Quantitative method Flashcards
is a research approach that emphasizes using numerical measurements
and statistical analysis to understand phenomena.
quantitative method
This method involves collecting data that can be
quantified and subjecting it to various statistical techniques to test hypotheses or explore relationships
between variables. It is widely used in science, economics, psychology, and social sciences, where
researchers aim to produce objective results that can be generalized across populations.
quantitative method
are valued
for their precision and the ability to handle large volumes of data, making them crucial for making
informed decisions and validating theories.
Quantitative methods
Quantitative methods provide a framework for obtaining objective data that
can be universally measured and analyzed. This reduces the influence of personal biases and
subjectivity in interpreting results.
Objective Analysis:
Using statistical methods to analyze data from a sample, quantitative research
allows for conclusions that can be generalized to a larger population. This is particularly valuable
in fields like medicine or economics, where researchers must broadly apply findings.
Generalizability:
Quantitative analysis often involves developing models to predict
outcomes based on measurable variables. These predictions are essential in weather forecasting,
finance, and logistics.
Predictive Capabilities:
The structured nature of quantitative research, emphasizing precise measurement
and replicable procedures, ensures that studies can be repeated. This replication is key to
verifying results and building on existing knowledge.
Replicability:
Quantitative research uses statistical tools to validate hypotheses. This
provides a robust framework for testing theories and establishing facts with a known degree of
accuracy.
Statistical Validity:
Quantitative methods are used to make informed decisions in business and
policy-making. For example, by analyzing customer data, businesses can optimize their strategies
to meet market demands better.
Decision Making:
The quantitative method is crucial in research and decision-making for several reasons:
- Objective Analysis: Quantitative methods provide a framework for obtaining objective data that
can be universally measured and analyzed. This reduces the influence of personal biases and
subjectivity in interpreting results. - Generalizability: Using statistical methods to analyze data from a sample, quantitative research
allows for conclusions that can be generalized to a larger population. This is particularly valuable
in fields like medicine or economics, where researchers must broadly apply findings. - Predictive Capabilities: Quantitative analysis often involves developing models to predict
outcomes based on measurable variables. These predictions are essential in weather forecasting,
finance, and logistics. - Replicability: The structured nature of quantitative research, emphasizing precise measurement
and replicable procedures, ensures that studies can be repeated. This replication is key to
verifying results and building on existing knowledge. - Statistical Validity: Quantitative research uses statistical tools to validate hypotheses. This
provides a robust framework for testing theories and establishing facts with a known degree of
accuracy. - Decision Making: Quantitative methods are used to make informed decisions in business and
policy-making. For example, by analyzing customer data, businesses can optimize their strategies
to meet market demands better.
Types of Quantitative Research Methods
- Surveys and Questionnaires
- Experiments
- Longitudinal Studies
- Secondary Data Analysis
- Purpose: To collect data from a large group of people efficiently.
- Method: Involves standardized questions to ensure consistency and comparability of responses.
Surveys can be conducted online, in person, or over the phone. - Application: Common in market research, sociology, and health studies to gauge public opinion,
satisfaction, or behaviors.
Surveys and Questionnaires
- Purpose: To determine cause-and-effect relationships by manipulating variables.
- Method: Participants are typically divided into control and experimental groups to test the
impact of specific changes. - Application: It is widely used in scientific research, psychology, and medicine, such as testing new
drugs or psychological interventions.
Experiments
- Purpose: To track changes over time.
- Method: Data is collected from the same subjects repeatedly over a prolonged period.
- Application: Useful in developmental psychology, education, and health sciences to study
changes across the lifespan or the effects of long-term interventions.
Longitudinal Studies
- Purpose: To analyze existing data collected for other research purposes.
- Method: Involves the statistical analysis of data from government databases, historical records,
or previous research studies. - Application: Economists, historians, and policy analysts often use this method to conduct large-
scale studies without requiring direct data collection.
Secondary Data Analysis