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Research and Scholarship Guide

This guide is an introduction to research questions, data collection, and data analysis.

Glossary


A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

 

A


Abstract: A summary of the research study, including its purpose, methods, results, and conclusions. 

Anecdotal Evidence: Non-scientific observations or stories that do not provide proof but may provide insight. 

Anova (Analysis of Variance): A statistical method used to compare means across multiple groups to determine if there are significant differences among them. 

B


Bias: Systematic errors introduced into sampling or testing that can affect the validity of the results. 

Bibliography: A list of the sources referenced in the research.

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C


Case Study: An in-depth analysis of a single event, situation, or individual. 

Citation: A reference to a source of information. 

Causality: The relationship between cause and effect, indicating that one event (the cause) directly influences another event (the effect). 

Confounding Variable: An external variable that can influence both the dependent and independent variables, potentially skewing the results. 

Construct Validity: The degree to which a test measures the concept it is intended to measure. 

Control Group: In an experiment, the group that does not receive the treatment being tested, used as a benchmark to measure how the other tested subjects do.

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D


Data: Factual information used as a basis for reasoning, discussion, or calculation. 

Data Triangulation: Using multiple data sources or methods to increase the validity of research findings. 

Dependent Variable: The variable being tested and measured in an experiment.

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E


Ethics: Moral principles that govern the conduct of research, ensuring honesty, integrity, and fairness.

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G


Grounded Theory: A qualitative research method aimed at theory development through the systematic collection and analysis of data.

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H


Hypothesis: A tentative statement predicting a relationship between variables that can be tested.

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I


Independent Variable: The variable that is manipulated or changed in an experiment to test its effects on the dependent variable.

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L


Literature Review: A comprehensive survey of publications in a specific field of study, providing an overview of current knowledge, theories, and findings. 

Longitudinal Study: Research that involves repeated observations of the same variables over long periods.

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M


Methodology: The systematic, theoretical analysis of the methods applied to a field of study. 

Meta-Analysis: A statistical technique that combines the results of multiple studies to identify patterns or overall effects.

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O


Operationalization: The process of defining variables in practical, measurable terms. 

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P


Paradigm: A framework of theories, methods, and standards that guides research within a specific discipline. 

Path Analysis: A statistical technique used to describe the directed dependencies among a set of variables. 

Peer Review: The evaluation of work by one or more people with similar competencies as the work's producers. 

Phenomenology: A qualitative research approach that focuses on the lived experiences of individuals and the meanings they attach to those experiences. 

Population: The entire group of individuals or instances about whom the research is concerned.

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Q


Qualitative Research: Research that explores phenomena through non-numerical data like interviews, surveys, and observations. 

Quantitative Research: Research that focuses on numerical data and statistical analysis to explain phenomena.

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R


Random Sampling: A sampling method where each member of the population has an equal chance of being selected. 

Reliability: The degree to which research consistently yields the same results under the same conditions. 

Research Design: The overall strategy used to integrate the different components of the study in a coherent and logical way. 

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S


Sample: A subset of the population selected for study. 

Sampling Bias: A bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others. 

Sampling Error: The error caused by observing a sample instead of the whole population. 

Statistical Significance: The probability that the observed results are not due to chance. 

Structural Equation Modeling (SEM): A statistical technique that tests the relationships between multiple variables simultaneously. 

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T


Thematic Analysis: A method used in qualitative research to identify and analyze themes or patterns within qualitative data. 

Theory: A system of ideas intended to explain something, based on general principles independent of the phenomena to be explained.

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V 


Validity: The extent to which a concept, conclusion, or measurement is well-founded and corresponds accurately to the real world. 

Variance: A statistical measure of the spread between numbers in a dataset, indicating the degree of variability from the mean.

Z


Z-Score: A statistical measurement that describes a value's relationship to the mean of a group of values, expressed in terms of standard deviations.  

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