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Complete an SPSS data analysis report using a one-way ANOVA for assigned variables. USE TEMPLATE PROVIDEDUSE DATA FROM EXCEL TO COMPLETE SPSS REPORTSStep 1: Write Section 1 of the DAAProvide a context of the grades.sav data set.Include a definition of the specified variables (predictor, outcome) and corresponding scales of measurement.Specify the sample size of the data set.Step 2: Write Section 2 of the DAAAnalyze the assumptions of the one-way ANOVA.Paste the SPSS histogram output for quiz3 and discuss your visual interpretations.Paste SPSS descriptives output showing skewness and kurtosis values for quiz3 and interpret them.Paste SPSS output for the Shapiro-Wilk test of quiz3 and interpret it.Report the results of the Levenes test and interpret it.Summarize whether or not the assumptions of the one-way ANOVA are met.Step 3: Write Section 3 of the DAASpecify a research question related to the one-way ANOVA.Articulate the null hypothesis and alternative hypothesis.Specify the alpha level.Step 4: Write Section 4 of the DAABegin by pasting SPSS output of the means plot and providing an interpretation.Also report the means and standard deviations of quiz3 for each level of section.Next, paste the SPSS ANOVA output and report the results of the F test, including: Degrees of freedom.F value.p value.Calculated effect size.Interpretation of the effect size.Finally, if the omnibus F is significant, provide the SPSS post-hoc (Tukey HSD) output. Interpret the post-hoc tests.Step 5: Write Section 5 of the DAADiscuss the conclusions of the one-way ANOVA as it relates to the research question.Conclude with an analysis of the strengths and limitations of one-way ANOVA. template_4.docx resources.doc grades.xlsx Unformatted Attachment Preview Running head: DATA ANALYSIS AND APPLICATION TEMPLATE Data Analysis and Application (DAA) Template 1 DATA ANALYSIS AND APPLICATION TEMPLATE Data Analysis and Application (DAA) Template Use this file for all assignments that require the DAA Template. Although the statistical tests will change from week to week, the basic organization and structure of the DAA remains the same. Update the title of the template. Remove this text and provide a brief introduction. Section 1: Data File Description 1. Describe the context of the data set. You may cite your previous description if the same data set is used from a previous assignment. 2. Specify the variables used in this DAA and the scale of measurement of each variable. 3. Specify sample size (N). Section 2: Testing Assumptions 1. Articulate the assumptions of the statistical test. 2. Paste SPSS output that tests those assumptions and interpret them. Properly integrate SPSS output where appropriate. Do not string all output together at the beginning of the section. 3. Summarize whether or not the assumptions are met. If assumptions are not met, discuss how to ameliorate violations of the assumptions. Section 3: Research Question, Hypotheses, and Alpha Level 1. Articulate a research question relevant to the statistical test. 2. Articulate the null hypothesis and alternative hypothesis. 3. Specify the alpha level. Section 4: Interpretation 1. Paste SPSS output for an inferential statistic. Properly integrate SPSS output where appropriate. Do not string all output together at the beginning of the section. 2. Report the test statistics. 2 DATA ANALYSIS AND APPLICATION TEMPLATE 3. Interpret statistical results against the null hypothesis. Section 5: Conclusion 1. State your conclusions. 2. Analyze strengths and limitations of the statistical test. 3 DATA ANALYSIS AND APPLICATION TEMPLATE References Provide references if necessary. 4 Assessment 4 Context Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero. Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true. In this context you will be studying the details of the first type of test again, with the added capability of comparing the means among more than two group at a time. This is the same type of test of difference between group means. In variations on this model, the groups can actually be the same people under different conditions. The main idea is that several group mean values are being compared. The groups each have an average score or mean on some variable. The null hypothesis is that the difference between all the group means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. One might ask why we would not use multiple t tests in this situation. For instance, with three groups, why would I not compare groups one and two with a t test, then compare groups one and three, and then compare groups two and three? The answer can be found in our basic probability review. We are concerned with the probability of a TYPE I error (rejecting a true null hypothesis). We generally set an alpha level of .05, which is the probability of making a TYPE I error. Now consider what happens when we do three t tests. There is .05 probability of making a TYPE I error on the first test, .05 probability of the same error on the second test, and .05 probability on the third test. What happens is that these errors are essentially additive, in that the chances of at least one TYPE I error among the three tests much greater than .05. It is like the increased probability of drawing an ace from a deck of cards when we can make multiple draws. ANOVA allows us do an overall test of multiple groups to determine if there are any differences among groups within the set. Notice that ANOVA does not tell us which groups among the three groups are different from each other. The primary test in ANOVA is only to determine if there is a significant difference among the groups somewhere. You will study the theory and logic of analysis of variance (ANOVA). Recall that a t-test requires a predictor variable that is dichotomous. The advantage of ANOVA over a t-test is that the categorical predictor variable includes 3+ values (groups). Just like a t-test, the outcome variable in ANOVA is quantitative and requires the calculation of group means. In ANOVA, there are two levels of hypotheses. There is first the overall question of whether all the group means are equal, or if there are some differences among the means somewhere. This is called the omnibus null hypothesis test. The test is designed to show that the probability that the group means are all equal is very low, leading to the researcher being able to legitimately claim there are differences. This is done with the F test. In ANOVA, once the omnibus null 1 hypothesis is rejected, then one may legitimately use special tests, called post hoc tests, to examine each of the pairs of groups in the set to determine which ones differ and which do not. For instance, if an ANOVA is performed for three groups, the omnibus null hypothesis is that the three groups have equal means. If that null is rejected, then the researcher may use special post hoc tests to compare groups 1 & 2, groups 1 & 3, and groups 2 & 3. Each of these post hoc tests are themselves null hypothesis tests, similar to the t tests which were studied previously. They are designed to control for multiple comparisons, or an inflation of the Type I error rate that is a result of doing many tests with some fixed probability of error on each test. Most are based on the assumption that the omnibus null has been rejected. The Logic of a One-Way ANOVA The ANOVA, or F-test, relies on predictor variables referred to as factors. A factor is a categorical (nominal) predictor variable. The term one-way is applied to an ANOVA with only one factor that is defined by two or more mutually exclusive groups. Technically, an ANOVA can be calculated with only two groups, but the t-test is usually used instead. The one-way ANOVA is usually calculated with three or more groups, which are often referred to as levels of the factor. If the ANOVA includes multiple factors, it is referred to as a factorial ANOVA. An ANOVA with two factors is referred to as a two-way ANOVA; an ANOVA with three factors is referred to as a three-way ANOVA, and so on. Factorial ANOVA is studied in Advanced Inferential Statistics. In this course, we will focus on the theory and logic of the one-way ANOVA. ANOVA is one of the most popular statistics used in psychological research. In nonexperimental designs, the one-way ANOVA compares group means across naturally existing characteristics of groups, such as political affiliation. In experimental designs, the one-way ANOVA compares group means for participants randomly assigned to treatment conditions (for example, high caffeine dose; low caffeine dose; control group). Avoiding Inflated Type I Error You may wonder why a one-way ANOVA is necessary. For example, if a factor has four groups ( k = 4), why not just run independent sample t tests for all pairwise comparisons (for example, Group A versus Group B, Group A versus Group C, Group B versus Group C, et cetera)? Warner (2013) points out that a factor with four groups involves six pairwise comparisons. The issue is that conducting multiple pairwise comparisons with the same data leads to inflated risk of a Type I error (incorrectly rejecting a true null hypothesis—getting a false positive). The ANOVA protects the researcher from inflated Type I error by calculating a single omnibus test that assumes all k population means are equal. Although the advantage of the omnibus test is that it helps protect researchers from inflated Type I error, the limitation is that a significant omnibus test does not specify exactly which group means differ, just that there is a difference somewhere among the group means. A researcher therefore relies on either (a) planned contrasts of specific pair wise comparisons determined prior to running the F-test, or, (b) follow-up tests of pair wise comparisons, also referred to as post-hoc tests, to determine exactly which pair wise comparisons are significant. Usually, if planned contrasts are designed correctly, there is no need to perform the omnibus null test, and the overall ANOVA is not necessary. 2 Hypothesis Testing in One-Way ANOVA The null hypothesis of the omnibus test is that all k population means are equal, or H0: µ1 = µ2 =…µk. By contrast, the alternative hypothesis is usually articulated by stipulating that H0 is not true. Keep in mind that this prediction does not imply that all groups must significantly differ from one another on the outcome variable. In fact, for reasons beyond the scope of our present discussion, it is not even strictly necessary that any two groups differ even if the omnibus null is rejected. Assumptions of One-Way ANOVA The assumptions of ANOVA reflect assumptions of the t-test. ANOVA assumes independence of observations. ANOVA assumes that outcome variable Y is normally distributed. ANOVA assumes that the variance of Y scores is equal across all levels (groups) of the factor. These ANOVA assumptions are checked in the same process used to check assumptions for the t-test discussed earlier in the course—using the Shapiro-Wilk test and the Levene test. Effect Size for a One-Way ANOVA The effect size for a one-way ANOVA is eta squared (η2). It represents the amount of variance in Y that is attributable to group differences. Recall the concept of sum of squares ( SS). Eta squared for the one-way ANOVA is calculated by dividing the sum of squares of between-group differences (SS-between) by the total sums of squares in the model (SS-total), which is reported in SPSS output for the F-test. Eta squared for the one-way ANOVA is interpreted with <.02 as small, < .06 as medium, and > .06 as large. References Lane, D. M. (2013). HyperStat online statistics textbook. Retrieved from http://davidmlane.com/hyperstat/index.html Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications. 3 id 106484 108642 127285 132931 140219 142630 153964 154441 157147 164605 164842 167664 175325 192627 211239 219593 237983 245473 249586 260983 273611 280440 287617 289652 302400 307894 337908 354601 378446 380157 390203 392464 414775 417003 419891 420327 434571 436413 447659 463276 466407 467806 473303 479547 490016 498900 lastname firstname gender VILLARRUZ ALFRED VALAZQUEZSCOTT GALVEZ JACKIE OSBORNE ANN GUADIZ VALERIE RANGIFO TANIECE TOMOSAWA DANIEL LIAN JENNY BAKKEN KREG LANGFORD DAWN VALENZUELA NANCY SWARM MARK KHOURY DENNIS MISCHKE ELAINE AUSTIN DERRICK POTTER MICKEY LEE JONATHAN DAYES ROBERT STOLL GLENDON CUSTER JAMES WU VIDYUTH CHANG RENE CUMMINGSDAVENA BRADLEY SHANNON JONES ROBERT TORRENCE GWEN UYEYAMA VICTORINE CARPIO MARY SAUNDERS TAMARA LUTZ WILLIAM SHIMA MIHAELA DOMINGO MONIKA RATANA JASON EVANGELISTNIKKI DE CANIO PAULA BADGER SUZANNA SURI MATHEW PANG SUZANNE GALANVILLEDANA HANSEN TIM PICKERING HEIDI DEVERS GAIL PARK SANDRA LANGFORD 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