INTRODUCTION TO ONE-WAY ANALYSIS OF VARIANCE Dale Berger‚ Claremont Graduate University http://wise.cgu.edu The purpose of this paper is to explain the logic and vocabulary of one-way analysis of variance (ANOVA). The null hypothesis tested by one-way ANOVA is that two or more population means are equal. The question is whether (H0) the population means may equal for all groups and that the observed differences in sample means are due to random sampling variation‚ or (Ha) the observed differences
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Example B: Raw Score Method You may wish to test the effects of a number of experimental treatments (counselling approaches): group counselling‚ peer counselling and individual counselling on the self-concepts of students. In this case‚ the independent variable‚ counselling approach has three levels. Necessarily there should be three groups randomly selected from the school population which will be exposed to three different counselling approaches. The dependent variable‚ self-concept‚ may be
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ANOVA Test Paper This week Team C is looking to further our knowledge of hypothesis tests by testing for variances and simultaneously comparing the different means of gasoline to conclude if the populations sampled were equal or not. We will test whether the three sample are from populations with equal variances. This type of testing is called analysis of variance or ANOVA (Lind‚ Marchal‚ & Wathen‚ 2004). The ANOVA test can be conducted with the intent of giving families information on where the
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One-way repeated measure ANOVA In a one-way repeated measures ANOVA design‚each subject is exposed to two or more different conditions‚ or measured on the continuous scale onthree or more occasions. It can also be used to compare respondents’ responses to two or more questions or items. These questions‚ hiwever‚ must be meausred using the same scale.( Likert scale) Example of research question: Is there a change in confidence scores over the three time periods? What you need: One group of participants
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ANOVA Hypothesis Test ANOVA Hypothesis Test Living near a major city can be a positive aspect of being a homeowner or someone who uses real estate as an investment. Increasing population contributes to land and space diminishing‚ resulting in high demand for what is available. Industry and markets are in the city‚ attracting buyers who want to have the convenience of living near commercial properties. The difference in the pay scale between jobs in the city and jobs in the suburbs could
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"Applying ANOVA and Nonparametric tests" simulation‚ I realize there were a few things to take into consideration when analyzing a problem. This particular exercise wanted to know the differences and causes of the variation. In order to resolved the solution‚ an individual or whoever is conducting the analysis will need to know what type of test to used‚ decide if the null hypothesis should be rejected or not‚ and make recommendation based on the collected data. Due to two parameters‚ ANOVA and the
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Applying ANOVA and Nonparametric test In the simulation‚ I selected the Kruskal-Wallis test which is used when it is difficult to meet all of the assumptions of ANOVA. The Kruskal-Wallis test is a nonparametric alternative to one way ANOVA. This test is used to compare three or more samples‚ to test the null hypothesis that the different samples in the comparison drawn from the same distribution or from distributions with the same median. Interpretation of the Kruskal-Wallis test is basically similar
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| Analyzing with ANOVA | Two-Way | | | 1/23/2013 | | Submit your answers to the following questions using the ANOVA source table below. The table depicts a two-way ANOVA in which gender has two groups (male and female)‚ marital status has three groups (married‚ single never married‚ divorced)‚ and the means refer to happiness scores (n = 100): a. What is/are the independent variable(s)? What is/are the dependent variable(s)? The independent variables are gender and marital status
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Two-Way ANOVA Two-way ANOVA is similar to one-way ANOVA in all aspects except that in this case additional independent variable is introduced. Each independent variable includes two or more variants. Take the Demonstration problem 11.3 on page number 437 of KEN BLACK Feed the data as shown below: And the values as: 1 1 3.47 1 2 3.43 1 3 3.44 1 4 3.46 1 5 3.46 1 6 3.44 2 1 3.40 2 2 3.41 2 3 3.41 2 4 3.45 2 5 3.40 2 6 3.43 3 1 3.38 3 2 3.42 3 3 3.43 3 4 3.40 3 5 3.39 3
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26 3.04 2.8 3.75 3.64 3.65 3.18 3.44 3.51 2.81 3.64 2.85 3.56 2.92 3.35 3.46 3.59 3.65 2.97 3.21 3.65 2.94 3.53 3.65 3.61 3.7 2.91 3.77 3.79 3.59 3.38 3.57 2.97 3.44 3.48 2.99 3.73 2.91 3.78 3.13 3.14 SUMMARY Groups Unemployed Part-time Full-time ANOVA Source of Variation Treatment Error Total Null hypothesis: Alternate hypothesis: Significance level: p-Value Decision: Count 25 45 130 0 0 Sum 82.110 152.050 450.130 0.000 0.000 Average 3.284 3.379 3.463 Variance 0.110 0.085 0.091 SS 0.771 18
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