Quantitative Research Methods and Hypothesis Types

Quantitative Research Features

• Study Association and Correlation: Focuses on the association and correlation between quantitative variables.
• Determination of Force: Attempts to determine the force of association or correlation between variables, as well as the generality, objectivity, and inference of results for a population.
• Causal Inference: It seeks to make causal inferences that explain why things happen or do not happen in a certain way.
• Fundamental Method: Relies on quantitative results, explanations, and observations.
• Logical Positivism: Uses logical positivism where hypotheses are countered and probabilistic. The investigator must ensure the hypothesis can be tested.
• Objectivity: Uses objectivity as a tool to get at the truth.
• Process: The process is a chain that is dynamic and concrete.
• Independence: There is independence between the object and the subject to join the study. They can interact during the interpretation and analysis phase.
• Theory: Theory gives sense to the investigation at both the beginning and the end.
• Predictions: Explanatory predictions increase the potential of the goal.
• Strategies: Uses deductive strategies (moving from the general to the particular). This is achieved through the study of the sample.

Terms of Hypotheses

• Clarity: Conceptually clear; avoid far-fetched terms.
• Empirical Reality: Concepts used must have an empirical reality.
• Verification: Conception must be susceptible to verification, with resources readily available.
• Specificity: Conception should be specific to some degree.
• Relation: Conception must be in direct relation to the subject.
• Truthfulness: It must be true, which is usually achieved.
• Problem Solving: They must offer a possible answer to the problem.
• Formulation: They might be asked as a denial or an affirmation.

Classification of Hypotheses

1. Classification by Nature

• Common Sense: Based on common facts of life.
• Scientific: Requires theoretical concepts and experimentation. This includes work hypotheses (specific investigations prompted by reference to a determined place, time, and population) and theoretical hypotheses (abstract versus independent of reality, with no verification and non-empirical).
• Metaphysics: Very general rules involving cosmological suppositions and components of reality.

2. Classification by Structure

• One Variable: Focuses on one characteristic of a population or universe.
• Two Variables: Focuses on two features of a population or universe; they must have a direct relation.
• Two or More Variables: Focuses on more than two characteristics; besides a relationship, they must have a degree of dependence.

3. Logical and Linguistic Forms

• Simple: Variables that are easy to understand and use.
  • Attributive: Hypotheses that have variables correlated to attributes of the subject.
  • Relationships: Several variables related to a subject, but with a degree of relationship.
• Composite Ratio: Defined by their utterances.
  • Copular: Having a close relationship between variables; they can be changed or replaced without losing significance.
  • Choice: Involves two situations, doubt, or questions.
  • Condition: Variables that are conditional.

4. Classification by Generality

• Unique: Refers to a single individual with an attributive reference.
• Individuals: Refers to a sample and only a portion of it.
• Limited Universals: Refers to the entire population, but within one specific space and time.
• Strict Universals: No restrictions, no specific statements, and no specific population or time; these are abstract.

5. Classification by Function

• Home: Establishes a relationship between variable concepts to encourage research.
• Sub-hypothesis: A relationship where the main hypothesis criteria are derived.
• Auxiliary: Focuses on validity, showing an indicative relationship between variables; these indicators are replaceable.
• General: Refers to the generalization of a sample.