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PSYC 2030 Introduction to Research Methods York University Lecture & Reading Notes

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Course
PSYC 2030 (PSYC2030)

Institution
York University (Ebor )

These are the complete notes for Jubis' PSYC 2030 Introduction to Research Methods class at York University. This includes all detailed and organized lecture and reading notes with images.

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Uploaded on September 15, 2023
Number of pages 78
Written in 2022/2023
Type Class notes
Professor(s) Rebecca jubis
Contains All classes

psyc 2030
research methods
york university
psychology
independent variable
dependent variable
extraneous variable
confounding variable
manipulated variable
control gro
introduction to research methods

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PSYC 2030
Introduction to Research Methods
Notes
Class 1: Chapter 5
INTRODUCTION TO EXPERIMENTAL RESEARCH
Pre-recorded Lecture:
Welcome Lecture
★ Not to be tested on “research examples” or “boxes” in the textbook
★ 98% of test content will be at least mentioned in lectures
★ Pre-recorded lectures
★ Strongly encouraged to use study guide when reading textbook!
Introduction to Experimental Research
★ In an experiment the researcher manipulates certain variables and holds others constant (ie controls them)
● Advantage: you can infer a cause and effect relationships
● Disadvantage: because experiments tend to be conducted in artificial environments (lab) results might
not be generalizable.
★ It can involve basic or applied and be conducted in the field or lab
Independent Variable (I.V)
★ the variable that’s manipulated or changed to see if it has an effect on behavior - in other words, the
situation that the experimenter creates
★ there must be a minimum of 2 levels (the groups or conditions that are being compared)
● the terms “level of the IV”, “group” and “condition” can be used interchangeably.
Dependent Variable (D.V)
★ The behavior that’s measured to see if the IV has an effect
★ Think of the IV as the “cause” and the DV as the “effect”
★ The DV is dependent upon the IV
★ There will typically be more than one IV and DV in an experiment
★ Example: Males aged 20, 25 and 30yrs were given a memory test consisting of 20 words to be recalled to
see if memory got worse with age.
● I.V. = age only (3 levels)
● Sex and list length are not IVs because they weren’t manipulated (they were constant). If females wer
also included and if different list lengths were compared, then they would be IVs.
● DV= memory score
Extraneous Variable

,★ variables that are of no interest to the experimenter, but that could affect behavior. As long as they are
controlled for or held constant (all groups have the same exposure to the variable), it’s not a problem.
★ an extraneous variable that’s not controlled (held constant) and that changes along with the IV (ie, the
extraneous variable is not equal across groups)
★ Not a problem - as long as it is controlled/held constant (all groups in study have same exposure to this
variable)
★ e.g extraneous variable could be amount of sleep - not problem as long as they all had same amount
Study Time Grade

0 hrs 60%

5 hrs 74%

10 hrs 62%
Confounding Variable
★ a confounding variable “interferes” with your study making it impossible to determine if it’s the IV or the
confounding variable that’s the cause for the DV (only the IV is supposed to be the “cause”).
★ Problem - not all subjects have same exposure
Sleep Amount Study Time Grade

Normal sleep 0 hrs 60%

Normal sleep 5 hrs 74%

No sleep 10 hrs 62%
★ Ex. Suppose you’re testing the effects of study time on grade. Sleep could affect grades (it’s an extraneou
variable) but it’s not a problem as long as the average amount of sleep that subjects (S) got across ALL
study time groups is constant. However, if everyone in the 10 hr study group got no sleep while the 0 hr
and 5 hr study groups got a normal amount of sleep, it becomes a confounding variable because it change
with the IV. In other words, sleep isn’t held constant across levels of study time so you don’t know
whether it’s the study time or the sleep that’s the cause for the grade.
● You can resolve this problem by ensuring that all groups get roughly the same amount of sleep.
The Independent Variable can be either a Manipulated Variable or a Subject Variable
★ Manipulated Variable:
● when the experimenter creates the situation that the S encounters and Ss can theoretically be randomly
assigned to the levels of the IV
● (e.g. alcohol dosage, how long you’re told to study for a test, etc)
★ Subject Variable:

, ● already-existing characteristics of subjects - you can’t manipulate a subject variable and you can’t
randomly assign Ss to these levels of the IV. Rather, Ss must be selected.
● (e.g. age, IQ, personality traits, gender, psych disorder) - can’t manipulate someone’s age or IQ
● When a subject variable is present, you have a quasi-experiment (not a true experiment).
★ Drawing Conclusions:
● If you have a manipulated IV you can infer cause and effect (as long as there are no study flaws) but
with a subject IV you cannot infer causation. All you can say is that the groups performed differently.
● A given variable could be a subject or a manipulated variable depending upon how it’s used in the
study (e.g people’s natural level of anxiety would be a subject variable, but manipulating a person’s
anxiety level by telling them that a test is easy or hard, would act as a manipulated variable)
Varieties of Manipulated Independently Variables
★ All manipulated variables can be classified into one of three general types:
● Task Variables - subjects are given different types of tasks to perform (e.g one group might have to
memorize nouns, whereas another memorizes verbs)
● Instructional Variables - subjects are given a different set of instructions on how they should perform
the task (e.g one group of subjects might be told to memorize a list of words by simply repeating the
words - rote memorization - whereas another group is told to create a mental picture of the words)
● Situational Variables - subjects encounter different environmental circumstances (e.g subjects
complete the task in either a lab or in a classroom)
Control Group
★ the group that gets the “0” level of the IV or the group that doesn’t get the “treatment” - must have
★ this group is used as the basis for comparison
★ It’s not always possible to have a true control group (i.e you can’t have “0” amount of age, gender, or
intelligence, etc)
Experimental Group
★ the group that gets the “treatment” and whose performance is compared to the control group.
★ The control and experimental groups should be similar to one another in every way except for the IV. Tha
way, if differences exist between the groups, you know that the difference must be caused by the IV and
not by some other uncontrolled variable – ie, a confounding variable
● e.g In comparing the effects of 0, 2, and 4 oz of alcohol on memory, the 0oz group is the control group
and the 2 and 4 oz groups are experimental groups.
4 Types of Validity in an Experiment
★ Statistical conclusion validity – stats are properly used - proper analysis
★ Construct validity – how good the measures are for the IV and DV; how representative are they of the
construct (a construct is something we assume exists even if we can’t directly see it)
★ External validity – the degree to which results generalize to other populations, times, environments.
● Ecological validity refers to whether the study has relevance to real-life situations.
★ Internal validity – the degree to which the study has no flaws, and thus, we can infer causation - degree to
which study is methodologically sound

, ★ Things that can threaten internal validity:
● if you have a confounding variable
● if you don’t have a true control group
● if the IV is a subject variable
● when a S is measured repeatedly resulting in a practice effect (to be discussed later)
● if you have a Pre-Post Study
Pre-Post Studies (Studies Extending Over Time):
★ studies that extend over a period of time (e.g when you want to determine the effectiveness of a therapy o
treatment)
★ if you wanted to determine the effectiveness of a therapy, you could randomly assign patients to a control
group (no therapy) or the experimental group (receives the therapy) and then compare the two groups.
★ Alternatively, you could first give people a “pre-test” (before therapy), they would then undergo the
therapy over a given time period, and then give the same people a “post-test” (after therapy). You would
then compare their pretest score to their post-test score to see if the therapy was effective (a control group
is not typically used).
● Problem: factors other than the therapy could have influenced post-test scores and so, results could be
due to a confounding variable rather than due to the treatment.
● e.g IF a control condition was used to determine if treatment successfully reduced anxiety:
Pre-test: 80 → treatment → Post-test: 65 → treatment is effective!
Pre-test: 80 → no treatment → Post-test: 80 → control condition - you would expect no difference
Pre-Post Studies (Studies Extending Over Time):
★ What factors could threaten internal validity in pre-post studies (ie, could act as a confounding variable)?
● History: an event outside of the study produces an effect on post-test scores.
● Maturation: post-test scores could be due to a subject’s natural maturation that occurred between
pre-post measures, and not due to the treatment per se.
● Regression to the Mean: When Ss are selected because they score extremely high or low on some
characteristic, their scores tend to change in the direction of the mean when they’re re-tested (e.g a
very high pre-test score is likely to become lower in the post-test, and a very low pretest score is likely
to become higher in the post-test - independent of actual treatment effects)
● Testing and Instrumentation: post-test scores could be influenced by the practice gained by first doing
the pre-test. Instrumentation could be a problem if the measurement tool used for the pre-test is
slightly different from the one used for the post-test (a different version was used, for example).
Threats To Internal Validity: Subject Problems
★ Subject-Selection Effects
● can occur when subjects cannot be randomly assigned to groups. (e.g if you wanted to determine if
lecture courses lead to higher grades than on-line courses, students have already enrolled in these
courses so you aren’t randomly assigning them. Consequently, if grades are higher in lecture courses,
you don’t know whether it’s due to the course-type or whether it’s due to the type of student who
chooses a lecture course)

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