Hypothesis Learning Outcome

Flipped Learning: Hypothesis

 Etymology of 'hypothesis'

• hypothesis (n.)

• 1590s, "a particular statement;"

• 1650s, "a proposition, assumed and taken for granted, used as a premise," from French hypothese and directly from Late Latin hypothesis, from Greek hypothesis 'base, groundwork, foundation," hence in extended use 'basis of an argument, supposition,"

• literally "a placing under," from hypo- "under" + thesis "a placing, proposition" (Harper, https://mv.v.et.vrnonline.corn)

Hypothetical as verb and adjective ...

• "to form hypotheses,"

1738, from hypothesis + ize. Hypothetize is an alternative form, preserving of the base.

Greek consonant

Related: Hypothesized; hypothesizing.

• The adjective hypothetical, meaning "having the nature of a hypothesis", or "being assumed to exist as an immediate consequence of a hypothesis", can refer to any of these meanings of the term "hypothesis"

Current usage of the term 'hypothesis'

• In common usage in the 21st century, a hypothesis refers to a provisional idea whose merit requires evaluation.

• For proper evaluation, the framer of a hypothesis needs to define specifics in operational terms.

• A hypothesis requires more work by the researcher in order to either confirm or disprove it.

• In due course, a confirmed hypothesis may become part of a theory or occasionally may grow to become a theory itself.

What is Hypothesis?

• A hypothesis is an educated guess or prediction about a relationship between variables.

• It is a statement that can be tested through scientific research.

• In scientific research a hypothesis is used to make predictions about what will happen under certain conditions.

• It is a proposed explanation for a phenomenon that is based on limited evidence and subject to further testing and verification.

Variables

• Variables are called variables because they vary, i.e. they can have a variety of values. Thus a variable can be considered as a quantity which assumes a variety of values in a particular problem.

• There are three main variables: independent variable, dependent variable and controlled variables.

• Independent variable - What you can decide to change in an experiment.

• Dependent variable - What you observe or measure

• Controlled variables - Things you keep the same —do not change

What is NOT a hypothesis?

• It is important to note that a hypothesis is not a fact or a proven theory.

• It is simply a starting point for further investigation. If the results of the research do not support the hypothesis, it may need to be revised or abandoned.

• It is not a question. It is simple statement. It may be complex state if the independent and dependent variables are more in number.

Richard Feynman & Ray Hilborn / Marc

• For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it.

• Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories.

• Even though the words "hypothesis" and "theory" are often used interchangeably, a scientific hypothesis is not the same as a scientific theory.

• As per the observations made by Feynman and Hilborn, a working hypothesis is a provisionally accepted hypothesis proposed for further research in a process beginning with an educated guess or thought.

The Function of Hypothesis

•- brings clarity to the research problem.

• - provides a study with focus. It tells us what specific aspects of a research problem to investigate.

•- informs what data to collect and what not to collect

•- enhances objectivity

•- enables to conclude specifically what is true or what is false (Falsifiability and Verificationism)

Falsifiability and Verificationism

• • Falsifiability is a standard of evaluation of scientific theories and hypotheses that was introduced by the philosopher of science Karl Popper in his book The Logic of Scientific Discovery.

• He proposed it as the cornerstone of a solution to both the problem of induction and the problem of demarcation.

•A theory or hypothesis is falsifiable (or refutable) if it can be logically contradicted by an empirical test that can potentially be executed with existing technologies.

• Verificationism, also known as the verification principle or the verifiability criterion of meaning, is the philosophical doctrine which maintains that only statements that are empirically verifiable (i.e. verifiable through the senses) are cognitively meaningful, or else they are truths of logic (tautologies).

• Verificationism thus rejects statements related to metaphysics, as well as fields such as theology, ethics and aesthetics, as "cognitively meaningless".

• Such statements may be meaningful in influencing emotions or behaviour, but not in terms of conveying truth value, information or factual content.

Video 2: Ph.D. Coursework - Hypothesis 02

Purpose of hypothesis in qualitative research and quantitative research

Qualitative

Qualitative research is used to formulate a hypothesis

 If you need deeper information about a topic you know little about, qualitative research can help you uncover themes. For this reason, qualitative research often comes prior to quantitative. It allows you to get a baseline understanding of the topic and start to hypotheses around correlation and causation.  

Quantitative research is used to test or confirm a hypothesis

Qualitative research usually quantitative. You need to have enough understanding about a in order to develop a hypothesis you can test. Since quantitative research is highly structured. You first need to understand what the parameters are and how variable they are in practice. This allows you to create a research outline that is controlled in all the ways that will high-quality data.

Analysis will also differ in both types of research

Qualitative

For qualitative data, you'll end up with data that will be highly textual in nature. You'll be reading through the data and looking for key themes that emerge over and over.

Quantitative

For quantitative data, you'll end up with a data set that can be analyzed, often with statistical software such as Excel, R, or SPSS. You can ask many different types of questions that produce this quantitative data, including rating(ranking questions, single-select, multiselect, and matrix table questions.

Interconnectedness - Research Questions and Hypothesis

Research Questions

A research question is what a study aims to answer after data analysis and interpretation.

Hypotheses

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. The research hypothesis makes a specific prediction about a new phenomenon or a formal statement on the expected relationship between an independent variable and a dependent variable. It provides a tentative answer to the question to be tested or explored.

Video 3: Ph.D. Coursework- Hypothesis Part 3

Hallmarks of Good Research Questions and Hypotheses

The sources outline key characteristics that define robust research questions and hypotheses:

·         Excellent Research Questions: Should be specific and focused. They are designed to integrate collected data and observations, serving to confirm or refute the subsequent hypotheses.

·         Good Hypotheses: Must be empirically testable. This concept of testability or falsifiability, referenced from Carl Popper, is fundamental. Good hypotheses should also be:

• Backed by preliminary evidence.
• Testable by ethical research.
• Based on original ideas.
• Supported by evidence-based logical reasoning.
• Predictable.

The Crucial Distinction: Quantitative vs. Qualitative Approaches

One of the most significant insights from the sources is the fundamental difference in how research questions and hypotheses function within quantitative versus qualitative research.

·         Quantitative Research: Typically involves fewer types of research questions but many types of quantitative research hypotheses.

o    Question Types: There are three main types: Descriptive, Comparative, and Relationship research questions. Descriptive questions measure responses to variables, comparative questions clarify differences between groups or effects of variables, and relationship questions define trends, associations, or interactions between dependent and independent variables.

o    Hypothesis Types: Quantitative research can yield almost a dozen different types. These include:

Simple Hypothesis: Predicts a relationship between a single dependent and single independent variable (e.g., higher dose of medication lowers blood pressure).

Complex Hypothesis: Predicts a relationship between two or more independent and dependent variables (e.g., multiple therapies increase survival rate).

Directional Hypothesis: Identifies a specific direction based on theory towards a particular outcome (e.g., privately funded projects have larger international scope than publicly funded ones).

Non-directional Hypothesis: Does not identify an exact study direction or nature of the relationship between variables (e.g., men and women are different in terms of helpfulness).

Associative Hypothesis: Describes variable interdependency, where a change in one causes a change in another (e.g., more vaccinations reduce infection incidence).

 Causal Hypothesis: Based on cause and effect, predicting an effect on a dependent variable from manipulating an independent variable (e.g., high fiber diet reduces blood sugar level).

Null Hypothesis: A negative statement indicating no relationship or difference between variables (e.g., no significant difference in reaction severity between a new drug and current drug). Framing a null hypothesis is often considered easier.

Alternative Hypothesis: Follows a null hypothesis, predicting a relationship between variables (e.g., the new drug is better on average than the current drug).

Working Hypothesis: Initially accepted for further research to produce a feasible theory (e.g., different formulations fed to cows produce different milk amounts).

Statistical Hypothesis: An assumption about a population parameter or relationship among characteristics, tested statistically (e.g., mean recovery rate is not significantly different between populations).

Logical Hypothesis: Proposes an explanation with limited or no evidence, based on logical connection (e.g., more education on contraception leads to fewer pregnancies).

Testable Hypothesis: The overarching requirement for quantitative hypotheses, linked to the use of deductive reasoning to test predictions against data.

  Qualitative Research: Contrasts sharply by having many types of research questions but typically leading to only one main type of hypothesis: hypothesis generating.

o    Question Types: A wide variety of question types are suited for qualitative research, often representing different modes of looking or approaches open to various interpretive strategies. Examples include:

 Contextual Research Question: Asks about the nature of existing phenomena or how individuals/groups function within their natural context (e.g., experiences of nurses working night shifts during Covid-19).

Descriptive Research Question: Aims to describe a phenomenon (e.g., different forms of disrespect experienced by women giving birth).

Evaluation Research Question: Examines the effectiveness of existing practices or frameworks (e.g., effectiveness of decisions in choosing birth location).

Explanatory Research Question: Clarifies a previously studied phenomenon and explains why it occurs (e.g., why teenage pregnancy is increasing).

Exploratory Research Question: Explores areas not fully investigated to gain deeper understanding (e.g., factors affecting mental health of medical students during Covid-19).

Generative Research Question: Develops in-depth understanding by asking "how would" or "what if" to identify problems and solutions (e.g., how extensive experience impacts success of new initiatives).

Ideological Research Question: Aims to advance specific ideas or ideologies (e.g., ability of nurses to promote humanised care).

Ethnographic Research Question: Clarifies people's nature, activities, interactions, and outcomes in specific settings (e.g., characteristics and outcomes of people with post-Covid complications).

Phenomenological Research Question: Seeks to understand phenomena that have impacted individuals (e.g., lived experiences of parents caring for children with autism).

Grounded Theory Question: Focuses on social processes, asking what happens and how people interact (e.g., problems pregnant adolescents face regarding social norms). These are particularly appropriate for literature research.

Qualitative Case Study Question: Assesses a phenomenon using different data sources to answer "why" and "how" questions, considering contextual influence (e.g., how changing roles impacts women's lives).

o    Hypothesis Type: The primary outcome is hypothesis generating. Qualitative research uses inductive reasoning, collecting data to develop formal hypotheses that may then serve as a framework for testing, potentially in a future quantitative study. The conclusions may lead to theoretical propositions or generate new hypotheses for further testing with different variables.

Identifying Your Research Type First

This detailed breakdown underscores why it is essential to first identify whether your research is qualitative or quantitative before you even begin formulating questions and hypotheses. Your research type will dictate the nature and function of these core elements.

Video 4: Ph.D. Coursework - Hypothesis framework and formation 04

Essential Frameworks for Robust Research

Before you even begin writing your questions and hypotheses, it's vital to ensure your research idea is sound. The sources introduce several frameworks to test the quality of your research plan:

·         The FINER Criteria: This framework helps assess if your research is Feasible, Interesting, Novel, Ethical, and Relevant. Applying this test helps ensure your research question and hypothesis are appropriate and viable.

PICOT/PEO Frameworks: Often used in specific research areas, these frameworks provide structure.

PICOT: Breaks down a research question into Population, Intervention (or Indicator), Comparison group, Outcome of Interest, and Time frame.

PEO: Focuses on Population, Exposure to pre-existing conditions, and Outcome of Interest.

·         FINER MAPS: An expanded framework encompassing Feasibility, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value, Publishable, and Systematic. This provides a comprehensive checklist from the feasibility to the systematic nature of your research question or hypothesis.

Six Steps to Constructing Effective Research Questions and Hypotheses

The sources outline a clear, step-by-step process for moving from a broad idea to specific questions and predictions:

1. Clarify the background: Start by understanding the existing knowledge related to your area of interest. This might stem from previous studies or unanswered questions from earlier academic work.
2. Identify the research problem: Pinpoint the specific issue or gap in knowledge you intend to address within a defined time frame.
3. Review or conduct preliminary research (Literature Review): Engage in extensive literature review to gather all available knowledge, studying theories and previous studies. This is crucial for identifying the research gap. If the problem is answered in the literature, further research might not be needed.
4. Construct research questions: Based on the identified problem and gap, formulate the questions you will investigate. These are the stepping stones to your hypotheses. You also need to identify the necessary variables at this stage and define constructs operationally.
5. Construct specific deductive or inductive predictions (Hypotheses): Formulate specific predictions in the form of hypotheses, which can be either deductive (for quantitative) or inductive (for qualitative leading to hypothesis generation).
6. State the study aims: Clearly articulate the objectives and aims of your study.

Avoiding Ambiguity: Lessons from Case Studies

The video highlights common pitfalls in formulating research questions and hypotheses by examining ambiguous examples. Points to be avoided include:

·         Vague and unfocused questions.

·         Closed questions that can be answered simply with yes or no.

·         Questions requiring only a simple choice.

·         Undeniable hypotheses that cannot be falsified (linking back to Carl Popper's concept).

·         Incompletely stated group comparisons.

·         Insufficiently described variables or outcomes.

·         Statements simply expressing facts without testable claims.

·         Research objectives that are unrelated to the questions and hypotheses.

·         Objectives that are unattainable or unexplorable.

Checking your research question, hypothesis, and research objective against these points can help identify and fix simple mistakes.

The General Flow

The six construction steps essentially form a general flow for developing effective research questions and hypotheses prior to conducting research. It moves from understanding the background and identifying the problem to reviewing literature, formulating questions, developing hypotheses, and finally stating the aims. Finding the research gap through literature review is a critical point that facilitates constructing effective research questions.

Algorithmic Approaches: Quantitative vs. Qualitative

Perhaps one of the clearest takeaways is the side-by-side algorithm presented for quantitative and qualitative research. This visually reinforces the fundamental differences discussed in Part 3.

·         Quantitative Research Algorithm:

• Select a topic.
• Clarify background information.
• Identify/state the research problem.
• Formulate research questions.
• Develop hypothesis to predict outcomes. (Notice hypotheses come after questions)
• Specify study aims.
• Formulate a plan to test/verify hypothesis.
• Collect and analyse data.
• Verify hypothesis based findings.
•  Make final conclusions.
• State recommendations.

·         Qualitative Research Algorithm:

• Make observations or note a lack of background in an unknown or unclear area.
•  Select a topic of interest or importance.
• Identify need or gap in the unknown/unclear area.
•  Clarify background information.
• Formulate research questions to investigate a research problem. (Questions are central early on)
• State the study aims.
• Choose methods, sites, subjects for research.
• Collect and analyze data.
• Complete the work and look for concepts and theories. (This is where hypothesis generating happens)
• Revise research questions if necessary or begin to form hypothesis (hypothesis generation).
• Complete conceptual framework and make conclusions.

This comparison vividly illustrates that quantitative research is driven by testing pre-defined hypotheses derived from questions, while qualitative research is driven by exploring questions to generate concepts, theories, and ultimately, hypotheses for potential future testing. It is therefore paramount to first identify whether your research is quantitative or qualitative.