46 Questions
What is a major criticism of quantitative research?
It relies on instruments and procedures, hindering connection with everyday life
What is Goodhart's Law related to?
Quantitative measures becoming targets, losing their value as measures
What type of error occurs due to differences between sampling estimates and population parameters?
Sampling error
What is a consequence of using quantitative social indicators for decision-making?
Corruption pressures and distortion of social processes
What is a limitation of quantitative research?
It fails to distinguish people and social institutions from the world of nature
What is a potential source of error in research?
Data collection error
What can lead to a static view of social life?
Analysis of relationships between variables
What can be affected due to the reliance on instruments and procedures?
The connection between research and everyday life
What type of error is represented by the I-term in the equation X=T + I + e?
Systematic error
What is a source of systematic measurement error in cross-national research?
Natural language differences
Which type of reliability assesses whether measures are consistent over time?
Stability
What is the main concern of internal validity?
Causality between variables
What type of validity is concerned with whether a measure performs according to theoretical expectations?
Construct validity
What is the main concern of measurement invalidity?
Systematic error
What is the main difference between measurement error and true variation?
True variation is the variation in the variable being measured, while measurement error is the variation due to the measurement process
Which type of reliability assesses whether measures are consistent between observers?
Inter-observer consistency
What is the main concern of external validity?
Generalization of results
What is an example of a systematic error or bias in measurement?
A question that is poorly worded, leading to misinterpretation
What is the term for the inconsistency of a measurement instrument under repeated uses?
Measurement unreliability
What is the term for the difference between the observed variable and the true variable of interest?
Measurement error
What is an example of measurement invalidity?
A question that is poorly worded, leading to misinterpretation
What is the total value of the construct of interest, including both systematic and random error?
Total value of the construct of interest
The ______ approach involves using data to develop a theory, whereas the ______ approach involves using a theory to make predictions that are tested with data.
inductive, deductive
In the ______ approach, data is collected in the form of numbers through instruments such as surveys.
quantitative
Data quality in ______ research refers mainly to research design and the measurable quality of data.
quantitative
The ______ cycle involves a process of deductive approach, with theory leading to observations and findings.
research
In ______ research, data is collected in the form of words, and the researcher is the instrument.
qualitative
Epistemology deals with the question of ______ can we know, whereas ontology deals with the question of what can we know.
how
[Blank] approach focuses on the use of statistics and methods to analyze data, whereas ______ approach focuses on the use of interviews, documents, and observations.
Quantitative, Qualitative
The ______ approach involves testing a theory by making predictions that are then tested with data, whereas the ______ approach involves developing a theory based on observations and findings.
deductive, inductive
The measure of ______ applies to both multiple indicators for latent variables and to single indicators.
T
X = ______ + I + e
T
______ reliability assesses whether measures are consistent over time.
Stability
______ validity refers to the extent to which a measure performs according to theoretical expectations.
Construct
______ validity refers to generalization.
External
______ error occurs due to differences in the translations in cross-national research.
Systematic
______ validity is concerned with whether a measure reflects the concepts.
Measurement
______ reliability assesses whether measures are consistent between observers.
Inter-observer
The ______ stage involves collecting data to test a hypothesis.
Findings
Quantitative researchers rely heavily on ______ to collect and analyze data.
Instruments
One criticism of quantitative research is that it treats people and social institutions as part of ______.
the world of nature
According to ______'s Law, a measure becomes less reliable when it becomes a target.
Goodhart
A ______ design involves studying a single case or a small number of cases in depth.
Case study
Sampling error occurs due to differences between ______ estimates and population parameters.
sampling
______ error occurs when there are problems with the data collection process.
Data collection
A ______ design involves studying a phenomenon over a long period of time.
Longitudinal
Study Notes
Variation and Error
- Variation consists of true variation and variation due to error.
- A variable with little error has a smaller difference between true variation and variation due to error.
- A variable with considerable error has a larger difference between true variation and variation due to error.
Measurement Error
- Measurement error (ME) is how far the measured variable is free from measurement error.
- ME consists of systematic error/bias (validity issue) and random measurement error (reliability issue).
- Systematic error/bias is a validity issue, where the measure does not capture the concept of interest.
- Random measurement error is a reliability issue, where the measure is inconsistent under repeated uses.
Measurement Error and Validity
- At the empirical level, there is a distinction between the variable we want to measure (T) and the observed variable (X).
- The observed value (X) is a combination of the true value (T), invalidity (I), and random error (e).
Sources of Measurement Error
- Sources of systematic measurement error include:
- Response scales (different scale points)
- Wording of items
- Order of response categories
- Context of question
- Response styles
- Natural language differences in cross-national research
- Unjustified differences in translations
- Differences in interpretation of questions in different countries
Quality Indicators in Quantitative Business Research
- Quality indicators include:
- Reliability (are measures consistent?)
- Replication/replicability (is the study repeatable?)
- Measurement (or construct) validity (do measures reflect concepts?)
- Validity (are conclusions well-founded?)
- Internal validity (are causal relations between variables real?)
- External validity (can results be generalized beyond the research setting?)
- Ecological validity (are findings applicable?)
Types of Reliability
- Stability: is the measure stable over time? (e.g., test-retest method)
- Internal reliability: are the indicators consistent? (e.g., split-half method)
- Inter-observer consistency: is the measure consistent between observers?
- External reliability: replicability
Types of Validity
- Face validity: intuitive process
- Construct validity (or measurement validity): extent to which a measure performs according to theoretical expectations
- Internal validity: refers to causality; is X really causing Y? (linked to confounding variable bias)
- External validity: generalization
- Ecological validity: are research findings applicable?
Main Preoccupations of Quantitative Research
- Measurement
- Causality
- Generalization
- Replication
- Reliability and validity
Criticisms of Quantitative Research
- Quantitative researchers fail to distinguish people and social institutions from the natural world.
- The measurement process possesses an artificial and spurious sense of precision and accuracy.
- The reliance on instruments and procedures hinders the connection between research and everyday life.
- The analysis of relationships between variables creates a static view of social life that is independent of people's lives.
Quantitative Measures: Pitfalls
- Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure."
- Campbell's Law: "The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor." (e.g., high-stakes testing; teaching to the test)
Sources of Error
- Sampling (-related) error:
- Sampling error: difference between sampling estimate and population parameter
- Sampling related error: size, selection, etc.
- Data collection error
Research Designs
- Experimental design
- Cross-sectional design
- Longitudinal design
- Case study design
- Comparative design
- Design science
Main Preoccupations of Quantitative Research
- Measurement
- Causality
- Generalization
- Replication
- Reliability and validity
Criticisms of Quantitative Research
- Failure to distinguish people and social institutions from the natural world
- Artificial and spurious sense of precision and accuracy in measurement
- Hindrance to connection between research and everyday life due to reliance on instruments and procedures
- Static view of social life that is independent of people's lives
Quantitative Measures: Pitfalls
- Goodhart's Law: a measure becomes a target, it ceases to be a good measure
- Campbell's Law: quantitative social indicator is used for social decision-making, it becomes subject to corruption pressures and distorts social processes
Sources of Error
- Sampling error: difference between sampling estimate and population parameter
- Sampling related error: size, selection, etc.
- Data collection error
Data Quality in Quantitative Research
- Data quality is a broad concept that refers to research design and measurable quality of data
- Refers to: % no opinion, differentiation in response patterns, item nonresponse, etc.
Epistemology and Ontology
- Epistemology: how can we know?
- Ontology: what can we know?
- Positivism
- Interpretivism
- Objectivism
- Constructionism
Deductive and Inductive Approach
- Deductive approach: theory → observations → findings
- Inductive approach: observations → theory → findings
- Quantitative methods: data as numbers, from instruments
- Qualitative methods: data in the form of words, from the field
Research Cycle
- Deductive approach process:
-
- Theory
-
- Data
-
- Measure of T (true value)
-
- Formula: X = T + I + e (observed value = true value + invalidity + random error)
Sources of Measurement Error (ME)
- I-term: invalidity or systematic error
- Sources of ME: response scales, wording of items, order of response categories, context of question, response styles, natural language differences, unjustified differences in translations, differences in interpretation of questions
Quality Indicators in Quantitative Business Research
- Reliability: are measures consistent?
- Replication/replicability: is study repeatable?
- Measurement (or construct) validity: do measures reflect concepts?
- Validity: are conclusions well-founded?
- Internal validity: are causal relations between variables real?
- External validity: can results be generalized beyond the research setting?
- Ecological validity: are findings applicable?
Types of Reliability
- Stability: is the measure stable over time?
- Internal reliability: are the indicators consistent?
- Inter-observer consistency: is the measure consistent between observers?
- External reliability: replicability
Types of Validity
- Face validity: intuitive process
- Construct validity (or measurement validity): extent to which a measure performs according to theoretical expectations
- Internal validity: refers to causality, linked to confounding variable bias
- External validity: generalization
- Ecological validity: are research findings applicable?
Understanding the concepts of variation and error in statistical measurements, including true variation and variation due to error, and measurement error comprising systematic and random errors.
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