QUANT PAPER

Question 1

objectives

Answer 1

clear objective and for these objectives quant was most useful

abstracts suggest a longitudinal study but it is not- cross sectional

Good to use a epidemiological study

population based sample good as it extends to lots of people: 1. Generalizability
Population-based samples ensure that the findings can be generalized to the entire population being studied, as they aim to represent the population accurately.
This reduces biases that may result from using a more selective sample.
2. Representative Results
By including a diverse set of individuals from all subgroups within a population (e.g., age, gender, socioeconomic status, geography), the sample reflects the characteristics of the whole population.
This is crucial for studies that aim to draw conclusions about broad groups.

.

Question 2

issues with canadian sample

Answer 2

However just canadian:
1. Limited Generalizability
Results may not be applicable to populations outside of Canada due to cultural, geographic, socioeconomic, or demographic differences.
Canada has unique characteristics (e.g., a high standard of living, strong healthcare system, bilingualism, multicultural policies) that may not reflect the realities of other countries.
2. Cultural and Ethnic Bias
While Canada is multicultural, certain groups may be underrepresented in a study (e.g., Indigenous communities or recent immigrants), leading to skewed results even within the country.
Findings might not align with cultural norms or practices in non-Canadian populations.
3. Economic and Political Context
Canada’s economy, healthcare, education systems, and social policies (e.g., universal healthcare) differ significantly from those in other nations.
Studies on health, social behaviors, or economic outcomes might not be relevant in countries with different systems, such as the U.S. or developing nations.

Question 3

lit review

Answer 3

comprehensive lit review
clearly stated the gaps e.g. first study to look at layers/levels of insomnia and population sample rather than clinical sample

However not very critical

Question 4

methods:

Answer 4

each questionaire was justified with internal consistency and test retest reliability, and was also checked for Canadian samples (Validation in French)

rigorous transparent way of grouping. However still technically made up.

No clear direct hypothesis, may be okay as explporatory

using Kisch reduced bias in sample

Question 5

Data analysis dis

Answer 5

Overly Complex Analysis:

The analysis being “too complex” could make it difficult for readers (especially non-statistical experts) to interpret the findings and assess their validity.
Excessively complex methods may also indicate overfitting to the data, where findings may not generalize to other populations or settings.
Data-Driven Approach (Exploratory Bias):

While data-driven approaches are valuable, they risk capitalizing on chance associations. Without clear a priori hypotheses, there’s a greater likelihood of spurious results.
This could reduce the theoretical impact of the study, as it may lack clear explanatory power for the observed relationships.
Bonferroni Correction for Critical Values, Not p-values:

Adjusting critical values rather than p-values is less common and might lead to inconsistencies or confusion.
It also increases the risk of over-conservatism, where true effects might be missed (Type II error), as the adjustment can be overly stringent, especially in studies with numerous comparisons.
Lack of Clarity or Justification for Methods:

If the paper does not adequately justify the choice of complex methods or explain them in detail, it becomes harder to evaluate their appropriateness for the research question.
Readers might question whether simpler, more standard approaches could have yielded similar insights.
Multiple Testing and Power Issues:

While Bonferroni correction mitigates Type I error, it does so at the cost of statistical power. The more tests conducted, the more conservative the threshold becomes, which might make it challenging to detect real, meaningful effects.
If the study does not explicitly discuss how they addressed power issues or justified the number of comparisons, this would be a methodological weakness

Question 6

data anlaysis implications

Answer 6

Unclear Methodological Choices:

The absence of justification for the complex data analysis methods can make it harder for readers to understand why specific analyses were conducted and whether they were appropriate for the research questions.
Bonferroni Correction Usage:

Without a clear explanation of why Bonferroni corrections were applied to critical values rather than p-values, this may raise concerns about whether the statistical adjustments were appropriately chosen for the study’s design and goals.
Data-Driven Approach:

If the methods are exploratory (data-driven), this approach needs explicit acknowledgment, including its limitations, such as the risk of identifying spurious correlations.

Question 7

data analysis advantages

Answer 7

Correction for Multiple Comparisons:

The implementation of a Bonferroni correction reflects an effort to control for the increased risk of Type I errors due to multiple statistical comparisons, demonstrating methodological rigor.

Question 8

ethics

Answer 8

nothing!
severe lack of ethical considerations.
confidentiality, even though questionnaires!
approval to conduct study?

Question 9

more disadvanatges-

Answer 9

1. Use of Baseline Data Only
   Using only baseline data limits the ability to assess how variables or outcomes evolve over time. This approach may capture only a snapshot of the relationships between variables, ignoring dynamic interactions or changes in the data. For instance, longitudinal studies could provide insights into causal relationships or temporal effects, which a baseline-only approach cannot achieve. This limitation reduces the depth and applicability of the findings.
2. Acknowledgment of Confounding Variables
   The paper acknowledges potential confounding variables such as gender and moderation effects. However, simply recognizing confounders without adequately addressing them can compromise the validity of the results. While this acknowledgment shows awareness of these influences, it is insufficient if the confounders are not properly controlled or accounted for in the analysis.
3. Inappropriate Use of ANOVA for Testing Moderation
   Using ANOVA to test moderation effects is statistically inadequate because moderation typically involves interaction terms between variables. Proper moderation analysis requires regression-based approaches (e.g., hierarchical regression or structural equation modeling) where interaction terms are explicitly included. ANOVA does not provide the nuanced capacity to test for interaction effects comprehensively, which may lead to inaccurate conclusions regarding moderation.