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Quantitative Research Terms - Coggle Diagram
Quantitative Research Terms
Data Characteristics and Types
These terms relate to the nature and structure of the data being collected and analyzed. Understanding data types and variables is crucial for selecting appropriate statistical methods and interpreting results accurately. Without these concepts, researchers would struggle to organize their data effectively or identify relationships between different factors in their studies.
Variables
Data types
Independent variable
Dependent variable
Measures of Variability
Variance
Standard deviation
Outlier
These terms describe how data points are spread out from the average. They help researchers understand the distribution of their data and identify unusual values. Without measures of variability, we would have an incomplete picture of our data, potentially missing important patterns or anomalies that could affect our conclusions.
Research Design and Methodology
Quantitative hypothesis
Data sources
Rigor: sample size
Sampling errors
These terms relate to how a study is planned and executed. They ensure that research is well-designed, data is properly collected, and potential errors are accounted for. Without attention to these aspects, studies could be poorly structured, leading to unreliable results or conclusions that don't accurately represent the population being studied.
Relationships and Causality
Correlation
Causation
These concepts help researchers understand how variables relate to each other. Correlation indicates a relationship between variables, while causation implies that one variable directly influences another. Distinguishing between these is crucial; without this understanding, researchers might incorrectly assume causation where only correlation exists, leading to flawed conclusions.
Research Quality Indicators
Validity
Reliability
Internal validity
External validity
These terms relate to the trustworthiness and applicability of research findings. They help ensure that studies measure what they intend to measure and produce consistent results. Without these indicators, the credibility of research would be compromised, making it difficult to trust or apply findings in real-world contexts.
Research Communication
Presenting results
-Transparency in reporting
These concepts focus on how research findings are shared with the scientific community and the public. Clear presentation and transparency are essential for replicability and peer review. Without effective communication and transparency, valuable research might be misunderstood or its integrity questioned, hindering scientific progress and the application of findings.
Statistical Approaches
Descriptive statistics
Inferential statistics
Statistical significance
These concepts represent different ways of analyzing and interpreting data. Descriptive statistics summarize data, while inferential statistics allow us to draw conclusions about populations based on samples. Statistical significance helps determine if results are likely due to chance. Without these approaches, researchers would lack the tools to meaningfully analyze data or make reliable generalizations from their findings.