Hypothesis

Question 1

Hypothesis

Answer 1

Based on your new image and our previous discussion, here is a breakdown of what a hypothesis is and how it connects to the concept of feasibility.

Essentially, while feasibility asks “Can we do it?”, the hypothesis asks “What do we think will happen when we do it?”

What is a Hypothesis?
A hypothesis is a formal, written prediction of what the researchers expect to find. It acts as a bridge that translates the broad research problem into a specific, testable statement.

According to the text, a strong hypothesis must identify three specific things:

Independent Variables: The factors the researcher will change or measure (the “cause”).

Dependent Variables: The outcomes or results they will measure (the “effect”).

The Population: The specific group of people being studied.

How Hypotheses Drive the Research
The hypothesis isn’t just a guess; it is the “DNA” of the study. It directly influences every step of the process:

Study Design: Deciding if the study will be experimental or observational.

Sampling Method: How the researchers will choose the right participants from the population.

Data Collection: What specific tools or tests are needed to measure the variables.

Interpretation: Whether the final results prove or disprove the original predictio

Question 2

Hypothesis

Answer 2

Feasibility Note: The “Doability” Check
1. Core Definition

Feasibility is the practical evaluation of whether a research problem and purpose can realistically be carried out. In critical appraisal, you examine the study to ensure the researchers didn’t just have a great idea (Abstract), but also had the means to execute it (Concrete).

2. The Five Pillars of Feasibility

To determine if a study is feasible, you must evaluate these specific resources:

Researcher Expertise: Did the team have the necessary training and experience to conduct the study?

Money Commitment: Was there enough funding to cover all research costs?

Availability of Participants: Was the specified population accessible and willing to participate?

Facilities: Did the researchers have the physical space (like labs or research rooms) required?

Equipment: Was the necessary technology or specialized tools available for use?

Question 3

Multiple hypotheses

Answer 3

Why a Study Might Have Multiple Hypotheses
A researcher rarely stops at one hypothesis because real-world problems are rarely driven by just one factor. Here is how those four or more hypotheses usually pile up:

1. Breaking Down Complex Variables

If a study is testing a Framework with multiple Propositions, each proposition needs its own hypothesis.

Example: If your framework suggests that “Lifestyle” affects “Heart Health,” you can’t just measure “lifestyle.” You need separate hypotheses for diet, exercise, and sleep.

2. Testing Sub-Groups

A researcher might have one hypothesis for the general population and others for specific demographics (age, gender, location) to see if the Relational Statement holds true across the board.

3. Exploring Mediators and Moderators

Sometimes the relationship isn’t direct.

Hypothesis 1: Variable A affects Variable B.

Hypothesis 2: Variable C changes how A affects B.

Connecting the Bridge: From Theory to Hypothesis
In your notes, you mentioned the Variable Spectrum. Here is how those multiple hypotheses act as the transition point:

The Framework (Abstract): Contains Propositions (e.g., “Social support improves psychological well-being”). These are broad and not directly testable.

The Hypotheses (Bridge): These take the Conceptual Definitions from your framework and turn them into Operational Definitions.

The Research Tool (Concrete): The hypotheses dictate exactly what questions you ask in a survey or what you measure in a lab.

Key Insight: You can think of Propositions as the “Theory’s Vision” and Hypotheses as the “Researcher’s To-Do List.”

Applying This to Your Notes

When you update your Abstract-to-Concrete Bridge summary, you might want to note that:

Assumptions are what we accept as true without testing.

Propositions are the theoretical relationships.

Hypotheses are the operationalized versions of those propositions that we actually put to the test.

Would you like to try taking a broad Proposition (Abstract) and breaking it down into two or three Hypotheses (Concrete) to see how the “bridge” works in practice?

Question 4

Associative hypothesis

Answer 4

Associative Hypotheses (The “Linked” Relationship)

Definition: These propose that variables exist together in the real world.

How they move: When one variable changes, the other changes as well.

The Big Distinction: They do not indicate that one variable causes an effect on another.

Real-world vibe: It’s like noting that people who buy more sunscreen also buy more ice cream. They are associated (likely because of the sun/heat), but the sunscreen isn’t causing the ice cream hunger.

Question 5

The hypothesis builds the research

Answer 5

In the world of research, the hypothesis acts as a formal prediction or a “bet” the researchers are making. When the results come back, they use statistics to see if they “won” that bet.

In the case of the Lerret et al. (2020) study you shared, the researchers placed their bet on the ePED app being better than usual care. However, the results were nonsignificant, meaning the “improvement” they hoped for didn’t actually happen in a way that mattered statistically.

Question 6

Simple hypothesis

Answer 6

Simple Hypothesis (1+1=2)

A simple hypothesis is like a straight line. It connects one independent variable (IV) to one dependent variable (DV).

Logic: If I change this one thing, this other thing will change.

Example: “Increased salt intake (1) is associated with higher blood pressure (2).”

Question 7

Complex hypothesis

Answer 7

Breakdown of the Complex Example
The text describes a hypothesis where three variables are linked:

Quality of discharge teaching (Variable 1)

Care coordination (Variable 2)

Readmission within 30 days (Variable 3)

The hypothesis stated that the first two variables would be inversely associated with the third. In plain English: as teaching quality and coordination go up, readmissions should go down.

Why “Three or More”?
In research, once you move past a single pair of variables, the statistical math and the conceptual complexity change.

Simple (2 variables): A straight line of logic.

Example: “App use leads to higher scores.”

Complex (3+ variables): A web of logic.

Example: “App use and care coordination lead to fewer readmissions.”

Outcome of the Lerret Study

Interestingly, while their simple hypothesis was supported (the app did improve teaching scores), this complex hypothesis was not supported. The researchers found only a weak association between those three variables, making the results non-significant.

Question 8

Complex hypothesis

Answer 8

Breakdown of the Complex Example
The text describes a hypothesis where three variables are linked:

Quality of discharge teaching (Variable 1)

Care coordination (Variable 2)

Readmission within 30 days (Variable 3)

The hypothesis stated that the first two variables would be inversely associated with the third. In plain English: as teaching quality and coordination go up, readmissions should go down.

Why “Three or More”?
In research, once you move past a single pair of variables, the statistical math and the conceptual complexity change.

Simple (2 variables): A straight line of logic.

Example: “App use leads to higher scores.”

Complex (3+ variables): A web of logic.

Example: “App use and care coordination lead to fewer readmissions.”

Outcome of the Lerret Study

Interestingly, while their simple hypothesis was supported (the app did improve teaching scores), this complex hypothesis was not supported. The researchers found only a weak association between those three variables, making the results non-significant.

Question 9

Non directional

Answer 9

Nondirectional Hypothesis
As your text mentions, this predicts that a relationship exists or a difference will occur, but it doesn’t specify exactly what that change will look like. It is often used when the researcher is exploring a new area where there isn’t much existing data to suggest a trend.

Keywords: “Differ,” “Relationship,” “Influence,” “Effect.”

Statistical Alignment: This corresponds to a two-tailed test because the researcher is looking for a result in either direction (increase or decrease).

Example from your text:

“Mean loneliness scores would differ based on diagnosis of depression.” (Note: It doesn’t say if people with depression are more or less lonely, just that they are different.)

Question 10

Describing the non directional expample

Answer 10

Mean loneliness scores would differ based on diagnosis of depression”—the researcher is just saying the two groups (Depressed vs. Not Depressed) won’t have the same score. They aren’t “betting” on which group is lonelier.

Question 11

Directional

Answer 11

The Directional Hypothesis
This type of hypothesis explicitly states the nature of the interaction between variables. You are no longer just looking for a “difference”; you are looking for a specific “tilt.”

How to spot it: Look for “power words” like positive, negative, less, more, increase, decrease, greater, higher, or lower.

The “Inverse” Example: In your text, researchers predicted that as the quality of teaching increases, the number of readmissions will decrease. This is a “negative” or “inverse” relationship—one goes up, the other goes down.

2. The Causal Hypothesis
   This is the “heavy hitter” of hypotheses. While a nondirectional hypothesis is often just associative, a causal hypothesis predicts that the independent variable is actually causing the change in the dependent variable.

Key Rule: All causal hypotheses are directional by nature. You can’t claim X causes Y without saying if it makes Y better or worse.

The Example: A study on stretching exercises (MSEP) predicted that the exercise group would score higher on self-efficacy than the control group. Because they used an experiment (RCT), they were looking for a causal effect.