1. Issues in Research
Reliability: The consistency and stability of a measure or score.
Validity: the extent to which a measure actually measures what it is intended to measure
The truthfulness of a measures.
A test can be reliable and not be valid.
Internal validity: the extent to which changes in the dependent variable can be attributed to the influence of the independent variable rather than to confounding variables.
Degree to which researchers can draw accurate conclusions about the effects of the independent variable.

2. Issues in Research
Construct validity: The extend to which a measure measures the conceptual variable it is designed to measure.
Convergent validity: the extent to which scores on a measure correlate with scores on a different measure of the same construct.
Discriminant validity: the extent to which scores on a measure do NOT correlate with scores on a measure of a different construct.

3. Developmental Research Methods (Schmidt & Teti, 2006)
Parameters of Developmental Change:
Age: chronological
Cohort: group of individuals born at the same time period experiencing the same events
Time of Measurement: calendar time or period
Interindividual differences: differences b/w individuals
Intraindividual change: differences within an individual over time

4. Cross-Sectional Designs
At least two samples of difference ages and cohorts measured at the same time
Compare memory strategies of 4 and 6 year olds.
Age group and interindividual differences
Problems/Issues:
Does NOT assess age changes, the stability of a characteristics over time, or intraindividual change
Compares group means (looses individual info)
Age by Cohort confound: results may be affected by historical or cultural differences b/w cohorts.
Limits external validity (generalizability)
Historical or cultural difference between cohort

5. Longitudinal Designs
Individuals of a given age and cohort are followed over time.
Compare how memory strategies change between 4 and 6 years of age.
Intraindividual change and age changes
Developmental sequences and co-occurring social and environmental change
Early-later relations
Shape of developmental function

6. Longitudinal Designs Issues/Problems: Expensive, time consuming
Sampling biases: those who participate in study may be different than those who don’t participate
Populations changes over time (representative at time 1 but not at time 2)
Attrition (drop out, moves, death), those who drop out typically differ from those who stay.
Siegler and Botwinich (1979) study
Testing and practice effects: familiar with measures, do better over time

7. Longitudinal Designs Issues/Problems:
Instrumentation: different tests at different ages
validity: are you measuring the same construct
Same tests may become dated, obsolete
Changes in study personnel
Changes in how researchers define and measure the IV and DV.
Theories and hypothesizes may change, new findings from other sources.
Cohort Effect: unique experience of the cohort
Age by time of measurement confound
Difficult to separate

8. Longitudinal Designs Regression to the Mean:
Tendency for scores to regress (move towards) the mean on a subsequent test.
Less error variance over time
More common when measuring extreme scores
If participants are selected because they have extreme scores on the pretest (e.g. select a school with very poor reading ability) there may be other factors due to measurement error that resulted in such low scores at the pretest (tired, bad day etc.) that may have slightly deflated their scores.
Measurement error causes extreme scores to be biased in the extreme direction (away from the mean)
So when you test them a second time it is unlikely that you will have those same factors that may have deflated their scores and their scores will increase a bit and make it look like the program has an effect.

9. Time-Lag Designs (Miller 1998)
Study individuals of the same age at different points in time
Study 6 year olds in 2006, 2008, and 2010
Does not give information on age changes or differences
validity: are you measuring the same construct
Provides information on factors that may confound age comparisons in other designs.
Generational factors (cohort effects) and time of measurement confounds.

10. Time of Measurement
2006
2008
2010
2012
1998 8 10 12 14
2000 6
2002 4
2004 2
Longitudinal
Year of Birth (Cohort)
Cross-Sectional
Time-Lag

11. Complex Designs
Sequential Design: combination of cross-sectional, longitudinal, and/or time-lag designs.
Allows researcher to separate effects of age, cohort, and time of measurement
Cohort-Sequential Design: follows different cohorts over time
Two overlapping longitudinal studies.
E.g., 6 yr olds followed from , and 6 yr olds from
Age not confounded with cohort (because different times of measurement)
Not limited to one cohort
Cross-sectional, longitudinal, and time-lag dimensions

12. Complex Designs
Time- Sequential Design: Two or more cross-sectional studies conducted at different times of measurement.
6, 8 and 10 yr olds compared in 2005, 2010, and 2015.
Age and time of measurement are not confounded
Complex designs are very costly, and time consuming

13. Quasi-Experimental Designs (Leary, 2004)
When researchers can not manipulate the independent variable, rather it is a grouping variable (gender, age, disability) and equivalence between the groups can not be ensured
Researchers can not randomly assign participants to groups, thus they lack control over extraneous variables
Quasi-independent variable: is not a true independent variable but usually occurs naturally or can not be manipulated.
Researchers still look for effect of the quasi-independent variable.

14. Pretest-Postest designs
Quasi-experimental designs usually have lower internal validity than true experiments.
Pretest-Postest designs
Test participants before and after the quasi-independent variable
One group: measure participants before and after the quasi-independent variable. Only have one group of participants (those that experienced the quasi-independent variable)
Test reading before children at school A start reading program and then test their reading after they finish the reading program.
O1 X O2

15. Threats to internal validity
Maturation: students may have matured over the reading program. They may have got better at reading just because of time and not due to the program.
History Effects: something other than the independent variable may have occurred between the pretest and posttest.
Pretest sensitization: taking the pretest may change the participants reaction to the posttest.
Regression to the mean: Tendency for extreme scores on pretest to regress (move towards) the mean on a subsequent test (posttest).

16. Nonequivalent Control Group Design:
We cannot randomly assign participants to control and study groups, so we select a control group that is similar to the group that gets the quasi-independent variable.
Posttest-only: measure both groups after one group has received the treatment.
Measure reading in School A and School B after School A has participated in the reading program.
Quasi-experimental group X O
Nonequivalent control group O
Selection bias: we do not know whether the two groups were similar before the intervention

17. Pretest-Posttest design: Test both groups before one groups gets the intervention, then test both groups again after one group gets the intervention (quasi-independent variable)
Quasi-experimental group O1 X O2
Nonequivalent control group O O2
Allows researchers to see if the two groups scored similarly on the dependent variable before the introduction of the treatment.
To determine if the quasi-independent variable had an effect you want scores to change between pretest and posttest ONLY for the Quasi-experimental group and NOT for the Nonequivalent control group

18. Simple interrupted time series design
Time Series Designs
Measure the dependent variable many times before and after the quasi-independent variable is introduced
Simple interrupted time series design
Researchers make a series of observations of the dependent variable before and after the treatment is introduced
O1 O2 O3 O4 X O5 O6 O7 O8
Evidence for a treatment effect occurs when there are abrupt changes in the time-series data at the time the treatment was implemented

19. Percentage of cavities after the introduction of fluoride into toothpaste in 1970

20. Percentage of cavities after the introduction of fluoride into toothpaste in 1970

21. Percentage of cavities after the introduction of fluoride into toothpaste in 1970

22. This design helps to distinguish changes due to maturation from the quasi-independent variable
Contemporary History: Observed effect could still be due to another event that occurred at the same time as the quasi-independent variable
Perhaps the electric toothbrush was introduced in 1970, or there was a major TV add campaign that promoted brushing teeth.

23. Interrupted time series with a reversal
Researchers measure the DV before and after the treatment is introduced and then again after the treatment is removed
O1 O2 O3 O4 X O5 O6 O7 O8 -X O9 O10 O11
We can see what happens to the DV after the quasi independent variable is introduced and then again after it is removed.
If the quasi-independent variable was really having an effect we would expect performance to change back to normal after it is removed

24. Percentage of cavities after the introduction of fluoride into toothpaste in 1970

25. Limitations:
Researchers may not have the ability to remove the quasi-independent variable
remove fluoride from toothpaste, remove a seatbelt law
Some effects of the quasi-independent variable may remain even after it is removed
if you did a time series study before and after introduction of reading program, and then removed the program, reading may not decrease, the children may not regress because they did learn to read.
Removal of the quasi-independent variable may produce effects that are not due to the quasi-independent variable

26. Control Group Interrupted time series
Measure more than one group on several occasions, but only one group receives the quasi-independent variable.
O1 O2 O3 O4 X O5 O6 O7 O8
O1 O2 O3 O O5 O6 O7 O8
Helps to rule out history effects, and we can be more certain the a change was due to X rather than an outside influence.
Could still have a local history effect.

27. Program Evaluation
Used to assess effectiveness of interventions (or programs) and provide feedback to the administrators
Assess needs, process, outcome, and efficiency of social services.

28. Descriptive Research (Leary, 2004)
Describe patterns of behavior, thoughts, and emotions among a group of individuals.
Provide information about characteristics about the sample rather than to test hypotheses.
1) Survey: most common type of descriptive research.
Select a sample of the population using predetermined questions
Surveys are usually questionnaires or interviews

29. 2) Demographic Research:
describes patterns of life events and experiences like birth, marriage, employment etc.
3) Epidemiological Research:
study the occurrence of disease in groups of people
Psychologists may study prevalence of psychological disorders.

30. Correlational Research(Leary, 2004)
Examine whether variables are related to one another (whether they vary together).
Correlation coefficient: statistic indicating how well two variables are related to one another (how well they vary together) in a linear fashion.
Must obtain a score on each variable for each participant.
Pearson correlation coefficient (r): most common. Values range from to +1.00
The direction of the relationship is indicated by the sign of the correlation coefficient.

31. Positive correlation: indicates a direct, linear, positive relationship (as one variable increases the other variable also increases).
Negative correlation: indicates a direct, linear, negative relationship (as one variable increases the other variable decreases)
Magnitude of the correlation: the numerical value (ignoring the sign) which expresses the strength of the relation
Correlation of .33, indicates that the variables are not as strongly related as variables with a correlation of .65
The stronger the correlation the more tightly the data cluster around the mean

37. Two variables may be related in a curvilinear fashion.
The correlation will be 0 but the variables may still be related in a non-linear way.

38. Basic Issues in Experimental Research (Leary, 2004)
Experimental designs allow researchers to make cause and effect conclusions.
Three characteristics of a true experiment:
Researcher must vary at least one independent variable and assess its effects on a dependent variable.
Researcher must assign participants to experimental conditions in a way that ensures initial equivalence.
Researcher must control extraneous variables that may influence the participants’ behavior.

39. Assigning participants to conditions:
Want to ensure that the participants are the same before they are assigned to conditions, so effects are due to the manipulation of the IV and not due to pre-existing participant characteristics.
Between subject designs:
Simple random assignment: Each participant has an equal probability of being placed in each condition.
Matched random assignment: Test the participants on a measure related to the dependent variable and then assign to conditions by matching to ensure you have the same number of people who are high and low on the measure in each condition

40. Within-subjects design
Repeated measures design: each participant completes all conditions
No need for random assignment
Participants may participate in the experimental and control group or in all the different levels of the independent variable
More powerful than between subjects design
Because the participants serve as their own controls
Requires less participants (can have 30 who participate in all three conditions, instead of 30 per condition making 90).

41. Order effects: the order in which the levels of the independent variable are received may affect the participant’s behavior
If studying memory for words under different lighting conditions (each condition has more light) participants may be tired by the last condition which may reduce performance.
Participants may show a practice effect in that they get better at the task in subsequent conditions.

42. Counterbalancing: A procedure in which the order of conditions in a repeated-measures design is arranged so that each condition occurs equally often in each order.
Carryover effects: occurs when the effects at one level of the independent variable are still present at another level (condition).
Must ensure drug of one dosage wears off before the next conditions started

43. Internal validity threats:
Biased assignment of participants to conditions: participants in each condition differ at the beginning, so differences in the dependent variable may reflect pre-existing differences among the participants rather than differences due to the independent variable

44. Random Assignment Biased Assignment A B B C B C A B C B C A A B B
C B A B B
A A B
B C B
B C
Biased Assignment
A A A
B B B
B B
A B B C
B C A B C B
C A A B B
C B A B B
A B B
B C C
C C

45. Differential attrition: participants who do not continue in the study (drop out). Attrition can occur at a different rate in the different conditions
Problematic when more participants drop out of one condition as compared to the other condition
People who drop out may be different than those who stay (more scared of experiment, less motivated).
Pretest sensitization: taking a pretest may affect how participants behave in the experiment, so it is hard to determine whether effect is due to the pretest or the independent variable.

46. History: history effects can effect the DV
Testing anxiety in participants, perhaps a participant in one group had just gone through a very anxious situation and may be more anxious already due to other factors than in the experiment.
Maturation: Participants may change overtime, may be difficult to distinguish effect of the independent variable from maturation changes over time.
More problematic in research with children.
Miscellaneous design confounds: due to participants being treated differently in different conditions, which results in confounding.

47. Experimenter expectancy effects: researchers may observe behavior in a biased way that reflects what they expect to happen.
Their expectations can distort the results
Demand characteristics: participants may behave differently because of noticeable aspects of the experiment
They may be able to guess what the researchers are researching and act accordingly.
Double-blind procedure: neither the participant nor the researcher knows which condition a participant is in.
Helps to eliminate experimenter expectancy effects and demand characteristics

48. Placebo Effects: an artifact that occurs when participant's expectations about what effect an experimental manipulation is supposed to have influence the dependent variable
If participants think they are in a drug group they may be more likely to say the drug produced an effect.
Placebo control group: receive a pill but with no drug, so participants do not know if they are truly receiving the drug