Chapter 01
Introduction to Developmental Science
Theory & Research Methods

1. Ask a (developmental) research question.
2. Develop a hypothesis.
3. Construct a methodology to test your hypothesis.
4. Draw a conclusion.
5. Share your findings.


• Is this a good research question?
!! NO !!
Good research questions must involve something that can be empirically defined and measured .

• Which of these is the best research question?
– A) Alcoholic parents tend to neglect their children more than non-alcoholic parents.
– B) What factors influence high school dating?
– C) Does the frequency and graphic quality of violence in video games influence levels of antisocial thinking in adolescent males?
– D) How much longer until class is over?
– E) B & C are equally good.

• Operational definition
– the description of the variable of interest in measurable terms .
• So, how might we operationally define?
– Aggression
– Happiness
• Measurement
– Device used to detect the events/phenomena to which the operational definition refers.
 With this in mind, take a moment to generate a research question of your own.

• Subjective measures
– Introspective reports
– Survey/Questionnaire
• Objective measures
– Standardized testing
– Naturalistic observation
• Physiological measures
– fMRI, galvanic skin response
– Levels of hormones, neurotransmitters
Are physiological measures subjective or objective measures?
A) subjective
B) objective
C) both

• Developing a hypothesis:
– Consider your research question.
– What do you think you’ll find?
• Why?
– That is, what is your
( theoretical/conceptual/empirical ) justification for you hypothesis?

• How are you going to test your hypothesis?
• Non-experimental Design
– Observation of variables of interest
• Experimental Design
– Manipulation of variables of interest

• Systematic Observation
– Naturalistic
– Structured
What are the (dis)advantages of a naturalistic vs. structured observation?
• Self-report Survey
– Clinical
Why choose a survey over an observation?
– Structured
• Qualitative
Ask Yourself!
What design is best for study
non-normative development?
– Case study – in-depth study of individual
– Ethnography – in-depth study of culture

• Cross sectional
• Benefits – cheap way to capture change over time
• Problems – cohort effect and other group differences
• Longitudinal
• Benefits – confidence that change being captured is genuine change
• Problems – reduction of sample size and learning effect
• Cross-sequential

 Time 1
2 nd
4 th
6 th
Time 2
4 th
6 th
8 th

 Time 1
2 nd
4 th
6 th
Time 2
4 th
6 th
8 th
Ask Yourself!
How do crosssequential designs reveal any cohort effects?

• You are asking a question about behavior in a given population
– It is difficult (if not impossible) to ever study an entire population – so what do we study instead?
teenagers

• You are asking a question about behavior in a given population
– It is difficult (if not impossible) to ever study an entire population – so what do we study instead?
• a sample.
teenagers

• How do we make sure that we can accurately generalize from a sample to a population?
• We choose a representative sample.
– controlled sampling
– random sampling

• Which makes for a good sample?
– A) when it is chosen to match the demographics of the population as closely as possible
– B) when it is randomly selected from the population
– C) both.

• What is a correlation ?
• Relationship between two variables
– A is related to B
– Positive relationship: A+/B+, A-/B-
– Negative relationship: A+/B-, A-/B+

• As a person gets angrier, they also get more violent.
– A) Positive
– B) Negative
• Positive . As anger
increases, violence
increases.
• As calorie consumption drops, people have less energy.
– A) Positive
– B) Negative
• As a person gets older, they start to remember fewer vocabulary words.
– A) Positive
– B) Negative
• Negative . As age
increases, vocabulary memory decreases.
• Positive . As calorie consumption
decreases, energy levels decrease
(movement is happening in the same direction).

relationships between variables
 Ultimately, we are typically interested in whether or not one variable causes another.
T/F:
All variables that are causally related are correlated .
T/F:
All variables that are correlated are causally related .

• A researcher compares older adults with chronic heart disease to those with no major health problems and finds that the first group scores lower on mental tests. Can the researcher conclude that heart disease causes a decline in intellectual functioning in late adulthood?
• A) yes
• B) no

• Two variables are correlated
X  Y
• Three possible relationships
X causes Y
Y causes X
Z causes both X and Y
– with correlation, we cannot know which it is.
22

third variable problem
 + 

 + 

.
• A  B
• A is the independent variable -- manipulated
• e.g., amount of television violence watched
• B is the dependent variable -- measured
• e.g., amount of aggressive behavior exhibited
25

Violent
TV
Non-violent
TV
?

• In the case of a 3 rd variable, you have two choices:
• Manipulate and measure x & y, while controlling for z .
or
• Manipulate and measure x, y, & z .

randomization
(controls for the 3 rd variable)
• used when z is not important for the study

selected groups
(measures the influence the of 3 rd variable)
• Used when z is important for the study
Adult supervision No adult supervision

describing variables
• Central tendency
• mode—most frequent
31

describing variables
• Central tendency
• mode—most frequent
• mean—average
Μ = 3.27
32

describing variables
• Central tendency
• mode—most frequent
• mean—average
• median—middle
33

describing variables
• Central tendency
• mode—most frequent
• mean—average
• median—middle
• Each of these tells us something different about our data.
34

• Variability
– range describing variables
7 – 1 = 6
35

• Many things tend to be normally distributed in a given population.
• So, we should expect most people to fall somewhere close to the middle, with the extreme cases being less frequent.

IQ is normally distributed.
mean

Income is one thing that is not normally distributed.
A) True
B) False
 Can you think of others?

• Validity
• Being able to draw accurate inferences (conclusions) about what you are studying from your measurements
• Invalid in definition
– Examples?
• Invalid in detection (measurement)
– Examples?

If I asked college students and mature adults to rate on a scale how much fun they have ever day and I found college students rated their level of fun much higher, could I then draw the conclusion that college students are happier than mature adults?
– A) yes
– B) no
• Why?
• Issue of invalid definition.
If I was interested in studying freshman college students’ general anxiety levels and so I polled all of my Intro students using a standard anxiety scale at the beginning of class right before they took an exam, could I draw conclusions about general levels of anxiety from those measurements?
– A) yes
– B) no
• Why?
• Issue of invalid detection.

• Internal validity
– Study was designed so that you were able to draw accurate inferences about causal relation between independent and dependent variables.
• External validity
– Study was designed so that your independent and dependent variables are defined in natural/realistic way.
• You can have internal validity but not have external validity – why?

• Reliability
• The tendency for measurement to produce the same results when used in the same way (or under the same conditions).
– Type 1 error (false positive)
– You want a measurement that is stable enough that it won’t detect changes in your variable when changes haven’t actually occurred.
• Power
• The tendency for measurement to produce different results when used in different ways (or under different conditions).
– Type 2 error (false negative)
– You want a measurement that is sensitive enough to detect changes in your variable when changes actually occur…

• You want a measurement that is sensitive enough to detect changes in your variable when changes actually occur…
• You also want a measurement that is stable enough that it won’t detect changes in your variable when changes haven’t actually occurred.
• This is an issue of:
– A) power
– B) reliability
• This is an issue of:
– A) power
– B) reliability

• Biases in observation
– Participant biases
• Demand characteristics
– Observer biases
• Confirmation bias

Double-blind experiments

• Ethical practices
– Informed consent
– Debriefing
– Special considerations for children
• Are there things we shouldn’t study?

• Drawing conclusions
– What kinds of conclusions can you draw?
– Can you generalize to a population?
• How broad of a population?
– Limitations
• Sharing your findings
– Conference presentations
– Publications