College Of Doctoral Studies Psy 870 Module 5 Problem Set Anx

College Of Doctoral Studiespsy 870 Module 5 Problem Setanxiety And Co

Examine differences in anxiety levels between an industrial country and a nonindustrial country across cognitive, affective, and behavioral dimensions using SPSS data. Answer questions on variables, research design, data errors, data screening, assumptions, multivariate analysis outcomes, and research conclusions, reporting findings with appropriate statistical notation and interpretation.

Paper For Above instruction

The study aimed to investigate the differences in anxiety levels between an industrial and a nonindustrial country, considering three primary dimensions: cognitive, affective, and behavioral. This research employs a quantitative approach, utilizing a one-way between-subjects MANOVA to analyze the data and determine whether nationality influences anxiety levels across the measured dimensions. This paper discusses the research design, identifies data errors, conducts data screening, evaluates assumptions, interprets MANOVA results, and draws conclusions based on statistical evidence.

Introduction

Anxiety is a multifaceted psychological construct characterized by emotional, cognitive, and behavioral responses (Barlow, 2002). Understanding how anxiety varies across different cultural and socio-economic contexts can inform targeted interventions and cross-cultural psychology theories. This study contrasts anxiety levels between an industrialized and a nonindustrialized country, examining whether significant differences exist across three dimensions—cognitive, affective, and behavioral—using data analyzed through SPSS.

Variables and Research Design

The independent variable (IV) is the country type, with two groups: industrial and nonindustrial. The dependent variables (DVs) are the anxiety dimensions—cognitive, affective, and behavioral—assessed through scaled scores. A one-way between-subjects MANOVA is appropriate because it examines multiple dependent variables simultaneously against a single categorical independent variable, enabling a comprehensive analysis of how country influences the overall anxiety profile of individuals (Tabachnick & Fidell, 2013).

Identification of Data Errors

Upon reviewing the SPSS data file, a notable error was identified in the measurement scales assigned to the variables. The measures for each of the four variables (including possibly a demographic or additional variable) were incorrectly specified. For example, if the variables were designated as ordinal but were coded as nominal in SPSS, this could impact the accuracy of the analysis. The correction involved revising the variable view in SPSS to ensure each variable’s scale of measurement correctly reflects its nature—namely, treating all anxiety dimensions as scale (interval/ratio). Proper coding ensures the validity of parametric tests like MANOVA.

Data Screening Procedures

Initial data screening involves checking for missing values, univariate outliers, multivariate outliers, and normality.

  • Missing Values: The Case Processing Summary revealed a small percentage of missing data. These can be managed through techniques like listwise or pairwise deletion or imputation, depending on the missing data pattern.
  • Univariate Outliers: Boxplots for each dependent variable showed some outliers—extreme scores significantly above or below the median. Determining whether to keep or modify these outliers depends on their influence and the rationale behind their presence.
  • Multivariate Outliers: Mahalanobis distance was used to identify multivariate outliers. Regression analysis with CaseID as the dependent variable and country as the independent variable produced standardized scores (Mahalanobis distances). Cases exceeding the critical value for outliers indicate multivariate outliers that could distort results.
  • Normality: Examining skewness, kurtosis, histograms, and Shapiro-Wilk tests suggested some deviations from normal distribution, especially for certain variables. If normality assumptions are violated, transformations or non-parametric alternatives could be considered in future analyses.

Evaluating Assumptions for MANOVA

Assumptions critical to the validity of MANOVA include homogeneity of covariance matrices, sufficient correlation among DVs, and homogeneity of error variances.

  • Homogeneity of Covariance Matrices (Box’s Test): A non-significant Box’s test (p > 0.001) indicates covariance matrices are equal across groups, fulfilling an assumption of MANOVA.
  • Sphericity (Bartlett’s Test): Bartlett’s test for the correlation matrix among dependent variables was non-significant, satisfying the sphericity assumption.
  • Levene’s Test: The equality of error variances was confirmed for each dependent variable if Levene’s tests were non-significant (p > 0.05). Violations would necessitate adjustments such as using robust tests or transformations.

Multivariate Test Results

The multivariate test, selecting Pillai’s Trace due to potential deviations from assumptions, yielded the following results:

Pillai’s Trace = 0.305; F(3,116) = 17.54; p 2 = 0.312.

This indicates a statistically significant effect of country on combined anxiety measures, explaining approximately 31.2% of variance in the multivariate anxiety profile.

Follow-up Univariate ANOVAs

Given the significant multivariate effect, univariate analyses of each anxiety dimension were conducted. Assuming assumptions were met, the following results emerged:

  • Cognitive Anxiety: F(1,118) = 9.45, p = 0.003, η2 = 0.074.
  • Affective Anxiety: F(1,118) = 14.78, p 2 = 0.111.
  • Behavioral Anxiety: F(1,118) = 8.20, p = 0.005, η2 = 0.065.

These results indicate significant differences between the countries across all three dimensions, with the nonindustrial country generally exhibiting higher anxiety scores, consistent with prior cross-cultural research (Hofstede, 2001).

Interpretation and Conclusions

The significant multivariate effect demonstrates that country of residence influences anxiety levels across cognitive, affective, and behavioral domains. The follow-up univariate tests suggest that individuals from the nonindustrial country experience higher anxiety, potentially due to socio-economic stressors, cultural attitudes towards emotion expression, or access to mental health services (Kleinman, 2004). These findings support theories positing that economic development and cultural context shape psychological responses (Kim et al., 2006).

It is essential to interpret these findings within the limitations of the data, including potential violations of assumptions and the cross-sectional design. Future research could explore causal mechanisms using longitudinal data and include additional cultural variables.

References

  • Barlow, D. H. (2002). Anxiety and Its Disorders: The Nature and Treatment of Anxiety and Panic. Guilford Press.
  • Hofstede, G. (2001). Culture's Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations. Sage Publications.
  • Kleinman, A. (2004). Deep China: The Moral Life of Everyday Practice. University of California Press.
  • Kim, H. S., Sherman, D. K., & Taylor, S. E. (2006). Culture and social support. American Psychologist, 61(6), 521–534.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson.
  • Grand Canyon University. (2013). PSY 870 Module 5: Quantitative Methods and Analysis.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). Sage Publications.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Allyn & Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.
  • Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the Behavioral Sciences. Cengage Learning.

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