This is the basic explanation on what are ANCOVA and MANCOVA in research study in which provides the definitions and the illustration on how can these both be use in SPSS tool analysis. If you's like to get practice file, do not hesitate to contact me.
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
A measurable characteristic that varies and may change from group to group, person to person, or even within one person over time.
Variable is a logical grouping of attributes, characteristics or qualities that describe an object. It may be either height, weight, anxiety levels, body temperature, income and so on.
Variable is frequently used in quantitative research projects pertinent to define and identify variables.
A variable incites excitement in any research than constants as it facilitate accurate explanation of relationship between the variables.
This presentation explains the concept of ANOVA, ANCOVA, MANOVA and MANCOVA. This presentation also deals about the procedure to do the ANOVA, ANCOVA and MANOVA with the use of SPSS.
A measurable characteristic that varies and may change from group to group, person to person, or even within one person over time.
Variable is a logical grouping of attributes, characteristics or qualities that describe an object. It may be either height, weight, anxiety levels, body temperature, income and so on.
Variable is frequently used in quantitative research projects pertinent to define and identify variables.
A variable incites excitement in any research than constants as it facilitate accurate explanation of relationship between the variables.
Topic: Variance
Student Name: Sonia Khan
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Analysis of variance (ANOVA) everything you need to knowStat Analytica
Most of the students may struggle with the analysis of variance (ANOVA). Here in this presentation you can clear all your doubts in analysis of variance with suitable examples.
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Follo.docxaman341480
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Following ANOVA
Analysis of variance (ANOVA)
is a statistical procedure that compares data between two or more groups or conditions to investigate the presence of differences between those groups on some continuous dependent variable (see
Exercise 18
). In this exercise, we will focus on the
one-way ANOVA
, which involves testing one independent variable and one dependent variable (as opposed to other types of ANOVAs, such as factorial ANOVAs that incorporate multiple independent variables).
Why ANOVA and not a
t
-test? Remember that a
t
-test is formulated to compare two sets of data or two groups at one time (see
Exercise 23
for guidance on selecting appropriate statistics). Thus, data generated from a clinical trial that involves four experimental groups, Treatment 1, Treatment 2, Treatments 1 and 2 combined, and a Control, would require 6
t
-tests. Consequently, the chance of making a Type I error (alpha error) increases substantially (or is inflated) because so many computations are being performed. Specifically, the chance of making a Type I error is the number of comparisons multiplied by the alpha level. Thus, ANOVA is the recommended statistical technique for examining differences between more than two groups (
Zar, 2010
).
ANOVA is a procedure that culminates in a statistic called the
F
statistic. It is this value that is compared against an
F
distribution (see
Appendix C
) in order to determine whether the groups significantly differ from one another on the dependent variable. The formulas for ANOVA actually compute two estimates of variance: One estimate represents differences between the groups/conditions, and the other estimate represents differences among (within) the data.
Research Designs Appropriate for the One-Way ANOVA
Research designs that may utilize the one-way ANOVA include the randomized experimental, quasi-experimental, and comparative designs (
Gliner, Morgan, & Leech, 2009
). The independent variable (the “grouping” variable for the ANOVA) may be active or attributional. An active independent variable refers to an intervention, treatment, or program. An attributional independent variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. The ANOVA can compare two groups or more. In the case of a two-group design, the researcher can either select an independent samples
t
-test or a one-way ANOVA to answer the research question. The results will always yield the same conclusion, regardless of which test is computed; however, when examining differences between more than two groups, the one-way ANOVA is the preferred statistical test.
Example 1: A researcher conducts a randomized experimental study wherein she randomizes participants to receive a high-dosage weight loss pill, a low-dosage weight loss pill, or a placebo. She assesses the number of pounds lost from baseline to post-treatment
378
for the thre ...
Topic: Variance
Student Name: Sonia Khan
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Analysis of variance (ANOVA) everything you need to knowStat Analytica
Most of the students may struggle with the analysis of variance (ANOVA). Here in this presentation you can clear all your doubts in analysis of variance with suitable examples.
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Follo.docxaman341480
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Following ANOVA
Analysis of variance (ANOVA)
is a statistical procedure that compares data between two or more groups or conditions to investigate the presence of differences between those groups on some continuous dependent variable (see
Exercise 18
). In this exercise, we will focus on the
one-way ANOVA
, which involves testing one independent variable and one dependent variable (as opposed to other types of ANOVAs, such as factorial ANOVAs that incorporate multiple independent variables).
Why ANOVA and not a
t
-test? Remember that a
t
-test is formulated to compare two sets of data or two groups at one time (see
Exercise 23
for guidance on selecting appropriate statistics). Thus, data generated from a clinical trial that involves four experimental groups, Treatment 1, Treatment 2, Treatments 1 and 2 combined, and a Control, would require 6
t
-tests. Consequently, the chance of making a Type I error (alpha error) increases substantially (or is inflated) because so many computations are being performed. Specifically, the chance of making a Type I error is the number of comparisons multiplied by the alpha level. Thus, ANOVA is the recommended statistical technique for examining differences between more than two groups (
Zar, 2010
).
ANOVA is a procedure that culminates in a statistic called the
F
statistic. It is this value that is compared against an
F
distribution (see
Appendix C
) in order to determine whether the groups significantly differ from one another on the dependent variable. The formulas for ANOVA actually compute two estimates of variance: One estimate represents differences between the groups/conditions, and the other estimate represents differences among (within) the data.
Research Designs Appropriate for the One-Way ANOVA
Research designs that may utilize the one-way ANOVA include the randomized experimental, quasi-experimental, and comparative designs (
Gliner, Morgan, & Leech, 2009
). The independent variable (the “grouping” variable for the ANOVA) may be active or attributional. An active independent variable refers to an intervention, treatment, or program. An attributional independent variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. The ANOVA can compare two groups or more. In the case of a two-group design, the researcher can either select an independent samples
t
-test or a one-way ANOVA to answer the research question. The results will always yield the same conclusion, regardless of which test is computed; however, when examining differences between more than two groups, the one-way ANOVA is the preferred statistical test.
Example 1: A researcher conducts a randomized experimental study wherein she randomizes participants to receive a high-dosage weight loss pill, a low-dosage weight loss pill, or a placebo. She assesses the number of pounds lost from baseline to post-treatment
378
for the thre ...
How do I do a T test, correlation and ANOVA in SpssSolution .pdfamitseesldh
How do I do a T test, correlation and ANOVA in Spss?
Solution
One-way between-subjects ANOVA A one-way between-subjects ANOVA allows
you to determine if there is a relationship between a categorical independent variable (IV) and a
continuous dependent variable (DV), where each subject is only in one level of the IV. To
determine whether there is a relationship between the IV and the DV, a one-way between-
subjects ANOVA tests whether the means of all of the groups are the same. If there are any
differences among the means, we know that the value of the DV depends on the value of the IV.
The IV in an ANOVA is referred to as a factor, and the different groups composing the IV are
referred to as the levels of the factor. A one-way ANOVA is also sometimes called a single
factor ANOVA. A one-way ANOVA with two groups is analogous to an independent-samples t-
test. The pvalues of the two tests will be the same, and the F statistic from the ANOVA will be
equal to the square of the t statistic from the t-test. To perform a one-way between-subjects
ANOVA in SPSS • Choose Analyze General Linear Model Univariate. • Move the DV to the
Dependent Variable box. • Move the IV to the Fixed Factor(s) box. • Click the OK button. The
output from this analysis will contain the following sections. • Between-Subjects Factors. Lists
how many subjects are in each level of your factor. • Tests of Between-Subjects Effects. The row
next to the name of your factor reports a test of whether there is a significant relationship
between your IV and the DV. A significant F statistic means that at least two group means are
different from each other, indicating the presence of a relationship. You can ask SPSS to provide
you with the means within each level of your between-subjects factor by clicking the Options
button in the variable selection window and moving your withinsubjects variable to the Display
Means For box. This will add a section to your output titled Estimated Marginal Means
containing a table with a row for each level of your factor. The values within each row provide
the mean, standard error of the mean, and the boundaries for a 95% confidence interval around
the mean for observations within that cell. Post-hoc analyses for one-way between-subjects
ANOVA. A significant F statistic tells you that at least two of your means are different from
each other, but does not tell you where the differences may lie. Researchers commonly perform
post-hoc analyses following a significant ANOVA to help them understand the nature of the
relationship between the IV and the DV. The most commonly reported post-hoc tests are (in
order from most to least liberal): LSD (Least Significant Difference test), SNK (Student-
Newman-Keuls), Tukey, and Bonferroni. The more liberal a test is, the more likely it will find a
significant difference between your means, but the more likely it is that this difference is actually
just due to chance. 14 Although it is the most liberal, simulations ha.
Describes the design, assumptions, and interpretations for one-way ANOVA, one-way repeated measures ANOVA, factorial ANOVA, SPANOVA, ANCOVA, and MANOVA. More info: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/ANOVA_II
This presentation discusses in detail about the procedure involved in two-factor MANOVA. Both the analysis i.e. multivariate as well as univariate has been shown in this design by solving an illustration using SPSS software.
This slide is about Analysis of Covariance. Analysis of covariance provides a way of statistically controlling the (linear) effect of variables one does not want to examine in a study.
ANCOVA is the statistical technique that combines regression and ANOVA.
Ducan’s multiple range test - - Dr. Manu Melwin Joy - School of Management St...manumelwin
In 1955, Duncan devised a method to compare each treatment mean with every other treatment mean. The procedure is simple and powerful and has become very popular among researchers, especially in the plant science area.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. “ ANCOVA AND MANCOVA”
PRESENTED BY: PRUM ROTANA (MS.)
DATE: FEB 17TH , 2019
1
The University of Cambodia
College of Education
Workshop on “ SPSS Analysis for MRP and Thesis”
3. Covariance
the mean value of the product of the deviations of two variates
from their respective means.
provides insight into how two variables are related to one
another.
A positive covariance means that the two variables at hand are
positively related, and they move in the same direction.
A negative covariance means that the variables are inversely
related, or that they move in opposite directions.
3
6. ANCOVA
Analysis of covariance (ANCOVA) is used in examining the
differences in the mean values of the dependent variables that are
related to the effect of the controlled independent variables while
taking into account the influence of the uncontrolled independent
variables.
6
7. Example Question of ANCOVA
Does test score effect by the level of Education regarding the
number of hours spent studying?
7
Level of
Education
Number of
Hours Spent
Studying
Test Score
Independent
Variable
Moderating
Variable
8. Why Ancova?
* In experimental designs, to control for factors which
cannot be randomized but which can be measured on an
interval scale.
* In observational designs, to remove the effects of
variables which modify the relationship of the categorical
independents to the interval dependent.
* In regression models, to fit regressions where there are
both categorical and interval independents.
8
9. Practice
Research Question
Does Weight Loss is influenced by Diet controlling by
Height?
DV: Weight Loss
IV: Diet
Moderator: Height
Download: ANCOVA PRACTISE
9
10. ANCOVA IN SPSS 10
Create WEIGTH LOSS Variable
Transform Calculate Variable
15. Interpretation
• Among the 3 types of diet,
Vegetarian Diet is more
effective for Weight Loss.
Mean= 3.153
• Std. is similar between each
type
YET there is an idea that :
15
Height will add more
influence on Weight Loss
16. Tests of Between-Subjects Effects
Dependent Variable: Weight loss
Source
Type III
Sum of
Squares
df Mean
Square F Sig.
Partial
Eta
Squared
Corrected
Model 7.523a 3 2.508 4.165 .012 .258
Intercept
.888 1 .888 1.475 .232 .039
Height
.033 1 .033 .054 .817 .002
Diet
7.118 2 3.559 5.912 .006 .247
Error
21.673 36 .602
Total
303.724 40
Corrected
Total 29.195 39
a. R Squared = .258 (Adjusted R Squared = .196)
• Since p= .006, Diet has
significant effect on weight
loss.
• Moreover, it has moderate
effect on weight loss as
Partial Eta Squared is only
.247= 24.7%.
Note: 0.2 Small effect
0.5 Moderate effect
0.8 Large effect
16
18. Estimated Marginal Means
Diet
Dependent Variable: Weightloss
Diet Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
Atkin Diet 2.658a .221 2.210 3.105
Vegetarian Diet 3.141a .222 2.691 3.590
Zone Diet 2.101a
.207 1.680 2.522
a. Covariates appearing in the model are evaluated at the following values:
Height = 161.98.
• Inserting Height:
Vegetarian Diet is still more effective
than others with Mean= 3.141
• No Height:
Vegetarian Diet proceed others with
Mean= 3.153
Conclusion:
18
The Effect of Height had been removed
or not account for weight loss
20. Research Questions
Do the various school assessments vary by grade level
after controlling for gender?
IV: grade level
DV: various school assessments
Moderator: gender
20
21. Which diseases are better treated, if at all, by either
X drug or Y drug after controlling for length of
disease and participant age?
IV: X drug or Y drug
DV: Diseases
Moderator: length of disease and participant age
21
22. Do the rates of graduation among certain state
universities differ by degree type after controlling
for tuition costs?
IV: Degree type
DV: the rates of graduation
Moderator: Tuition costs
22
23. MANCOVA
Multivariate Analysis of Covariance an extension of
analysis of covariance methods to cover cases where there
is more than one dependent variables and where the
control of concomitant continuous independent variables –
covariates/Moderator – is required.
23
25. Type of MANCOVA
Do gender and the outcome of the final exam
influence the standardized test scores of math,
reading, and writing?
IV: the outcome
DV: test scores (math, reading, and writing)
Moderator: : gender
25
One-way MANCOVA
27. One-way MANCOVA IN SPSS
Research Question
Are the number of working hours and income
influenced by the age group within the level of
education?
Download: MANCOVA File Practice
27
