Research Terms

Question 1

External Validity

Answer 1

The extent to which an effect in research can be generalized to other
populations, settings, and treatment variables

Question 2

Concurrent Validity

Answer 2

The extent to which the results of a particular test, or measurement,
correspond to those of a previously established measurement for the same construct.
You want to make sure the test accurately measures what it is supposed to measure.
One way to do this is to look for other tests that have already been found to be valid
measures of your construct, administer both tests, and compare the results of the tests
to each other.

Question 3

Predictive Validity:

Answer 3

Predictive Validity: This involves testing a group of subjects for a certain construct, and
then comparing them with results obtained at some point in the future. For example: You
want to predict the risk factors for High School Dropout. You create a survey for 10th
graders and then later look at high school dropout rates of the surveyed students to see
if the results predicted dropping out.

Question 4

Internal Validity

Answer 4

The confidence that can be placed in the cause-and-effect relationship
in a study.

Question 5

Reliability

Answer 5

Reliability is the overall consistency of a measure. Higher reliability indicates a
measure will produce statistically similar results under consistent experimental conditions. Ex.
If two different social workers administer the same interview to a client, do they get the same
results?

Question 6

Validity

Answer 6

The degree to which a tool measures what it claims to measure. For example: the
Beck Depression Inventory is supposed to measure a person’s level of depression. It has validity
if it measures that. It would not have validity if, in reality, it measures a person’s level of anxiety
and not their level of depression.

Question 7

Statistical Significance

Answer 7

If a result is statistically significant, that means it’s unlikely to be explained solely by chance or random factors. In other words, a statistically significant result has a very low chance of occurring if there were no true effect in a research study.

The p value, or probability value, tells you the statistical significance of a finding. In most studies, a p value of 0.05 or less is considered statistically significant, but this threshold can also be set higher or lower.

Question 8

Experimental/Intervention Group

Answer 8

In research, a collection of subjects who are matched
and compared with a control group in all relevant respects, except that they are also subject to a
specific variable being tested (that is, they receive the new treatment/intervention).

Question 9

Randomized Controlled Trial:

Answer 9

n experimental design that measures the effect of an
intervention by randomly assigning participants to either an experimental/intervention group or a
control group.

For example, a new drug to treat depression is developed. Participants are randomly
assigned to either a control group (receiving a placebo/sugar pill) or the treatment group
(receiving the new drug). The Beck Depression Inventory (BDI) is given at the beginning
and end of treatment to see whether the treatment group saw a greater decrease in
depressive symptoms than the control group.

Question 10

Quasi-Experimental Design

Answer 10

The prefix ‘quasi’ means ‘resembling.’ So this is a design that
resembles a randomized controlled trial, but does not involve the random assignment to a
control group and experimental group (instead, it allows the researcher to control the
assignment to the treatment and control groups using some criteria other than random
assignment). It is commonly used in field research where random assignment is difficult or not
possible.
● You can use the exact same example as the randomized controlled trial with one
difference: the researcher places the participants in the treatment or control group using
some other criteria besides random assignment.

Question 11

Single Subject Design:

Answer 11

Single Subject Design: Research where the subject serves as their own control, rather than
using another individual/group.
● For example, a social worker administers the BDI to client(s) before beginning a specific
treatment to establish a baseline, and then administers the BDI again after receiving the
treatment to see if the treatment was effective in decreasing their BDI score..

Question 12

Retrospective Design:

Answer 12

Retrospective Design: In a retrospective design, participants are asked to retrospect (literally,
to ‘look back’) and try to remember what they were like at an earlier time point.
● For example, researchers could ask older teenagers how they were disciplined as kids.

Question 13

Cross-sectional Design:

Answer 13

Cross-sectional Design: In a cross-sectional design, researchers collect data at a single point
in time from participants of different ages.
● For example, researchers might hypothesize that people become more traditional in their
attitudes and more resistant to social change as they get older. To study this, they might
get participants in their 20s, 40s, and 60s to complete a measure of traditionalism and
then test whether there is a positive correlation between age and traditionalism.

Question 14

Longitudinal Design:

Answer 14

Longitudinal Design: In a longitudinal design, the same people are measured at different
ages.
● For example, researchers could follow the development of babies who experienced
developmental delays.

Question 15

Cross-sequential Design

Answer 15

Cross-sequential Design: A cross-sequential design is a combination of cross-sectional and
longitudinal designs. At the first point, groups of people from several different ages are
measured. If the design were to stop there, it would be a simple cross-sectional design, but
these groups are then followed over time, incorporating the longitudinal aspect.

Question 16

Correlation:

Answer 16

Correlation: A mutual relationship between two variables that are related; a change in one
variable is associated with a change in the other variable.
● For example: there is a positive correlation between height and weight. Taller people
tend to be heavier and vice versa.
● Correlation (a pattern between two variables) does not always mean causation (that one
variable causes the other).
○ For example: Someone may find that children who get tutoring receive worse
grades than children who do not receive tutoring. There is a correlation between
tutoring and lower grades, but tutoring does not cause the lower grade (it is likely
that tutoring is sought out because of the child’s low grade).

Question 17

Independent Variable:

Answer 17

Independent Variable: When performing an experiment, we look at the effect the
independent variable has on the dependent variable. The independent variable is the variable
changed (or controlled/manipulated) in a scientific experiment

Question 18

Dependent Variable:

Answer 18

Dependent Variable: The dependent variable is the variable tested and measured in a
scientific experiment. An easy way to remember this is that the dependent variable is dependent
on the independent variable. As the experimenter changes the independent variable, the effect
on the dependent variable is observed and recorded.

For example: Someone is testing the effect of a new antidepressant. The new
medication is the independent variable and the level of depression is the dependent
variable

Question 19

Inter-rater Reliability

Answer 19

Inter-rater Reliability: The degree to which different people give similar scores for the same
observations; refers to the consistency of a measure.

Question 20

Literature Review:

Answer 20

Literature Review: The process of searching published work to find out what is already known
about a research topic.

Question 21

Null Hypothesis:

Answer 21

Null Hypothesis: A statement that no relationship exists between study variables.

Question 22

Pretest

Answer 22

Pretest: A questionnaire or other data-gathering instrument administered to a subject just
before a period of inquiry that provides a baseline for comparison with the end results

Question 23

Posttest

Answer 23

Posttest: A questionnaire or other data-gathering instrument administered to a subject at the
end of a specific period of inquiry

Question 24

Pilot Study

Answer 24

Pilot Study: A procedure for testing and validating a questionnaire or other instrument by
administering it to a small group of respondents from the intended test population. The
procedure helps determine whether the test items possess the desired qualities of
measurement and the ability to discriminate other problems before the instrument is put to
widespread use.

Question 25

Objective Data:

Answer 25

Objective Data: Objective data is data that you can measure. This includes things like age, the
number of times a behavior occurred, blood pressure, temperature, etc. Objective data should be
an unbiased observation/measurement.

Question 26

Subjective Data

Answer 26

Subjective Data: Subjective data is data that is given from the viewpoint of the client (or
someone in the client’s life) and that is not measurable. This includes things like how a person
feels or their pain level. This is someone’s personal evaluation

Question 27

Qualitative Research:

Answer 27

Qualitative Research: Systematic investigations that include inductive, in-depth studies of
individuals, groups, organizations, or communities. They focus on the ‘why’ and ‘how’ of
decision making to better understand human behavior.

Question 28

Quantitative Research:

Answer 28

Quantitative Research: Systematic investigations that include descriptive or inferential
statistical analysis
● For quaLitative vs. quaNtitative: I like to look at the ‘n’ in quaNtitative and associate that
with Numbers. Quantitative data will have to do with numbers and qualitative will not. I
look at the ‘L’ in qualitative and think of quaLities (or an even simpler way is thinking of
qualitative as non-numerical)