The document outlines the importance of content and construct validity in qualitative research, defining both concepts and emphasizing their significance for ensuring data relevance and accuracy. Content validity ensures that a measurement instrument comprehensively covers a research topic, while construct validity verifies that the instrument accurately measures the theoretical constructs intended. Additionally, the document discusses operational definitions, reliability types, and challenges associated with measuring variables in research.

2. OBJECTIVES
Understand and Differentiate Between Content Validity and Construct
Validity:
•By the end of this lesson, will be able to define content validity and
construct validity, explain the differences between the two, and
understand their significance in qualitative research.
Apply Validity Concepts in Qualitative Research:
•By the end of this lesson, will be able to assess the content validity and
construct validity of qualitative research studies, and develop strategies to
enhance the validity of their own qualitative research designs.

3. When conducting qualitative research,
researchers must ensure that their data is
valid and reliable. This is crucial in order to
draw accurate conclusions and make
meaningful insights. In this article, we will
discuss two types of validity that are essential
for qualitative research: content validity and
construct validity.
Quanlitative Research

4. Content validity refers to the extent to which a measurement instrument, such as a
survey or interview guide, covers all aspects of a particular concept or topic. In
other words, it assesses whether the data collected is relevant and comprehensive
enough to represent the entire scope of the research question. Content validity is
important because it ensures that the data collected is relevant and representative
of the research topic. (Wieringa, 2022)
What is Content Validity?

5. The Importance of Content Validity in Qualitative
Research
Validation

6. Construct validity refers to the extent to which a research study measures what it intends to measure. It assesses
whether the data collected truly reflects the theoretical constructs and concepts being studied. In other words, it
ensures that the measurements used in the study accurately represent the underlying theoretical framework.
What is Construct Validity?

7. Measurability of Variables
Write the Operational Definition
Example
 Conceptual: Socio-economic status is a person’s economic
standing based on lifestyle, prestige, power, and control of
resources (Liu, Ali, Soleck, Hopps, Dunston, Pickett, 2004).
 Operational: In this study, socio-economic status was defined as
participants’ self-report of their average monthly.

8. Measurability of Variables
Example
 Conceptual: Intrinsic motivation is defined as the
motivation for a behavior that is based on the inherent
enjoyment in the behavior itself (Ryan and Deci, 2002).
 Operational: In this study, intrinsic motivation was
measured by responses to seven items of a 5-point
Likert-scale on a self-report questionnaire.

9. 2. Validity
Definition: The extent to which a measurement instrument measures
what it is intended to measure.
Types:
• Content Validity: Ensures the instrument covers the entire domain of the
concept.
• Construct Validity: Ensures the instrument measures the theoretical construct it
is intended to measure.
• Criterion Validity: Compares the instrument to an external criterion (concurrent
or predictive validity).
Measurability of Variables

10. Reliability
Definition: The consistency of a measurement instrument.
• Types:
• Test-Retest Reliability: Stability over time.
• Inter-Rater Reliability: Consistency between different raters.
• Internal Consistency: Consistency of items within the instrument
(e.g., Cronbach's alpha).
Measurability of Variables

11. Examples of Measurability in Research
• Psychology: Measuring stress levels using a standardized
questionnaire.
• Economics: Measuring GDP growth rates.
• Health Sciences: Measuring blood pressure using a
sphygmomanometer.
Measurability of Variables

12. Challenges in Measurability
• Ambiguous Variables: Concepts that are difficult to define and
measure (e.g., happiness, intelligence).
• Measurement Errors: Inaccuracies in data collection can lead
to invalid results.
• Cultural Differences: Different interpretations of variables
across cultures can affect measurability.
Measurability of Variables

13. Sample Study
Influence of Social Media Trends on Students'
Psychological Behavior
Measurability of Variables

14. https://www.facebook.com/smcresearchoffice
A Guide to Appropriate Statistical
Selection