PHAR 6123 Exam 2 Chicago State University
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Free PHAR 6123 Exam 2 Chicago State University Questions
Which of these statistical tests would be the most appropriate to compare the mean A1C levels between an exercise group and a usual care group in a study?
- Independent t-test
- Chi-Square Test
- Survival Analysis
- Kruskal-Wallis test
Explanation
Explanation:
Correct Answer: (A) Independent t-test
An independent t-test is the most appropriate test for comparing the means of a continuous variable (A1C levels) between two independent groups. A1C is a continuous numerical variable and there are exactly two groups being compared, making the independent t-test the correct choice.
B. Chi-Square Test — Used for comparing categorical data, not continuous variables like A1C levels. C. Survival Analysis — Used to analyze time-to-event data such as time to death or disease recurrence, not for comparing means. D. Kruskal-Wallis test — A non-parametric alternative used when comparing three or more groups or when data is not normally distributed. With two groups and continuous data, the independent t-test is preferred.
In a journal club literature presentation, the discussion section should include:
- Connect study's findings to theory or previous research
- Note weaknesses and strength of the study
- Outline future directions and implications
- All of the above
Explanation
Explanation:
Correct Answer: (D) All of the above
A thorough discussion section in a journal club presentation should connect findings to existing theory and prior research to contextualize results, critically evaluate both the strengths and weaknesses of the study design and methodology, and identify future directions and clinical or research implications of the findings.
Tertiary references include only studies or original report of data published in biomedical journals.
- True
- False
Explanation
Explanation:
Correct Answer: (B) False
Tertiary references are compiled, synthesized sources of information such as textbooks, drug compendia (e.g., Drug Facts and Comparisons, Micromedex), encyclopedias, and clinical guidelines. They do not contain original research data. It is primary references (original research articles) that contain original reports of data published in biomedical journals. Tertiary references summarize and consolidate information from both primary and secondary sources for quick reference use.
What does degree of freedom (df) mean in statistics?
- Difference between mean and variance
- Number of independent values that can vary in a calculation
- Total number of observations in a dataset
- Number of samples collected
Explanation
Correct Answer: B) Number of independent values that can vary in a calculation
Degrees of freedom (df) refers to the number of independent values or scores in a dataset that are free to vary when estimating a statistical parameter. When a constraint is placed on a dataset — such as fixing the mean — one value becomes determined by all the others, reducing the number of values that can freely vary by one.
This concept is fundamental in many statistical tests including the t-test, chi-square test, and ANOVA, where the degrees of freedom determine which distribution is used and how critical values are looked up. It is not the total number of observations (that is simply n), nor is it the difference between mean and variance.
Which of the following best defines a confounder in a regression analysis?
- A variable that explains all variation in the dependent variable
- A variable that increases the effect between two other variables
- A factor that distorts the relationship between the independent and dependent variable if not accounted for
- A random error that has no impact on the outcome
Explanation
Explanation:
Correct Answer: (C) A factor that distorts the relationship between the independent and dependent variable if not accounted for
A confounder is a third variable that is associated with both the independent and dependent variables, distorting or masking the true relationship between them if not properly controlled for in the analysis. Failing to account for confounders can lead to biased and misleading results.
A. A variable that explains all variation in the dependent variable — No single variable typically explains all variation; this does not define a confounder. B. A variable that increases the effect between two other variables — This describes an effect modifier or interaction variable, not a confounder. D. A random error that has no impact on the outcome — Random error is a separate statistical concept unrelated to confounding.
The term "power" in power analysis refers to:
- The probability of making a Type I error
- The ability to avoid all statistical errors
- The probability of correctly rejecting a false null hypothesis
- The strength of the correlation between variables
Explanation
Explanation:
Correct Answer: (C) The probability of correctly rejecting a false null hypothesis
Statistical power is defined as 1 - β, representing the probability of correctly detecting a true effect — that is, rejecting the null hypothesis when it is indeed false. Higher power reduces the risk of a Type II error (missing a real effect).
A. The probability of making a Type I error — This describes the significance level (α), not power. B. The ability to avoid all statistical errors — Power specifically addresses Type II errors, not all statistical errors. D. The strength of the correlation between variables — This describes a correlation coefficient, not statistical power.
A patient satisfaction survey uses the following responses: Strongly agree, Agree, Disagree, Strongly disagree. This variable is best classified as:
- Nominal
- Interval
- Ordinal
- Continuous
Explanation
Correct Answer: C) Ordinal
The responses Strongly agree, Agree, Disagree, and Strongly disagree represent an ordinal level of measurement because the categories have a meaningful rank or order, but the exact distance between each category is not equal or precisely measurable. Nominal data has categories with no inherent order, such as blood type or gender.
Interval data has ordered categories with equal and measurable intervals between values, such as temperature in Celsius. Continuous data can take any value within a range and is measured on an uninterrupted scale, such as weight or blood pressure. Likert-scale responses like this survey are the classic example of ordinal data.
If a variable changes the strength or direction of the relationship between an independent and dependent variable, it is called a:
- Confounder
- Mediator
- Residual
- Moderator (effect modifier)
Explanation
Explanation:
Correct Answer: (D) Moderator (effect modifier)
A moderator, also known as an effect modifier, is a variable that changes the strength or direction of the association between an independent and dependent variable across different subgroups or levels. It represents a statistical interaction between the modifier and the independent variable in the model.
A. Confounder — A confounder distorts the apparent relationship between an independent and dependent variable but does not modify the direction or strength of the relationship across subgroups. B. Mediator — A mediator explains the pathway or mechanism through which an independent variable influences a dependent variable, rather than altering the strength or direction of their relationship. C. Residual — A residual is the difference between an observed and predicted value in a regression model, not a variable that modifies relationships.
An observational study that surveys exposure and disease status at a single point in time is:
- Cross-sectional study
- Cohort study
- Case-control study
- None of the above
Explanation
Correct Answer: A) Cross-sectional study
A cross-sectional study is an observational study design that measures both the exposure and the outcome (disease status) simultaneously at a single point in time within a defined population. It provides a snapshot of the population and is commonly used to determine the prevalence of a condition.
Because exposure and outcome are measured at the same time, it cannot establish temporal sequence or causality. A cohort study follows participants over time based on exposure status. A case-control study starts with the disease outcome and looks backward at exposure. Neither of these designs measures exposure and disease status at a single point in time.
Chi-square test of homogeneity in research studies is used to:
- Compare the means of two independent groups
- Test the relationship between two continuous variables
- Compare proportions between two categorical groups
- Evaluate differences in median values across multiple groups
Explanation
Explanation:
Correct Answer: (C) Compare proportions between two categorical groups
The chi-square test of homogeneity is used to determine whether the distribution or proportions of a categorical variable are the same across two or more independent groups. It assesses whether different populations share the same distribution of a categorical outcome.
A. Compare the means of two independent groups — Comparing means is performed using an independent t-test or ANOVA, not a chi-square test. B. Test the relationship between two continuous variables — Relationships between continuous variables are assessed using correlation or linear regression, not chi-square. D. Evaluate differences in median values across multiple groups — Differences in medians across groups are assessed using non-parametric tests such as the Kruskal-Wallis test, not the chi-square test of homogeneity.
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