Chapter 4

• Quantitative research is a principal investigative orientation in the social sciences focusing on numerical data collection, deductive theory-research relationships, a natural science approach, and an objectivist view of social reality.

Steps in Quantitative Research

• The quantitative research process typically begins with a theory, from which a hypothesis is derived for testing.
• A research design is selected, influencing the external validity of findings and the ability to determine causality.
• Concepts are measured through operationalization, which involves defining how they will be measured.
• A research site is chosen and an ethics review is conducted.
• Participants or units of analysis are selected for the study.
• Research instruments are administered, such as pre-testing, manipulating variables, post-testing, or conducting interviews.
• Collected information is systematically recorded for analysis.
• Data is coded and transformed into numbers to facilitate quantitative analysis.
• Statistical techniques are used to test relationships between variables and assess reliability.
• Results are interpreted to answer research questions and evaluate hypotheses
• The research is written up and published to contribute to the body of knowledge.
• Published findings feed back into the research process, creating a cycle of deduction and induction.

Concepts

• Concepts are ideas or mental representations of things and serve as building blocks of theory.
• They can be independent variables (possible causes) or dependent variables (requiring explanation).
• Measurement allows for the identification of fine differences, consistent comparisons, and the establishment of relationships between variables.

Indicators

• Indicators are used to measure concepts, with nominal definitions describing the concept in words and operational definitions specifying measurement operations.
• Indicators can be direct or indirect measures of a concept.
• Indicators can be deviced through questions, observational classifications, official statistics, or written material analysis
• Multiple indicators can be combined, such as in Likert scales, to provide a more comprehensive measurement of a concept.
• Multiple-item measures can address issues such as misclassification and the failure to capture the full meaning of a concept.
• Multiple indicators allow for sophisticated data analyses and can help to identify response sets.
• Concepts may have multiple dimensions that should be considered when developing measures.

Coding

• Unstructured data, such as responses to open questions, is coded by deriving themes or categories and assigning numbers to them.
• Coding requires adhering to principles such as non-overlapping and exhaustive categories, and clear rules for application.

Reliability and Measurement Validity

• Reliability concerns the consistency of measures, including stability over time, internal reliability, and inter-observer consistency.
• Stability is assessed using the test-retest method, where a measure is administered and re-administered to the same sample to check for consistent results.
• Internal reliability refers to the consistency of multiple measures administered at one time and can be tested using Cronbach’s alpha coefficient or the split-half method.
• Inter-observer consistency is important when multiple researchers are involved in subjective judgments, such as coding observations.
• Measurement validity refers to whether a measure accurately gauges the concept it is designed to measure.
• Face validity is established by determining whether the measure appears to reflect the concept.
• Concurrent validity is gauged by examining a criterion relevant to the concept at the same time the measure is administered.
• Construct validity involves determining whether the concepts used in the research relate to each other in a way that is consistent with theoretical predictions.
• Convergent validity is gauged by comparing a measure to other measures of the same concept developed through different methods.
• Reliability and validity are related, as a measure cannot be valid if it is not reliable.

Goals of Quantitative Researchers

• Goals include accurate measurement, establishing causality, generalization of findings, and replication.
• Establishing causality involves searching for causal explanations, often using experimental designs or statistical controls.
• Generalization of findings involves applying the results beyond the study participants and context.
• Replication involves repeating research procedures to verify findings and minimize biases.

Criticisms of Quantitative Research

• Quantitative research is criticized for failing to distinguish people from the natural world, producing a false sense of precision, creating a disconnect between research and everyday life, promoting a remote view of social life, neglecting the perceptions of the people, and assuming an objectivist ontology.
• Some quantitative studies have shown a gap between stated and actual behavior.
• A study on COVID-19 deaths in Ontario long-term care homes found a relationship to their profit status.