I. Why Are Research Methods Important?

Science, at a basic level attempts to answer questions (such as "why are we aggressive") through careful observation and collection of data. These answers can then (at a more complex or higher level) be used to further our knowledge of us and our world, as well as help us predict subsequent events and behavior. But, this requires a systematic/universal way of collecting and understanding data -- otherwise there is chaos.

At a Practical level, methodology helps US understand and evaluate the merit of all the information we're confronted with everyday. For example, do you believe in the following studies?

2) study demonstrated a link between smoking and poor grades.

There are many aspects of these studies that are necessary before one can evaluate the validity of the results. However, most people do not bother to find out the details (which are the keys to understanding the studies) but only pay attention to the findings, even if the findings are completely erroneous.

They are also practical in the work place:

1) Mental Health Profession - relies on research to develop new therapies, and learn which therapies are appropriate and effective for different types of problems and people.

2) Business World - marketing strategies, hiring, employee productivity, etc.

1) Basic Research answers fundamental questions about the nature of behavior. Not done for application, but rather to gain knowledge for sake of knowledge.

Some people erroneously believe that basic research is useless. In reality, basic research is the foundation upon which others can develop applications and solutions. So while basic research may not appear to be helpful in the real world, it can direct us toward practical applications.

2) Applied Research is concerned with finding solutions to practical problems and putting these solutions to work in order to help others.

Today, there is a push to more applied research. This is no small part due to the perspective in Canada where we want solutions and we want them now! BUT, we still need to keep our perspective on the need for basic research.

3) Program Evaluation look at existing programs in such areas as government, education, criminal justice, etc., and determine the effectiveness of these programs. DOES THE PROGRAM WORK?

For example - Does capital punishment work? Think of all the issues surrounding this program and how hard it is to examine its effectiveness. The most immediate issue, how do you define the purpose and "effectiveness" of capital punishment? If the purpose is to prevent convicted criminals from ever committing that same crime or any other crime, than capital punishment is an absolute - 100% effective. However, if the point of capital punishment is to deter would-be criminals from committing crimes, then it is a completely different story.

We all observe our world and make conclusions. HOW de we do this:

1) seek an authority figure - teacher tells you facts...you believe them. Is this such a good idea? For example, if your teacher tells you that there is a strong body of evidence suggesting that larger brains = greater intelligence.

2) intuition -

a) Are women are more romantic then men?

b) Is cramming for an exam is the best way to study?

Whatever your opinion, do you have data to support your OPINIONS about these questions???

Luckily, there is a much better path toward the TRUTH...the Scientific Method.

IV. THE SCIENTIFIC METHOD

How do we find scientific truth? The scientific method is NOT perfect, but it is the best method available today. To use the scientific method, all topics of study must have the following criteria:

A. Goals of the Scientific Method

1) Description - the citing of the observable characteristics of an event, object, or individual. Helps us to be systematic and consistent. This stage sets the stage for more formal stages - here we acquire our topic of study and begin to transform it from a general concept or idea into a specific, testable construct. In stating the hypothesis, the variables of interest need to be clearly defined. The variables should be operationally defined

If you are studying aggression, you might define aggression as the number of times your subjects bop each other on the head. Thus, aggression - a very abstract concept - has been clearly defined for the purposes of your study.

An important thing to notice here is that operational definitions do not mean that the thing being defined is equal to what it is being defined as. That is, head bops are not equal to aggression. Aggression can be - and is - more than head bopping, and head bopping is not necessarily aggessive.

This may seem obvious to you, but there are some people who do studies on abstract things that are operationally defined, and then make broad generalizations about those abstract things, which may not be appropriate.

For example, the person who studies aggression and finds that people are more likely to bop each other on the head after watching a Jean-Claude Van Damme movie than after watching "The Sound of Music" may claim that Van Damme movies lead to more aggression. Although the Van Damme film does objectively contain more violent and aggressive actions than "The Sound of Music," it may be that in the Van Damme film was really bad, but contained a scene in which Van Damme gets his head bopped by a female character; the people who see the film make fun of it by re- enacting that head bopping scene.

It is important to define what you are talking about in your research, but the phenomenon that you are interested in is likely bigger than the definition that you give it. Do not be fooled into believing that an operational definition is all there is to say about a concept. This is especially common in thinking about concepts which can be measured by some psychological test. For example, an IQ test is not all there is to intelligence. It may be part of what intelligence is about, but it is not all of it. This applies to all other psychological concepts.

2) Prediction - here we formulate testable predictions or HYPOTHESES (specifically, about our variables). Thus, we may define a hypothesis as a tentative statement about the relationship between two or more variables. For example, one may hypothesize that as alcohol consumption increases driving ability decreases. Some people suggest the primary activity of science is hypothesis testing.

3) Select Methodology & Design - chose the most appropriate research strategy for empirically addressing your hypotheses.

4) Control - method of eliminating all unwanted factors that may effect what we are attempting to study (we will address in more detail later).

5) Collect Data - data collection is simply the execution and implementation of your research design.

6) Analyze & Interpret the Data - use of statistical procedures to determine the mathematical and scientific importance (not the "actual" importance or meaningfulness) of the data. Were the differences between the groups/conditions large enough to be meaningful (not due to chance)?

Then, you must indicate what those differences actually mean...discovery of the causes of behavior, cognition, and physiological processes.

7) Report/Communicate the Findings - Science is based on sharing - finding answers to questions is meaningless (to everyone except the scientist) unless that information can be shared with others. We do this through publications in scientific journals, books, presentations, lectures, etc.

B. Ways of Conducting Scientific Research

1) Naturalistic Observation - As the name suggests, naturalistic observation involves observing behavior as it occurs in its natural environment without interference or intervention by the researcher. For example, observing children in a classroom, observing sibling interaction in their homes, or observing animals in their natural habitat.

weaknesses: often not easy to observe without being intrusive.

strengths: study behavior in real setting - not lab.

2) Case Study - in depth investigation of an individual's life(or case), used to reconstruct major aspects of a person's life. Attempt to see what events led up to current situation.

Usually involves: interview, observation, examine records, & psych. testing..This is the method that Freud used in developing his theories of personality and that Piaget used in developing his theories of human development. Present day cognitive neuroscience still uses often this technique. Much can be learned from case studies, but any generalizations from them should be cautiously made.

weaknesses: very subjective. Like piecing together a puzzle, often there are gaps - relies on memory of the individual, medical records, etc. Any one individual can be an anomaly, or a unique case

strengths: good for assessing psychological disorders - can see history and development.

3) Survey - either a written questionnaire, verbal interview, or combination of the two, used to gather information about specific aspects of behavior. Many people are asked a few questions about some particular aspect of their life, or for their opinion on some aspect of the world. This is a good method of gathering data from many different people in a short span of time. There are several factors to consider when making a survey or interpreting survey results: (i) who was surveyed and how many of them were surveyed? representativeness is important, and the more people surveyed the better (e.g., 3 of 4 dentists surveyed....) (ii) what was the wording of the survey questions? the wording of questions asked of people can influence their answers. also, the order in which the questions are asked can make a difference. Finally, (iii) you have to trust that how people have responded to the survey is in fact the truth; people's responses may not be what is actually the case because they are delibrately misleading you, or because they are erroneously recalling the relevant information.

weaknesses: self-report data (honesty is questionable)

strengths: gather a lot of information in a short time.

gather information on issues that are not easily observable.

4) Psychological Testing - provide a test and then score the answers to draw conclusions from. Examples. - I.Q. tests, personality inventories, S.A.T., G.R.E., etc...

weaknesses: validity is always a question; honesty of answers.

strengths: can be very predictive and useful if valid.

5) Experimental Research (only way to approach Cause & Effect) - method of controlling all variables except the variable of interest which is manipulated by the investigator to determine if it affects another variable. From the previous methods of study, NO conclusions can be made about cause and effect. No conclusions about cause and effect can be made, because nothing has been manipulated. Manipulation is simply the introduction of an element into a situation for some people, and not for others. Only through manipulation can there be clear and conclusive statements of cause and effect among the elements in the study.

Both types of statistics come into this, because descriptive stats (usually the mean and standard deviation) will be used to summarize the data collected from the participants in each condition. Then inferential statistics will be used to compare the conditions to determine whether they are the same or different (the notion of statisical significance). Depending on the outcome of the inferential statistics, we can say that our hypothesis is supported or not supported.

A variable is any measurable condition, event, characteristic, or behavior that can be controlled or observed in a study.

The variables in the experimental design that the experimenter manipulates are the independent variables. These are the variables that researchers control and manipulate so that the phenomenon of interest is clearly and effectively studied. Independent variables are independent of the participants in the study. Study participants are not asked what condition they want to be in, rather they are assigned to a condition by the researcher. The dependent variables are what is measured, so dependent variables are the participants responses in the experiement. Those measures are dependent on the participants. Examples include test scores, percentage correct, and reaction time. The other factors in the experiment are the extraneous variables, which may have an effect on the study. Researchers try to control them by keeping them constant in each study condition. For individual differences among the participants, researchers will control that through randomization, although it may also be achieved through exclusion. For example, if a study involves responding to colours on a computer screen, then an experimenter would want to exclude anyone who is colour blind. We assume that amoung groups the intelligence of the subjects is controled through random assignment of the subjects to the groups.

Also, sometimes there are control conditions, in which the independent variable has no value. These are done to see if the independent variables are really having the effect or if the effect would be achieved without them.

For example - how quickly can rats learn a maze (2 groups). What to control?

  Groups (of subjects/participants) in an Experiment may include:

experimental group - group exposed to the IV in an experiment.

control group - group not exposed to IV. This does not mean that this group is not exposed to anything, though. For example, in a drug study, it is wise to have an experimental group (gets the drug), a placebo control group (receives a drug exactly like the experimental drug, but without any active ingredients), and a no-placebo control group (they get no drug...nothing)

both groups must be treated EXACTLY the same except for the IV.

A Confound occurs when any other variable except the IV affects the DV (extraneous variable) in a systematic way. In this case, what is causing the effect on the DV? Unsure.

Many things may lead to confounds (here are just two examples):

a) Experimenter Bias - if the researcher (or anyone on the research team) acts differently towards those in one group it may influence participants' behaviors and thus alter the findings. This is usually not done on purpose, but just knowing what group a participant is in may be enough to change the way we behave toward our participants.

b) Participant Bias - participants may act in ways they believe correspond to what the researcher is looking for. Thus, the participant may not act in a natural way.

c) Observer Effects - occur when the performance of a subject being studied can be attributed to the presence of the experimenters or the observers.

Back in the 1920s, a Western Electric Company plant in Chicago - the Hawthorne Works - was used in a study to assess how the workplace could be improved to increase production. Specifically, researchers were interested in relationship between illumination levels and production. Thus, the researchers chose a particular part of the plant where some women were assembling part of the automobile.

The intital findings were that when the illumination in the workplace increased there was more productivity. Then the researchers decreased the illumination levels to verify their results. They found that as the illumination levels decreased productivity still increased. The reason for the increased productivity for both increases and decreases in illumination is that the workers were getting more attention than before. In the '20s, the average worker wasn't really cared for. Management didn't really give a damn, and there were few governmental policies regarding the treatment of workers in the workplace. When several researchers attended to these workers, caring about how they were performing, the workers responded by working more productively. That was also the case for the control group, where the illumination was not changed.

7). Types of Experimental Designs: true experiment, & correlation.

a) The True Experiment: Attempts to establish cause & effect

To be a True Experiment, you must have BOTH - manipulation of the IV & Random Assignment (RA) of subjects/participants to groups.

Manipulation of the IV - manipulation of the IV occurs when the researcher has control over the variable itself and can make adjustments to that variable.

For example, if I examine the effects of Advil on headaches, I can manipulate the doses given, the strength of each pill, the time given, etc.. But if I want to determine the effect of Advil on headaches in males vs females, can I manipulate gender? Is gender a true IV?

Random Assignment - randomly placing participants into groups/conditions so that all participants have an equal chance of being assigned to any condition.

c) Correlation: attempts to determine how much of a relationship exists between variables. It can not establish cause & effect. CORRELATION DOES NOT IMPLY CAUSATION. When we get a correlation, we are tempted to infer causality. The human mind does not believe in correlation, it believes in causal relations.

To show strength of a relationship we use the Correlation Coefficient (r). The coefficient ranges from -1.0 to +1.0:

-1.0 = perfect negative/inverse correlation

+1.0 = perfect positive correlation

0.0 = no relationship

positive correlation- as one variable increases or decreases, so does the other. Example. studying & test scores, parental income and years of education (i.e., as parental income goes up, years of education goes up)

negative correlation - as one variable increases or decreases, the other moves in the opposite direction. Example. as food intake decreases, hunger increases, parental income and dental problems (i.e., as parental income goes up, dental problems goes down)

More Examples:

- GPA and reading test scores? Positive relationship: (r = +0.60) Note that r = +0.6 is an index of how related two variables are.

- Gymnastic ability and body size? Negative relationship: (r = -0.50)

- Head size and grade point average? No relationship: (r = 0)

- Shoe size and grade point average? No relationship: (r = 0)

- GPA and number of hours watching TV

Will grades go up if TV viewing goes down? What should parents do? Maybe people watch TV when they can't do schoolwork.

When there is a correlation, several different types of underlying realities are possible

A causes B

B causes A

A causes C which causes B

C causes both A and B

Example: smoking is negatively correlated with GPA (as smoking goes up, GPA goes down). Why?

i. A causes B: if you smoke more, you become worse at tests?

ii. B causes A: if you have a low GPA, you feel like smoking?

iii. A causes C which causes B: smoking causes brain damage which causes a low GPA?

iv. C causes both A and B: low IQ causes both stupid smoking and stupid testing?

THE BETWEEN vs WITHIN SUBJECTS DESIGN

1) Between-subjects design: in this type of design, each participant participates in one and only one group. The results from each group are then compared to each other to examine differences, and thus, effect of the IV. For example, in a study examining the effect of Bayer aspirin vs Tylenol on headaches, we can have 2 groups (those getting Bayer and those getting Tylenol). Participants get either Bayer OR Tylenol, but they do NOT get both. T

2) Within-subjects design: in this design, participants get all of the treatments/conditions. For example, in the study presented above (Bayer vs Tylenol), each participant would get the Bayer, the effectiveness measured, and then each would get Tylenol, then the effectiveness measured.

VALIDITY vs RELIABILITY

Validity - does the test measure what we want it to measure? If yes, then it is valid.

If a method is valid or not, is a question of whether the method is achieving the stated goal or not. For example, is the test actually testing what it is claiming to test? Test of marital fidelity by a coin flip. Such a test is probably not valid. Similarly, the quizes in this class will not be a valid tests of athletic ability. However, I hope and intend the quizes in this classes to be valid tests of psychological knowledge that has been presented in this course.

There is more than one means to validly study a phenomenon. For example, the different perspectives on psychology - biological, cognitive, and behavioral - could all be valid means of studying a phenomenon, but they would not use the same method.

Another Example - Does a stress inventory/test actually measure the amount of stress in a person's life and not something else.

Reliability - is the test consistent? Does the procedure produce similar results each time it is used, or is there a lot of variation between uses? If we get same results over and over, then the test is reliable. If the data that are collected are consistent and dependable, then the method may be said to be reliable.

For Example - an IQ test - probably won't change if you take it several times. Thus, if it produces the same (or very, very similar) results each time it is taken, then it is reliable.

However, a test can be reliable without being valid, so we must be careful.

For Example - the heavier your head, the smarter you are. If I weighed your head at the same time each day, once a day, for a week, it would be virtually the same weight each day. This means that the test is reliable. But, do you think this test is valid (that is indeed measures your level of "smartness")? Probably NOT, and therefore, it is not valid.