As You Look Closer At Your Article, Did It Discuss Validity ✓ Solved

As you look closer at your article, did it discuss validit

As you look closer at your article, did it discuss validity and reliability? How can that impact research results? Please write a response and include the reference: Houser, J. (2018). Nursing research: Reading, using, and creating evidence, 4th ed. Jones & Bartlett Learning.

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Introduction

When critically appraising a research article—especially one that reports survey data—readers should look for explicit discussion of validity and reliability. These two measurement properties determine whether the instrument measures what it intends to (validity) and whether it does so consistently (reliability). Failure to address validity and reliability can undermine conclusions, inflate type I or II errors, and limit the generalizability and usefulness of findings (Houser, 2018; Polit & Beck, 2017).

Defining Validity and Reliability

Validity is the degree to which evidence and theory support the interpretations of test scores for intended uses. Key types include content validity (coverage of the construct), construct validity (theoretical alignment), and criterion validity (correlation with a gold standard) (Creswell & Creswell, 2018; DeVellis, 2017). Reliability refers to the reproducibility or consistency of scores across time, items, and raters—commonly estimated with internal consistency statistics (e.g., Cronbach’s alpha), test–retest correlations, or interrater reliability coefficients (Tavakol & Dennick, 2011; Nunnally & Bernstein, 1994).

Did the Article Discuss Validity and Reliability?

In a thorough methods section, researchers will describe how they evaluated and established validity and reliability for their survey instrument. This can include pilot testing, expert panel review for content validity, factor analysis for construct validity, correlation with established measures for criterion validity, and calculation of Cronbach’s alpha or intraclass correlation coefficients for reliability (Streiner, Norman, & Cairney, 2015). If an article omits these details, readers should treat reported relationships and inferences cautiously because measurement error and poor construct representation may be present (Houser, 2018).

How Validity and Reliability Impact Research Results

Measurement properties directly affect the accuracy and interpretability of research results in several ways:

  • Bias in Estimates: Poor validity leads to systematic error—measures may capture a related but different construct, biasing effect estimates (Creswell & Creswell, 2018).
  • Reduced Statistical Power: Low reliability increases random measurement error, attenuating correlations and regression coefficients and raising the chance of type II errors (Nunnally & Bernstein, 1994).
  • Misleading Cross-tabulation and Subgroup Findings: When categorical survey responses are cross-tabulated, unreliability can create spurious associations or mask true relationships across subgroups (Polit & Beck, 2017).
  • Limited Generalizability: If validity evidence is limited to a specific population or context, findings may not transfer to other settings or populations (Burns & Grove, 2010).
  • Threats to Inference: Measurement error complicates causal inference because observed associations may reflect instrument artifacts rather than substantive relationships (Trochim, 2006).

Practical Examples

Consider a patient-satisfaction survey used to compare two clinics. If items lack content validity (missing key domains of satisfaction), the survey may under-represent true patient experience; apparent clinic differences might reflect omitted aspects rather than real variation. If internal consistency is low (Cronbach’s alpha

Assessing Validity and Reliability in Published Articles

When reading an article, look for:

  • Descriptions of instrument development, pilot testing, and expert review (content validity) (Streiner et al., 2015).
  • Statistical assessments such as exploratory or confirmatory factor analysis for construct validity (Kline, 2015).
  • Evidence of criterion-related validity when comparable measures exist (Polit & Beck, 2017).
  • Reported reliability indices: Cronbach’s alpha, ICC, or kappa for interrater agreement (Tavakol & Dennick, 2011).
  • Discussion of limitations if validity or reliability evidence is incomplete (Houser, 2018).

Recommendations for Authors and Readers

Authors should report validity and reliability evidence transparently, including sample characteristics for validation, factor structures, scale properties, and any adaptations made for context or language (DeVellis, 2017). Readers and reviewers should request or seek supplemental materials (e.g., instrument items, validation tables) when these details are absent. If instrument properties are weak or unreported, interpret results cautiously, and consider replication with validated measures (Burns & Grove, 2010; Polit & Beck, 2017).

Conclusion

Validity and reliability are foundational to the integrity of survey-based research. Explicit reporting and assessment of both help ensure that conclusions reflect underlying phenomena rather than measurement artifacts. When an article discusses validity and reliability thoroughly (as guided in texts such as Houser, 2018), readers can have greater confidence in the study’s conclusions. Conversely, omission or weak evidence regarding measurement properties should temper interpretation and prompt further validation work.

Key Takeaway

Always check for content, construct, and criterion validity evidence and appropriate reliability indices when evaluating survey findings. These properties determine whether results are meaningful, reproducible, and generalizable (Houser, 2018; Creswell & Creswell, 2018).

References

  • Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Jones & Bartlett Learning.
  • Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Wolters Kluwer.
  • Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
  • DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
  • Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health measurement scales: A practical guide to their development and use (5th ed.). Oxford University Press.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
  • Burns, N., & Grove, S. K. (2010). Understanding nursing research: Building an evidence-based practice (5th ed.). Elsevier.
  • Kline, P. (2015). A handbook of test construction: Introduction to psychometric design (2nd ed.). Routledge.
  • Trochim, W. M. (2006). Research methods knowledge base. Atomic Dog Publishing. Retrieved from https://conjointly.com/kb/

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