublished, or unretrieved studies required to reduce this averaged effect size to a specific level (Lipsey & Wilson, 2001). To reduce the averaged effect size to a specified level of.1, the fail-safe N = 65, which indicates that it would take an additional 65 studies with an effect size of 0 to reduce the current meta- analyzed effect size of.1221 to.1. To reduce the average effect size to a specified level of.05, the fail-safe N = 424. To reduce the average effect size all the way to 0, the fail-safe N = 358,680. Page 166 of Lipsey/Wilson

(1) Average ES: in-text Unweighted  “The unweighted effect size average is.1451 (CI =.1339,.1563, z = 25.14, p <.001). “ Difference Test to Weighted  “The test of the differences between the two dependent effect sizes was non-significant, z =.41, p =.69. In other words, the weighted effect size was not influenced by particular sample sizes that were extremely large or small. “ 

Chart: Funnel Plot

(1) Average ES: in-text Outlier analysis  “Outlier analysis determines the existence of extreme effect sizes, as compared to the analysis above which tested the influence of extreme sample sizes. Chart 3 shows the boxplot for the weighted effect sizes.”  “Eliminating the outliers produces a weighted effect size of.1137 (CI =.1054,.1219, z = 26.89, p <.001).” Difference test to weighted after removing outliers  “The test of the differences between the weighted effect sizes with and without the outliers was non-significant, z =.15, p =.88. Thus, the weighted effect size was not significantly influenced by outliers.”

Chart: Boxplot

Overall Strategy (2) Moderators  In-text: Interpreting the data and comparing/contrasting  Charts/Tables: ES of Moderators Categorical Moderator Data Continuous Moderator Data 95% Error Bar Chart Multivariate Data

Table: Groupings of the ES

Table: Moderators

Chart: Error bars (95% CI)

Table: Multivariate