Need Someone Who Knows How To Use SPSS Software And Statisti
Need Someone That Knows How Use Spss Software And Statisticsanxiety A
Need someone that knows how use SPSS Software And Statistics. Anxiety and Country This study examined differences in anxiety level between an industrial country and a nonindustrial country. Anxiety is assessed three ways—cognitive, affective, and behavioral—with higher scores indicating higher levels of the anxiety dimensions. Directions: . NOTE: Helpful hints are provided here for you to use while answering these questions.
1. What is the ONE independent variable in this study? What are the dependent variables? 2. Why is a one-way between-subjects MANOVA appropriate to use for this research design?
HINT: Consider the number of IVs and the number of DVs for your answer. 3. Did you find any errors that the researcher made when setting up the SPSS data file (don’t forget to check the variable view)? If so, what did you find? How did you correct it?
HINT: YES! The Measures (for scale of measurement) is wrong for each of the 4 variables! You need to indicate what was wrong and what should be the correct measures. 4. Perform Initial Data Screening.
What did you find regarding missing values, univariate outliers, multivariate outliers, normality? Although you are not asked to make adjustments, what should you consider when you find these kinds of outcomes? HINTS: • For missing values, see Case Processing Summary • Univariate outliers: inspect boxplots • Multivariate outliers: Don’t forget to create the Case ID variable to do this analysis. Then, perform a regression analysis with CaseID as the DV and Country as the IV in order to compute the Mahalanobis distance measures. Be sure to click Save when you are setting up the regression so the regression scores will be saved to a new variable (automatically named MAH_1). Then, Explore MAH_1 scores, remembering to check the “Outliers” box that is found with Plots. This will give you information about multivariate outliers. • Normality: examine the skewness and kurtosis values for each dependent variable; examine the histograms; examine the Shapiro-Wilks’ results. 5. Perform a one-way between-subjects MANOVA on the data. Before interpreting the results of the MANOVA, check outcomes that test other assumptions for this statistic: equality of covariance matrices (see Box’s Test) and sufficient correlation among the DVs (see Bartlett’s Test of Sphericity). Also check the results of the Levene’s Test of Equality of Error Variances to evaluate that assumption for the univariate ANOVAs that are run and show in the Tests of Between-Subjects Effects output.
What have you found about whether the data meet these additional assumptions for the MANOVA and followup ANOVAs? HINTS: • Be sure to read the instructions very carefully in the textbook for what to check to get these results for these tests of assumptions (e.g., you have to check Residual SSCP matrix within Options to get the results of the Bartlett’s Test of Sphericity). • Be sure to review what a statistically significant outcome means for each test: in some cases, it means a violation, but in others it means an assumption is met. 6. What is the outcome of the multivariate tests (which looks at the effects of the IV on all three DVs at the same time)? Given results of your tests for homogeneity of variance-covariance matrices for the dependent variables, is it more appropriate to use Wilks’ lambda or Pillai’s trace to interpret outcomes, or does it make a difference?
Report either the Pillai’s Trace or Wilks’ Lambda for your results, as well as the associated F value and its statistical significance. Use the following format for notation: Pillai’s Trace OR Wilks’ lambda = ____; F(df, df) = ____, p = ____, η²= _____. What does this information tell you about the difference between the two countries on the linear combination (the variate) of the dependent variables? HINT: Here, and ONLY for a one-way MANOVA with only two groups for the IV, eta squared and partial eta squared are the same value; you can use the value given for partial eta squared in the SPSS results of the Multivariate Tests to be eta squared, η², and save the step of hand calculating η². 7. Given the results of the multivariate tests, is it OK now to move on to interpret the results of the Tests of Between-Subjects Tests? Why? Explain. If yes, what are the results and what do they mean? (Report each of the results using the format of F(df, df) = _____, p = _____ , η² = _____ for each DV.) 8. Citing the results of your statistical analyses, what is the conclusion you can draw (and support) regarding research question that was posed in this research (see problem statement)?
HINT: Use the sample results write-up in the textbook to see what you should report and how to say it. Just substitute the correct language and values for the analyses you have done for this problem.
Paper For Above instruction
Understanding and executing multivariate analysis of variance (MANOVA) is crucial in research contexts where multiple dependent variables are assessed simultaneously across different groups. This paper addresses a hypothetical study comparing anxiety levels between an industrial and a nonindustrial country, utilizing SPSS software to analyze the data systematically. The discussion will cover the identification of independent and dependent variables, the appropriateness of the MANOVA, data preparation and screening, assumption testing, interpretation of multivariate and univariate results, and conclusions drawn from the statistical analyses.
Identification of Variables and Research Design
The independent variable in this research is 'Country,' with two levels: industrial and nonindustrial. The dependent variables are the three dimensions of anxiety: cognitive, affective, and behavioral. Each of these is scored to reflect the participant's anxiety levels in each domain, with higher scores indicating greater anxiety.
A one-way between-subjects MANOVA is appropriate for this design because it involves a single independent variable with two levels and multiple dependent variables. This statistical technique accounts for the correlation among dependent variables, controls the overall Type I error rate, and enables the researcher to evaluate the collective effect of 'Country' on all three anxiety measures simultaneously. As noted by Tabachnick and Fidell (2013), this approach is well-suited for exploring whether differences in anxiety dimensions exist between the two country groups.
Data Preparation and Errors in Variable Setup
An initial review of the SPSS data file reveals common errors in the variable view, particularly in the 'Measure' column. The error likely involved assigning ordinal or nominal labels to variables that are continuous scales of measurement. Correcting this involves setting the 'Measure' attribute to 'Scale' for all anxiety-related variables, reflecting their continuous nature, which is essential for parametric testing assumptions.
Initial Data Screening
Data screening procedures involve examining missing values, univariate outliers, multivariate outliers, and distribution normality. The 'Case Processing Summary' indicates whether any missing data exists; if found, potential remedies include imputation or case deletion, depending on the extent.
Univariate outliers are detected through boxplots; significant deviations suggest the need to consider transformations or robust statistical methods. Multivariate outliers are assessed using Mahalanobis distance, with a regression analysis performed in SPSS to generate Mahalanobis scores (MAH_1). Values exceeding the critical chi-square value denote multivariate outliers, which can influence the MANOVA results if unaddressed.
Normality is assessed via skewness and kurtosis statistics, histograms, and the Shapiro-Wilks test. Violations of normality can impact the validity of MANOVA; transformations such as log or square root may be necessary, and the researcher should consider the robustness of MANOVA to mild violations.
Assumption Testing for MANOVA
Before conducting the MANOVA, it is essential to test assumptions including homogeneity of covariance matrices with Box’s M test, and the equality of variances among groups using Levene’s test for each dependent variable. Bartlett’s test of sphericity checks whether the DVs are sufficiently correlated for multivariate analysis. Violations—indicated by significant tests—may require using more robust multivariate tests or data transformations.
Multivariate Test Results
Results such as Pillai’s Trace or Wilks’ Lambda indicate whether the independent variable significantly affects the combined dependent variables. For a study with two groups, Wilks’ Lambda is commonly used; a significant multivariate test suggests the groups differ overall in their anxiety profiles. The reported statistic includes the test value, F statistic, degrees of freedom, p-value, and eta squared (η²), which quantifies the effect size.
Follow-up Univariate Analyses
Given significant multivariate results and assumptions met, follow-up univariate ANOVAs are interpreted. These reveal whether 'Country' influences each specific anxiety dimension. The results, reported with F values, degrees of freedom, p-values, and effect sizes, provide detailed insights into which aspects of anxiety differ between countries.
Conclusions and Implications
Based on the statistical outputs, the researcher concludes whether differences in anxiety levels exist between the two countries. For example, if the multivariate test was significant, and univariate tests showed higher cognitive and affective anxiety in the nonindustrial country, interventions could be tailored accordingly. These findings inform mental health policy and cross-cultural psychology, emphasizing the importance of context-specific assessments of anxiety.
References
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