3 Research Methods in the Study of Abnormal Behaviour
Research Methods in the Study of Abnormal Behaviour
What is Science?
Science operates on the principle that the universe follows natural laws.
These laws dictate that events occur in an orderly manner, allowing us to determine cause and effect.
These natural laws are discoverable and testable through experimentation.
We can use these laws to make predictions and conduct experiments to validate them.
Scientific results must be replicable under prescribed circumstances, not just a one-time occurrence.
Essential Beliefs: Underlying the scientific method are certain essential beliefs about the nature of reality and our ability to understand it.
Psychology as a Science
Psychology is defined as the science of studying mental processes and behaviors.
The goals of psychology are:
Describe: To detail the characteristics of behaviors and mental processes.
Explain: To understand why behaviors and mental processes occur.
Predict: To anticipate when certain behaviors or mental processes might occur in the future.
Control: To influence or modify behaviors and mental processes.
Scientific Method
The scientific method involves the following steps:
Identify questions of interest and consult literature: Begin by identifying a topic of interest and reviewing existing research.
Develop a testable hypothesis: Formulate a specific, testable prediction (hypothesis) that is operationally defined.
Select a research method, choose participants, and collect data: Choose an appropriate research method, select participants, and collect relevant data.
Analyze the data and accept or reject the hypothesis: Analyze the collected data to determine whether to support or reject the initial hypothesis.
Seek scientific review, publish and replicate the study: Submit the research for peer review, publish the findings, and encourage replication of the study.
Build a theory: Develop a broad explanation or model (theory) based on the collected evidence.
Theories vs. Hypotheses
Theory
A well-developed set of ideas that aims to explain observable phenomena.
Theories are widely accepted in the scientific community and supported by data.
The primary goal of science is to advance theories to account for data, often by proposing cause-effect relationships.
Hypothesis
A testable prediction or proposed explanation for an observable phenomenon.
Qualitative vs. Quantitative Research
Qualitative Research
Does not test the relationship between variables.
Focuses on understanding the "what" and exploring new or uncommon phenomena.
Deals with the messiness of being human and getting the stories behind the numbers.
Utilizes various artifacts like pictures, meeting minutes, diaries, music, art, etc., not just words.
Quantitative Research
Includes both correlational and experimental research designs.
Involves gathering numbers and analyzing data through statistical computations.
Aims to determine relationships and cause-and-effect relationships between variables.
Nomothetic vs. Idiographic Research
Nomothetic Research
Measuring a group of people on a number of variables.
Examines the relationship among the variables (variable-centered).
Correlational methods are an example of nomothetic research.
Idiographic Research
Detailed understanding of the individual (person-centered).
Case studies and qualitative methods are examples of idiographic research.
Case Studies
The detailed study of one individual.
Providing detailed descriptions of the individual's experiences.
Collecting historical and biographical information.
Often includes details of therapy sessions to provide insight into therapeutic techniques.
Several case studies can be compared and analyzed for common elements through a specific method.
Example: Little Albert
Epidemiology
Examines rates of occurrence of abnormal behavior in the population as a whole and in various subgroups (e.g., race, ethnicity, gender, age, or social class).
Provides a general picture of a disorder.
Prevalence: Proportion of a population that has the disorder at a given point or period of time.
Incidence: The number of new cases of the disorder that occur in some period, usually a year.
Risk factors: Conditions or variables that, if present, increase the likelihood of developing the disorder.
Correlational Research
Aims to examine the strength and direction of a relationship between two variables.
Focuses on how variables covary, or vary together.
Variable: Any characteristic, number, or quantity that can be measured or counted and that is being studied.
Types of Linear Relationships
Interpreting Relationships
Correlation coefficients range between -1 and +1.
The closer the coefficient is to \pm 1, the stronger the relationship.
The closer the coefficient is to 0, the weaker the relationship.
Using Correlations
Correlation does not imply causation; third variables may be at play.
If a causal relationship exists, be cautious of directionality.
Despite their limitations, correlations are still important sources of information (e.g., smoking and lung cancer research started with a correlation).
Allows us to make predictions.
Experimental Research
Basic features of experimental study methods:
The researcher typically begins with an experimental hypothesis.
The investigator chooses an independent variable (IV) that can be manipulated (different conditions – often experimental vs. control groups).
Participants are assigned to the conditions by random assignment.
The researcher arranges for the measurement of a dependent variable (DV).
Analyze the data to determine if there has been an experimental effect.
Independent Variable (IV): The variable that you manipulate.
Dependent Variable (DV): The variable that you measure (or the variable that is changed by the IV).
Experimental Research Design
Random assignment of participants ensures groups are equivalent at the start of the experiment.
Experimental group is exposed to the independent variable (new therapy).
Control group (on waitlist or original therapy) does not receive the independent variable.
Measure dependent variable both before (pre-test) and after (post-test) the intervention for both groups; compare the results.
Statistical Significance in Experimental Research
Within-group variance: The difference within the groups.
Between-group variance: The difference between groups (the experimental effect).
Statistical significance is tested by dividing the between-group variance by a measure of the within-group variance. When the average difference between the two groups is large relative to the within-group variance, the result is more likely to be statistically significant.
Assessing Difference
Tests of statistical significance allow us to compare the means of groups to see if the differences are statistically significant (meaningful).
P-value or Probability Statistic: A number describing how likely it is that your data would have occurred by random chance.
A p-value less than 0.05 (typically \leq 0.05) is statistically significant.
If you want to know how big the effect is - need to calculate the effect size.
Effect Size
P-value tells us how sure we can be that one thing is related to another or how sure we can be that two groups are different (the results are from group differences, not just random chance).
Example: How sure can we be that happiness and parenting status are related?
Example: How sure can we be that parents vs. non-parents are actually different?
Effect size:
How strong is the relationship between the two variables?
How big is that difference?
We can use r (for correlations) or Cohen’s d (for group differences).
Effect Size and r
Example: Are mental health literacy and confidence in support skills associated with hours spent in mental health training?
Strong correlation (r=0.78) between hours spent in training and improvements in mental health literacy.
Moderate correlation (r=0.58) between hours spent in training and improvements in confidence in their support skills.
The number of hours a person spends in training has a moderate effect on confidence in support skills and a strong effect on mental health literacy.
Effect Size and Cohen’s d
We use Cohen’s d to assess effect size when comparing the means (averages) of two experimental groups (e.g., treatment group vs. control group) or comparing two independent groups (e.g., men vs. women, before vs. after intervention).
Placebo Effect
The placebo effect is an improvement in a physical or psychological condition that is attributable to a client’s expectations of help rather than to any specific active ingredient in a treatment.
Single-blind Procedures: When the patient or client is unaware of what group they have been placed in (placebo or treatment).
Double-blind Procedures: When neither the researchers nor the clients are aware of who has been placed in the treatment or placebo control groups.
Validity
Internal validity: Extent to which effect can be confidently attributed to manipulation of IV; Inclusion of at least one control group.
Confounders: Effects are intermixed with the effects of the IV leading to internally invalid studies (e.g., passage of time).
External validity: Can the results be generalized beyond the immediate study?
Analogue experiments: The use of a related phenomenon (an analogue) in the lab where behavior is rendered temporarily abnormal through experimental manipulations.
Meta-Analysis
Involves the review of many studies to determine the effects of treatment.
Examine published studies, combine the results into a common format, and then determine the extent of improvement (effect size).
Limitations
It is a complicated process that requires decisions at each of numerous phases or steps.
Results of a meta-analysis are difficult to interpret.
Need to take into account moderator variables (e.g., gender) that may influence or qualify the results in some meaningful way.