Research & The Experimental Design

Why do psychologists engage in research?

Why do people engage in research? Well, some of my colleges say they engage in research because it’s an addiction. “An addiction to what?” you may ask. Pursuit of the truth.

Research helps us to understand things so we can develop better products, better treatments, and better ideas that ultimately advance the human condition. Here in Canada, research is the basis for our country’s productivity, economic success, and high standard of living.

Even though it is an extremely important endeavor, research is guided by very conservative (old-fashioned) rules and deliberate procedures termed the

scientific method. Using a consistent method like the scientific method we can ensure truth and validity in our research findings.

What are 3 examples of how research is used in the world today?

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What are the benefits of using the scientific method? Name two.

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The Experimental DesignThe experimental design is considered the jewel of research because it

provides researchers with cause-and-effect relationships. In other words this means that the experimental reveals measureable data about how often someone or something occurs when you ask or do something to them/it.

Experimental designs have three key components:

1) They involve the random assignment of subjects2) They have a control group3) They manipulate one or more of the research variables

5 Steps to Experimental Design

Step1: The Idea

Every experiment has to start with an idea. But where do research ideas come from? Ideas are not in short supply, we all have ideas! For example we all have ideas about: what makes a good relationship, why the Canadian and women won hockey gold medals, how to spend our money wisely. Most ideas come from our everyday lives but with a twist, the twist being that the idea is reworked and organized to spike our interest. Many ideas come from newspapers, books, internet or journals.

Eventually your idea will become twisted into a theory. A theory is a well-defined, logically organized set of ideas that describes and explains relationships between certain events. Theories aid in the development of a hypothesis which is a specific, testable prediction about what you expect to happen in your study. Generally it tries to answer questions related to “how” and “why” things are the way they are.

What is the main difference between a theory and a hypothesis?

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Step2: State and Define Your Variables

Variables are all the factors or characteristics in your study that can change or vary. Variables come in all shapes and sizes. Some examples include; gender, weight, mood, hair/eye color, shoe size, pain, occupation sentence length, type or amount of drug, personality, smoking, heart rate, number of relationships, abuse, tone of a bell, magnetic fields, intelligence, ect! Not only do you have to state your variables but you also have to operationally define them. This means you must create an exact definition about your variables in your study. For example if your variable is

love you must define what you consider love is. I’ve listed some of my favorite class-generated definitions of love: (make sure you pick the definition that you want to measure for)

Love is a “warm fuzzy” feeling Love is not thinking about other guys/girls Love is bringing me another beer!

Love is expensive

Once you have your variables operationally defined they can now be categorized as either Independent variables or Dependent variables.

Independent Variables: the variables in your study that are manipulated by the experimenter. This variable will change in quality or quantity according to the wishes of the experimenter.

Dependent Variables: the variables that do or do not change as result of manipulation of the independent variables. These are the actual observed, scored, or measured outcomes of the study.

Using the example given above about love how would you operationally define love? What kind of independent variable would you use to test for love? What would the outcomes of your experiment called?

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Step3: Choose and Assign Your Subjects

Pick participants or subjects for the experiment. The subjects are the people or animals that will be observed. The subjects you pick should be chosen randomly to represent a sample of the population. Randomly selected means each member of the population has an equal chance of being selected. Subjects are usually selected randomly to minimize possible errors or biases.

Now that you have your subject you must randomly assign them to either the experimental or control condition.

Experimental Condition: This is the group of subjects who will be exposed to your manipulation of the independent variable.

Control Condition: This is the group of subjects who undergo the exact same experimental procedures as the experimental condition, except the independent

variable is not manipulated. The control condition is sometimes considered to be the “placebo” or “sugar pill” group.

When might a scientist choose not to use randomly selected subjects?

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Why is it important to have a control condition? What does it reveal about the experiment?

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Step4: Run the Study

Finally it’s time to run the experiment by manipulating the independent variable and measuring the outcomes (dependent variable).

But how should the study be run?

Should the kids know they are in the experimental or control condition?

Should the experimenter, who is counting the aggressive acts, know which kids are in the experimental or control condition? If we chose this option it would be called the double-blind procedure because neither the subjects nor the experimenter knows which group they were placed in. This is a good method to use if you want to eliminate bias.

If you are using the double blind procedure to eliminate bias, what kinds of biases do you think you will be eliminating?

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Step5: Analyze and Interpret The Data

Data is the term used to describe a set of scores. Now that you have the data it is time to organize it so that you can determine if your theory (idea) was correct or incorrect. There are two ways to analyze the data:

1) Descriptive : these statistics summarize data (graphs, tables, averages)

2) Inferential : these statistics allow researchers to make conclusions about the data collected in the study. They may state their findings in a concluding statement.

Although descriptive and inferential data both provide measurable evidence, do you think this evidence is enough to prove a causal relationship exists? Why or why not?

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