1. What is science?

Induction

Method of introspection

Freud as an example

Limitations of this approach

Objectivity

Research controls for biases

Falsificiation

Must be able to test competing claims

2. Kinds of questions that clinical research can address:

Risk factor

Associated features

Nature and course of illness

Treatment outcome

Prevention

3. Usually studies address the above questions by looking at

Covariation OR

Comparison

4. What makes a good hypothesis?

Stated in declarative form

Proposes a relationship between the variables

Reflects a theory or body of knowledge

Brief and to the point

Testable

5. What is the place of research in clinical psychology?

Tension between research and practice

Scientist-practitioner model

Different world views of scientists and practitioners

Contributes to a shared understanding about mental illness & treatment

Contributes to the development of theory

6. What makes good research?

Based on the work of others (theory, prior research, etc.)

Can be replicated

Generalizable to other settings

Do-able

Generates new questions

Incremental

Apolitical activity for the betterment of society

MOST IMPORTANT: asks interesting questions

1. Two main types of measures: quantitative & qualitative.

Most research in psychopathology is quantitative.

Why? Numbers are useful:

They enable greater precision in measurement.

They can be manipulated by statistics to summarize data & test predictions.

They allow for comparisons across individuals and across studies.

So this lecture focuses on quantitative methods.

But there is a long history of qualitative research in psychopathology.

We’ll talk more about that in our lecture on small-sample studies.

And you should review the info in your text on qualitative methods.

2. Process of translating psychological concepts into numbers is OPERATIONALIZATION.

Operational definitions: a description of how you will measure your psychological concepts

When you operationalize (measure) something, you create a variable.

See book (p. 50) for different domains of variables

But translating complex phenomena to numbers is very difficult.

3. Six main classes of variables

Dependent variable – generally speaking, the outcome variable

Independent variable – the factor that is expected to influence the dependent variable

Confound – a third variable that is related to both the DV and IV, obscuring the relationship between the DV and IV. Confounds are not measured in the study.

Control – potential confounds that are included in the design

Mediator – a variable that accounts for the relationship between the DV and IV

Moderator – a variable that changes the relationship between the DV and IV

4. Different types of measures in clinical research:

Observations

1. Continuing with different types of measures in clinical research:

Projective measures

Clinical records (or archival data)

Self-report methods will be discussed in labs; see also your text

2. Taking a step back, how can we evaluate the quality of individual measures?

We examine their psychometric properties.

Psychometrics = reliability and validity

Reliability refers to consistency of the measure

Validity refers to the measure’s accuracy

3. Different ways of measuring reliability

Test-retest

Equivalent forms

Split-half

Internal consistency

Inter-rater reliability

4. Validity refers to the measure’s accuracy

Different types of validity:

Face

Content

Criterion

Construct

5. Relationship between reliability and validity

Measures can’t be valid unless they’re reliable.

BUT they can be reliable without being valid!

1. Review of reliability and validity

2. Reliability and validity are important issues with regards to diagnosing psychopathology.

Early studies used clinicians’ diagnoses.

BUT Clinicians’ diagnoses not very reliable. WHY?

Problems with early diagnostic systems

Lack of standardization among interviews

Differences among clinicians

2. Steps taken to resolve this problem:

Feighner criteria

Research Diagnostic Criteria (Spitzer & colleagues) 1978

Structured interviews:

3. Structured diagnostic interviews

Very focused on symptoms

Reduce clinicians’ biases

Ensure that interviewers cover key points in DSM diagnostic system

Help ensure that diagnoses are consistent across studies

BUT they are time-intensive

AND they require extensive training to administer

AND they do not provide other information about clinical status

AND they encourage a focus on "pure" diagnoses

Comorbidity

Bring in any questions you have about the exam!

You also will be getting a formal review in labs this week.

Note about the readings for this week: Chapter 7 is very dense!

It’s not necessary to know all of the individual designs!

Refer to your lab review for more information about how to focus your reading for this week.

3. Ultimate goal of design: minimize the ambiguity of findings

4. Basic choices: Timing of the study

cross-sectional

longitudinal

retrospective

5. Basic choices: Setting of the study

Clinical vs. analogue research

Ways that analogue research can deviate from clinical studies

Target problem

Sample

Treatment setting

6. Basic choices: Type of design

"experimental" versus "correlational"

another way of saying this: "experimental" versus "quasi-experimental"

Correlational designs

examine relations among variables

BUT can’t assign participants to groups

Experimental designs

Can exert more control

In particular, can assign participants to groups

With control: a greater ability to make causal inferences

With control: more able to rule out alternate explanations for findings

7. Internal validity

Degree to which design rules out alternate explanations

Does so by controlling for confounds

Ways of controlling for confounds:

Random assignment

Blocking

Control or comparison groups

Statistical controls

Good luck on the exam!