How To Choose The Correct Statistical Test Procedure

1in Order To Choose The Correct Statistical Testprocedure It Is N

In order to choose the correct statistical test/procedure, it is necessary for investigators to take into account all of the following except: hypotheses, type of variables, distribution of variables, p-values of the test, study design, none of the above.

Choose the appropriate statistical procedure based on the analysis scenario:

  • How cholesterol (mg/dL) varies with weight (lbs), annual income ($), and height (inches): Multiple linear regression
  • Whether a diagnosis of cancer is associated with gender and age (years): Multiple logistic regression
  • How systolic blood pressure (mmHg) varies with education level (less than high school, high school graduate, some college, bachelor’s degree, grad school): ANOVA
  • How cortisol (mcg/dL) varies with gender, weight (lbs), and income ($): Multiple linear regression

If an experiment is conducted with 5 conditions and 6 subjects in each condition, what are degrees of freedom numerator (dfn) and degrees of freedom error (dfe)?

Provide the analysis of variance summary table for a hypothetical study on the effects of age and time on scores on a reading comprehension test, based on the data provided.

Determine whether the ADHD treatment case study design is between-subjects or within-subjects; then create an ANOVA summary table based on the dataset which contains four scores per subject.

The ADHD case study investigates the effects of stimulant medication on cognitive functioning in children with mental retardation and ADHD. It measures the delay of gratification (DOG) task after various doses of methylphenidate (MPH). The study employs a repeated-measures design because each participant performed the task after each dosage, allowing for within-subject analysis of treatment effects.

Furthermore, a researcher seeks to assess whether the average viewing times of a news station are the same across different groups, assuming normal distribution and equal population standard deviations. Conduct hypothesis testing at a significance level of 0.05 for this comparison.

Paper For Above instruction

The process of selecting the appropriate statistical test is fundamental to ensuring valid and reliable research outcomes. Investigators must carefully consider various factors prior to analysis, including hypotheses, the types of variables involved, their distributions, the study design, and p-values. Notably, the choice of statistical procedure hinges on the specific data characteristics and research questions.

For instance, when examining how multiple continuous variables relate, such as cholesterol levels in relation to weight, income, and height, multiple linear regression is typically appropriate. This method allows for the assessment of the combined effect of several predictors on a continuous outcome, providing insight into their individual contributions while controlling for others (Tabachnick & Fidell, 2013). Conversely, if the goal is to analyze whether a categorical outcome, such as cancer diagnosis, is associated with predictors like gender and age, multiple logistic regression is the standard approach (Hosmer, Lemeshow, & Sturdivant, 2013).

When comparing means across groups characterized by different categories, such as education level and systolic blood pressure, analysis of variance (ANOVA) is generally suitable. It tests for significant differences among group means and is effective when the independent variable is categorical with multiple levels (Field, 2013). Further, in cases where the independent variables include both categorical and continuous predictors affecting a continuous outcome, analysis of covariance (ANCOVA) might be employed, combining features of ANOVA and regression (Miller & Han, 2009).

Determining degrees of freedom in experimental design is equally vital. For a study with 5 conditions and 6 subjects in each, the degrees of freedom numerator (dfn) corresponds to the number of groups minus one, i.e., 4, while the error degrees of freedom (dfe) is the total number of observations minus the number of groups, i.e., (5*6) – 5 = 25. These parameters are essential for constructing ANOVA tables and conducting significance tests.

The hypothetical study on reading comprehension involves analyzing how age and time influence scores. An analysis of variance summary table would typically involve factors for age and time, with calculations for main effects and interaction effects, and associated degrees of freedom, mean squares, F-statistics, and p-values (Gelman & Hill, 2007). Such a table provides a structured summary of the variance attributable to each factor and their interactions, facilitating hypothesis testing.

In the ADHD treatment case study, the design is within-subjects because each child underwent multiple dosages, and their responses were measured repeatedly. This repeated-measures design helps control for individual variability and provides more sensitive detection of treatment effects (Tabachnick & Fidell, 2013). An ANOVA summary table for this data would include within-subject factors (dose levels), degrees of freedom, sum of squares, mean squares, F-statistics, and p-values, allowing for assessment of whether different dosages significantly impacted cognitive functioning.

Lastly, comparing the mean viewing times across different groups involves performing an ANOVA at a specified significance level. If the F-test indicates significance, it suggests that at least one group mean differs from others, warranting further post hoc tests. The assumptions of normality and equal variances are crucial for the validity of these tests (Field, 2013).

Overall, selecting the correct statistical procedure depends on aligning the analysis method with the type of data and the research question. Proper understanding and application of these tests ensure meaningful interpretation of results, ultimately advancing scientific knowledge in various fields.

References

  • Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  • Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons.
  • Miller, M. W., & Han, Y. (2009). Analysis of covariance (ANCOVA): An application to social science research. Journal of Social Science Research, 4(1), 35–50.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.

Related Assignments