Research Questions & Hypotheses
Philosophy of Science
An empirical approach to knowledge sits within a positivistic view of the world, which assumes:
- The world is made up of bits of data
- This data can be systematically ‘measured’, ‘recorded’ and 'analysed'
- Interpretation can lead to valid and useful insights about how people think, feel and behave
An empirical research approaches should be distinguished from:
Some quotes about the positivistic, empirical approach to knowledge-development:
- "If you are a scientist you believe that it is good to find out how the world works, that it is good to find out what the realities are, that it is good to turn over to mankind at large the greatest possible power to control the world... It is not possible to be a scientist unless you believe that the knowledge of the world, and the power which this gives, is a thing which is of intrinsic value to humanity, and that you are using it to help in the spread of knowledge, and are willing to take the consequences."
- J. Robert Oppenheimer (1904-1967)
- “What are the facts? Again and again and again - what are the facts? Shun wishful thinking, ignore divine revelation, forget "what the stars fortell", avoid opinion, care not what the neighbors think, never mind the unguessable "verdict of history“ - what are the facts, and to how many decimal places? You pilot always into an unknown future; facts are your single clue. Get the facts!”
- the notebooks of Lazarus Long, Robert Heinlein “Time Enough for Love”
- "I believe there is no philosophical high-road in science, with epistemological signposts. No, we are in a jungle and find our way by trial and error, building our road behind us as we proceed."
- Max Born (1882-1970)
This is an iterative (cyclical) model of the research process:
- Need for information/research
- Define research problem -> Establish Research Question
- Define target constructs
- Establish Hypotheses
- Operationalise constructs (minimise measurement error)
- identify target population & sampling frame
- choose sampling technique (minimise sampling error)
- Collect data (mode of administration)
- Analyse -> interpret -> write report / feedback
- Should be stated as a question, e.g., "Is there a relationship between a person's age and their favourite day of the week?"
- Should involve the relationship or difference between two or more variables (i.e., an independent and a dependent variable), e.g., IV = age, DV = favourite day of the week.
- In the Introduction, you should clearly define each of the target constructs (IVs and DVs) and in the Method explain how each of them is operationalised (measured).
- Introduce the RQ within the first two pages of the Introduction, then go on to review relevant theoretical and research literature, and then restate/justify the RQ towards the end of the introduction and use this to lead in to the statement of hypotheses.
- Should relate to the research literature and a problem/issue to be solved.
- Serves to provide an overall focus the study - it is the study's goal.
- Leads into specific, testable hypotheses.
- Follows on from the overall RQ(s).
- A clear, testable statement, not a question.
- Concise and to the point.
- Is readily understood by others.
- States specific predictions.
- Usually in future tense.
- Each hypothesis should be able to be tested via one analysis (or one set of related analyses).
- Identifies specific relationships between variables.
- Can be:
(e.g., It is predicted that female participants will nominate their favourite sense as smell more frequently than male participants.) or
(e.g., It is predicted that female and male participants will differ in the frequency with which they nominate smell as their favourite sense).
- Technically, each hypothesis should be stated using:
- null (H0) and
- alternative hypotheses (H1)
- In practice, social science researchers often just state H1.
- Number or letter each of your hypotheses (e.g., 1, 2, 3; 1a, 1b, 2a, 2b) and use this as organising device for your Results and Discussion.
- For the lab report, you should have at one hypothesis for each of the major analyses you undertake (and more likely several hypotheses for each of the ANOVA and MLR analyes).
- Sometimes, e.g., for exploratory research or qualitative research, a RQ may not lend itself to having an accompanying hypothesis - in this case, just ask a RQ.
Your lab report should probably be based around one or two central research questions (RQs). To start off with, caste a wide net and generate at least half a dozen possible RQs. You may want to write down all the variables in the study.
You may be able to generate useful questions simply by looking at the variables, the questionnaire and possibly the data itself (but watch out for data snooping!), but it is recommended that you start off by familiarising yourself with the topics pursued in the readings on the motivation and satisfaction of university students. It would also be helpful to become familiar with the factor structure of the instrument. Your brainstormed RQs could then emerge from:
- What you've observed/experienced/heard
- Theory (lit review)
- Research (lit review)
Also try to develop some possible hypotheses for each of your RQs - this could be revealing - you might find that its difficult to establish hypotheses for some of your RQs.
Whittle the questions down, e.g., consider:
- Is it an important/useful question?
- Am I interested in the question?
- Will the available data allow me to tackle this question?
- Will the questions lead to hypotheses which can be tackled via MLR, Advanced ANOVA, and qualitative analysis?
It is recommended that you show your RQ to your tutor before finally deciding. Your tutor might ask questions like:
- What is/are the DV(s)?
- What is/are the IVs(s)?
- Define each of the DVs and IVs.
- What are your hypotheses?
- What analyses will you conduct?
- What type of research are you conducting? e.g.,
- Information gathering
- Theory building/testing
- What is the level of measurement for each of the variables? (Will any recoding be required?)
Interesting questions tend to:
- Test a novel relationship, e.g.
- “is time spent studying for exams associated with increased incidence of brain cancer?”
- “is hemline length related to the Dow Jones index?” (.85)
- Avoid simply showing an expected relationship, e.g.
- “is time spent studying studying for exams associated with higher exam marks?”
- “is time related to the Dow Jones index?”
- "What is the effect of sport involvement on adolescents’ physical self-concept?"
- "What personal and social factors are associated with successful attempts at major life change?"
- "H1: Older people will report more positive attitudes towards smoking than younger people.” [differences]
- “H1: There will be a positive linear relationship between attitudes to smoking & age, such that as age increases attitudes become more positive.” [correlational]
- “H1: It is predicted that there will be a positive relationship between self-esteem and academic performance, such that as self-esteem increases academic performance will also increase.” [correlational]
An essential component of the scientific process is the formulation and evaluation of hypotheses. In seeking to learn more about the social world, social scientists ask many different kinds of questions about relationships between factors of social life. How do investors change their behavior when market conditions change? What role did political and social factors play in the Salem witch trials? Do feelings of connectedness influence students' performance in school? To address these questions, social scientists form hypotheses which they then evaluate using some form of data.
You may be familiar with examples of hypotheses and hypothesis testing from the natural sciences, perhaps through schoolwork or participation in a school science fair. You may have evaluated hypotheses such as:
- The combination of certain chemical compounds yields heat energy.
- Plants' growth is enhanced through exposure to ultraviolet light.
- When a moving object collides with another object, the total kinetic energy of the two objects does not change.
Typically, hypotheses such as these are generated from some theory or theoretical perspective, then evaluated using data collected through some laboratory procedures. Research in the social sciences works similarly (though often outside the laboratory). This module is designed to introduce you to hypotheses in the social sciences.
What is a Hypothesis?
A hypothesis is an empirically-testable statement about a relationship involving two or more variables. Examples of hypotheses from the social sciences include:
- Investors seek low-risk investments in economic downturns.
- The Salem witch trials were an expression of tension of political and social power in that community.
- Students' feelings of connectedness to school are an essential element of their academic success.
Each of these specifies a relationship that may or may not exist under particular conditions. They are testable statements about relationships between different factors. But why bother with forming a hypothesis as part of the research process?