Developmental Psychology
Research Methods
In Chapter 2, we continued our discussion of research methods by discussing reliability and validity.
Reliability refers to the consistency and repeatability of a measure, while validity measures the degree to
which a measure represents what it is supposed to represent. We covered two types of reliability:
integrated test-retest reliability and within-subjects reliability. Integrated test-retest reliability measures
the consistency and repeatability of a measure when students are given the same test twice, while
within-subjects reliability measures the consistency and repeatability of a measure when students are
given different versions of the same test. We also discussed the strength of the relation between two
variables, which is determined by how consistent the results are across different samples. If two
variables are highly correlated, then their relationship is strong. If two variables are moderately
correlated, then their relationship is somewhat strong. If two variables are weakly correlated, then their
relationship is weak. We also looked at confounding variables, which are factors that can influence the
results of a study but don't necessarily affect the independent or dependent variable. One example of a
confounding variable is whether students watch television or drink tea before taking a test. In Chapter 2,
we continued our discussion of research methods by discussing reliability and validity. Reliability refers
to the consistency and repeatability of a measure, while validity measures the degree to which a
measure represents what it is supposed to represent. We covered two types of reliability: integrated
test-retest reliability and within-subjects reliability. Integrated test-retest reliability measures the
consistency and repeatability of a measure when students are given the same test twice, while within-
subjects reliability measures the consistency and repeatability of a measure when students are given
different versions of the same test. We also discussed the strength of the relation between two
variables, which is determined by how consistent the results are across different samples. If two
variables are highly correlated, then their relationship is strong. If two variables are moderately
correlated, then their relationship is somewhat strong. If two variables are weakly correlated, then their
relationship is weak. We also looked at confounding variables, which are factors that can influence the
results of a study but don't necessarily affect the independent or dependent variable. One example of a
confounding variable is whether students watch television or drink tea before taking a test.
School on campus decided to buy tablets for their preschool kindergarten classrooms and they want to
understand the changes in teachers perceptions before the tablets are proof given to them. The study
will look at their perception of efficiency usability and their own mastery of digital content. The school is
interested in what I want to find out too so this is a nice you know hitting two birds with one stone kind
of research. Longitudinal designs permit that in common patterns and individual differences in
development and it permits studying of relationships between early and late events and behaviors. It is
especially important because we can't manipulate some of the variables that were interested in so
seeing how it longitudinally unfolds actually gives us better grounds to start thinking about causality. The
kids born in 1970 may not actually be representative of kids that are born in 1990 because of the
differences in the environment especially technological media. Cohort is basically a generation but
Research Methods
In Chapter 2, we continued our discussion of research methods by discussing reliability and validity.
Reliability refers to the consistency and repeatability of a measure, while validity measures the degree to
which a measure represents what it is supposed to represent. We covered two types of reliability:
integrated test-retest reliability and within-subjects reliability. Integrated test-retest reliability measures
the consistency and repeatability of a measure when students are given the same test twice, while
within-subjects reliability measures the consistency and repeatability of a measure when students are
given different versions of the same test. We also discussed the strength of the relation between two
variables, which is determined by how consistent the results are across different samples. If two
variables are highly correlated, then their relationship is strong. If two variables are moderately
correlated, then their relationship is somewhat strong. If two variables are weakly correlated, then their
relationship is weak. We also looked at confounding variables, which are factors that can influence the
results of a study but don't necessarily affect the independent or dependent variable. One example of a
confounding variable is whether students watch television or drink tea before taking a test. In Chapter 2,
we continued our discussion of research methods by discussing reliability and validity. Reliability refers
to the consistency and repeatability of a measure, while validity measures the degree to which a
measure represents what it is supposed to represent. We covered two types of reliability: integrated
test-retest reliability and within-subjects reliability. Integrated test-retest reliability measures the
consistency and repeatability of a measure when students are given the same test twice, while within-
subjects reliability measures the consistency and repeatability of a measure when students are given
different versions of the same test. We also discussed the strength of the relation between two
variables, which is determined by how consistent the results are across different samples. If two
variables are highly correlated, then their relationship is strong. If two variables are moderately
correlated, then their relationship is somewhat strong. If two variables are weakly correlated, then their
relationship is weak. We also looked at confounding variables, which are factors that can influence the
results of a study but don't necessarily affect the independent or dependent variable. One example of a
confounding variable is whether students watch television or drink tea before taking a test.
School on campus decided to buy tablets for their preschool kindergarten classrooms and they want to
understand the changes in teachers perceptions before the tablets are proof given to them. The study
will look at their perception of efficiency usability and their own mastery of digital content. The school is
interested in what I want to find out too so this is a nice you know hitting two birds with one stone kind
of research. Longitudinal designs permit that in common patterns and individual differences in
development and it permits studying of relationships between early and late events and behaviors. It is
especially important because we can't manipulate some of the variables that were interested in so
seeing how it longitudinally unfolds actually gives us better grounds to start thinking about causality. The
kids born in 1970 may not actually be representative of kids that are born in 1990 because of the
differences in the environment especially technological media. Cohort is basically a generation but
