Week 2 Hypothesis Structuring from Slides Flashcards
To: Review of structuring a hypothesis
What are the key components regarding the literature review are required when setting out my initial research question?
- know the research area well
- Read methods & results sections in each article for literature review.
- Ask what’s been done before, how was it done, what were the results & why does it require clarification &/or replication. Is there a gap?
Once I have reviewed the literature, what are the key components required when structuring my research design?
- What am I trying to ascertain - What unique question do I hope to answer
- Can the aim or purpose of my research be measured
- how will you go about constructing the research design? -is the question testable?
- How will I operationalize variables.
- Are instruments reliable & valid?
Why is a good hypothesis essential?
*A good hypothesis underpins good research design & analysis. Carefully worded it will create less stress at the analysis stage.
What does Neuman (2011) tell us about hypothesis testing?
- Knowledge rarely advances by the testing a single hypothesis but rather knowledge develops over time
- Each hypothesis represents an explanation & if evidence fails to support the hypothesis, they’re gradually eliminated from further research in the area.
- However, even if support is obtained then it’s only support given to that proposition, it is never proven
- The principle of replication: hypotheses require repeated support before gaining broad acceptance
What is the purpose of a hypothesis?
Hypothesis are used to test the direction and strength of a relationship between variables in a correlational design, for example
Neuman & Karl Popper both discuss falsification testing of null & alternative hypothesis (a.k.a. an experimental hypothesis), With Neuman viewing hypotheses as links in a “causal chain”. How do researchers treat evidence from a hypothesis?
Researchers treat evidence that supports a hypothesis differently from evidence that opposes it.
*However, identifying negative evidence is critical when evaluating the hypothesis. Linked to logic of the disconfirming hypotheses.
What does Neuman say the logic of the disconfirming hypothesis (which is the logic of the null hypothesis) is based on?
The idea that confirming empirical evidence makes a weak case for the existence of a relationship; instead of gathering supporting evidence, testing that no relationship exists provides more cautious, indirect support for its possible existence
Okay, so what exactly is a null hypothesis (H0)?
- “A hypothesis stating that there is no significant effect of an independent variable on a dependent variable” (Neuman, 2011, p. 183)
- Statistically this is what is tested in the analysis.
And what is the alternative (H1) hypothesis testing?
- The alternative hypothesis is paired with the null hypothesis.
- It states that an independent variable has a significant effect on a dependent variable
I have heard that Double-Barrelled hypothesis are a bad thing, what is one?
“A confusing and poorly designed hypothesis with two independent variables in which it is unclear whether one or the other variable or both in combination produce an effect” (Neuman, 2011, p. 183)
What does Hills have to say on the topic of hypothesis testing?
- Research needs to be based on a clear & concise research question.
- Reference to existing theory leads to the expression of the research question as a hypothesis.
- This is a tentative statement about the relationship between two or more variables.
- The aim of the research is then to test the research hypothesis, by finding evidence that either supports or refutes it.
What does Hills say about the possible relationships between variables?
Variables can be:
- positively related (as one increases the other increases), -negatively related (as one increases the other decreases)
- or unrelated (changes in one are not associated with any predictable change in the other).
- In experimental research causal relationships are investigated by looking for differences between groups treated differently.
What are some common logical errors to avoid when developing a good explanation for any theory?
- Tautology
- Teleology
- Ecological Fallacy
- Reductionism
- Spuriousness (false, mirage)
What is Tautology?
- Circular Reasoning
- An explanation error where the causal factor and the result are actually the same or restatements of one another, making an apparent causal relationship true by definition
- E.g.: Poverty is caused by having limited finances.
What is Teleology?
- Intention is inappropriate or there is a misplaced temporal ordering
- An explanation error where the causal relationship is empirically untestable as the causal factor does not come earlier in time than the result
- or the causal factor is a vague general force that cannot be empirically measured
- E.g.: People get married in religious ceremonies because of societal rules.