dependent and independent variable in research

variable in research

1. Variables Research is all about variables! What is a variable? A variable is a quantity or quality that can be different at different times or indifferent places. In psychology we are mainly interested in properties that vary from person to person or within the same person at different times. These could include: age; race; gender; number of hours slept each night; size of cerebral hemispheres; level of awareness; type of brain damage; capacity of short-term memory; learning ability; type of psychological illness – the list is really endless.

2. Every experiment has at least one independent and one dependent variable. An independent variable (IV) is deliberately manipulated or varied in some way by the experimenter. This is planned before the experiment begins. Simple experiments use one independent variable with two values (male/female; yes/no) – in the research by Kearins it was Aboriginal Australians/non-Aboriginal Australians. In a more complex experiment the IV could be continuous – that is, it could have a range of values on a scale; for example, age, body mass, IQ, blood alcohol content (BAC), optimism.

3. The dependent variable (DV) is the property that is measured in the research. Its value depends on the IV and that is why it is called ‘dependent’. The DV is therefore the property that the researcher believes will change as a result of changes in the value of the IV. The DV is usually continuous (that is, has any value within a certain range) and should be stated as an operational definition.

4. Examples of independent and dependent variables Research Question Independent variable(s) Dependent variable(s) Do tomatoes grow fastest under fluorescent, incandescent, or natural light? The type of light the tomato plant is grown under The rate of growth of the tomato plant What is the effect of diet and regular soda on blood sugar levels? The type of soda you drink (diet or regular) Your blood sugar levels How does phone use before bedtime affect sleep? The amount of phone use before bed Number of hours of sleep Quality of sleep How well do different plant species tolerate salt water? The amount of salt added to the plants’ water Plant growth Plant wilting Plant survival rate

5. The independent variable is the variable the experimenter manipulates or changes, and is assumed to have a direct effect on the dependent variable. For example, allocating participants to either drug or placebo conditions (independent variable) in order to measure any changes in the intensity of their anxiety (dependent variable). In an experiment, the researcher is looking for the possible effect on the dependent variable that might be caused by changing the independent variable.

6. OPERATIONAL DEFINITIONS Operationalisation of a variable means that it is stated in terms that show how it is measured. For example: - age: operationalised as age in total months - IQ: operationalised as the score on a 40- item multiple choice test - agression operationalised as the number of agressive responses in an observed 30- minute period.

7. Measurement Scien ti sts use two type of measurements to record the careful and controlled observa ti ons that characterize the scien tifi c method: Physical measurement – involves dimensions for which there is an agreed-upon standard and an instrument for doing the measuring. E.g. length. Psychological measurement – for measuring constructs that can’t be measured physically, e.g. aggression, intelligence. In a sense, the human observer is the instrument for psychological measurement. E.g. if several independent observers agree that a certain ac ti on warrants a ra ti ng of 3 on a 7-point ra ti ng scale, that is a psychological measurement of the ac ti on

8. Measurements must be valid and reliable: Validity = refers to the “truthfulness of a measure. Does the measure of a construct measure what it claims to measure? Reliability = the consistency of the measurement. - Instrument reliability = whether an instrument works consistency .

9. Hypotheses A hypothesis is a tenta ti ve explana ti on for a phenomenon. They frequently try to answer the ques ti ons “How?” and “Why?” At one level, a hypothesis may simply suggest how par ti cular variables are related. At a theore ti cal level, a hypothesis may o ff er a reason (why) for the way par ti cular variables are related. One characteris ti c that dis ti nguishes casual, everyday hypotheses from scien tifi c hypotheses is testability. If a hypothesis cannot be tested it is not useful to science. Three types of hypotheses fail to pass the “testability test”. A hypothesis is not testable when: Its constructs are not adequately de fi ned or measured. The hypothesis is circular – when an event itself is used as an explana ti on of the event. The hypothesis appeals to ideas not recognized by science – science deals with the observable, the demonstrable, the empirical.

10. GOALS OF THE SCIENTIFIC METHOD The scien tif c method is intended to meet four goals: descrip ti on, predic ti on, explana ti on, and applica ti on.

11. Description Psychologists seek to describe events and rela ti onships between variables. Researchers can use nomothe ti c or ideographic approaches. Nomothe ti c is most common. - The nomothe ti c approach = psychologists try to establish broad generaliza ti ons and general laws that apply to a diverse popula ti on. Researchers seek to describe the “average,” or typical, performance of a group. - Ideographic approach = researchers study the individual rather than groups.

12. Researchers can use quan ti ta ti ve or qualita ti ve analysis. Quan ti ta ti ve is most common. - Quan ti ta ti ve research = refers to studies in which the fi ndings are mainly the product of sta ti s ti cal summary and analysis. - Qualita ti ve research = produces verbal summaries of research fi ndings with few sta tisti cal summaries or analysis. The data of qualita ti ve research are most commonly obtained from interviews and observa ti ons and can be used to describe individuals, groups, and social movements. Central to qualita ti ve research is that inves ti gators ask par ti cipants to describe their experiences in ways that are meaningful to them, rather than to use categories established by theorists and previous research.

13. Quantitative approaches Attempts to explain phenomena by collecting and analysing numerical data Tells you if there is a “difference” but not necessarily why Data collected are always numerical and analysed using statistical methods Variables are controlled as much as possible (RCD as the gold standard) so we can eliminate interference and measure the effect of any change Randomisation to reduce subjective bias If there are no numbers involved, its not quantitative Some types of research lend themselves better to quant approaches than others

14. Quantitative data Data sources include Surveys where there are a large number of respondents ( esp where you have used a Likert scale) Observations (counts of numbers and/or coding data into numbers) Secondary data (government data; SATs scores etc ) Analysis techniques include hypothesis testing, correlations and cluster analysis

15. What quant itative researchers worry about Is my sample size big enough? Have I used the correct statistical test? have I reduced the likelihood of making Type I and/or Type II errors? Are my results generalisable ? Are my results/methods/results reproducible? Am I measuring things the right way?

16. What’s wrong with quant itative research? Some things can’t be measured – or measured accurately Doesn’t tell you why Can be impersonal – no engagement with human behaviours or individuals Data can be static – snapshots of a point in time Can tell a version of the truth (or a lie?) “Lies, damned lies and statistics” – persuasive power of numbers

17. Qualitative approaches Any research that doesn’t involve numerical data Instead uses words, pictures, photos, videos, audio recordings. Field notes, generalities. Peoples’ own words. Tends to start with a broad question rather than a specific hypothesis Develop theory rather than start with one  inductive rather than deductive

18. Gathering qualitative data Tends to yield rich data to explore how and why things happened Don’t need large sample sizes (in comparison to quantitative research) Some issues may arise, such as Respondents providing inaccurate or false information – or saying what they think the researcher wants to hear Ethical issues may be more problematic as the researcher is usually closer to participants Researcher objectivity may be more difficult to achieve

19. Sources of qualitative data Interviews (structured, semi-structured or unstructured) Focus groups Questionnaires or surveys Secondary data, including diaries, self-reporting, written accounts of past events/archive data and company reports; Direct observations – may also be recorded (video/audio) Ethnography

20. What qualitative researchers worry about Have I coded my data correctly? Have I managed to capture the situation in a realistic manner? Have I described the context in sufficient detail? Have I managed to see the world through the eyes of my participants? Is my approach flexible and able to change?

21. What’s wrong with qualitative research? It can be very subjective It can’t always be repeated It can’t always be generalisable It can’t always give you definite answers in the way that quantitative research can It can be easier to carry out (or hide) ‘bad’ (poor quality) qual research than ‘bad’ quant research

22. Qualitative Quantitative The aim of qualitative analysis is a complete detailed description. In quantitative research we classify features, count them, and construct statistical models in an attempt to explain what is observed. The design emerges as the study unfolds All aspects of the study are carefully designed before data is collected. Researcher is the data gathering instrument. Researcher uses tools (questionnaires or equipment) to collect data. Data is in the form of words (interviews), pictures (videos), or objects (artifacts). Data is in the form of numbers and statistics. Qualitative data is more rich, time consuming, and less able to be generalized. Quantitative data is more efficient, able to test hypotheses, but may miss contextual data.