Quantitative Research Vocabulary

Population: Refers to the entire group that research aims to understand or draw conclusions about.
Sample: A subset of the population used to represent the population in a study.

Descriptive Statistics:
- Purpose: To summarize and describe the characteristics of a dataset.
- Example Question: "Do you enjoy the films of Nicolas Cage?"
Inferential Statistics:
- Purpose: To infer or draw conclusions about a population based on sample data.
- Example Question: "If someone enjoys the films of Nicolas Cage, what else can we infer about him/her?"

Definition: Constructs are abstractions generalized from particulars; they are ideas that researchers form to summarize observations about things that cannot be directly observed.

Definition: The scale used to measure constructs impacts the type of statistics utilized for data analysis (e.g., mean vs. mode) and hypothesis testing (e.g., correlation vs. t-tests).
Nominal Level:
- Definition: For categorical data where numbers assigned to categories are indices without real meaning.
- Characteristics: These data are typically represented by counts and often analyzed by mode.
- Example in text: "The sample was largely female (n = 60; 74%)."
Ordinal Level:
- Definition: For numerical data where numbers suggest order but do not provide precise measurements of distance between categories.
- Characteristics: Reflects the rank order of categories.
Interval Level:
- Definition: For numerical data where numbers not only suggest order but assume equal distances between categories (e.g., temperature in Celsius).
Ratio Level:
- Definition: For numerical data that includes an absolute zero point, thus allowing for the comparison of absolute magnitudes (e.g., weight, height).
- Characteristics: Incorporates all properties of interval level as well as an absolute zero point.

Common levels of measurement for social science constructs typically include ordinal scales, especially in surveys measuring attitudes or traits, exemplified by Likert scales.
Example in survey format from a measure of Machiavellianism:
- Strongly Disagree (1)
- Disagree (2)
- Undecided (3)
- Agree (4)
- Strongly Agree (5)

Definition: Measurement is the process of determining the existence, characteristics, size, and/or quantity of change in a variable through systematic recording and organizing of observations.

Definition: It consists of rules for assigning numerical values to units (people) to indicate the relative level/degree of a variable that is present.
Example: Belief in the Supernatural
- Conceptual Definition: "Belief in one or more extraordinary phenomena that defy explanation according to current scientific understanding of natural law."
- Operational Definition: Utilization of the Paranormal Belief Scale or Supernatural Belief Scale (refer to Survey for details).

Reverse Scoring: For the odd-numbered items, inverse the score (e.g., for items sb1r, sb3r, sb5r, … sb91r; 1=5, 2=4, 3=3, 4=2, 5=1).
Summation: Total the scores for all items (sb1 + sb2 + sb3 + … sb20).
Average Calculation: Divide the total score by the number of items (20) to revert scores to the original 1-5 scale.

Difference: A hypothesis is a specific, testable prediction about the expected outcome of a study, while a research question is a broad query that the study seeks to answer.
Directionality: Refers to whether the hypothesis predicts the direction of the relationship between variables (positive/negative) or simply states a relationship without specifying direction.

Independent Variable: The causal variable that is manipulated by the researcher to observe its effect.
Dependent Variable: The effect variable that is measured to see the impact of changes in the independent variable.
Key Characteristics: Independent variables serve as predictors of dependent variables, thus being critical to the design and analysis of quantitative research.