GRE® Psychology Measurement, Methodology and Other: Measurement and Methodology Part 1

It is a general procedure which psychologists use for gathering and interpreting data.

A well-supported, organized explanation of phenomena, built from repeated testing of hypotheses and consistent observations.

Unlike a hypothesis, which is a specific testable prediction, a theory integrates evidence into a broader explanatory framework.

It is obtaining similar results to a previous study using the same methods.

Explaining why something happened after it has occured.

It is where researchers systematically manipulate a variable and observe the response in a laboratory.

A prediction of how two or more factors are related.

Researchers use operational definitions to precisely describe variables in relation to their study.

For example, “effectiveness of studying” can be operationally defined with a test score.

• The factor being manipulated is the independent variable.

• The factor being measured is the dependent variable.

• independent: method of studying (Brainscape versus regular flashcards).

• dependent: amount learned, as measured by their test scores.

It refers to all the individuals to which the study applies.

A subgroup of a population that constitutes participants of a study.

Larger sample sizes are ideal because they are the most representative of the population.

sampling error

Every individual from a population has an equal chance of being chosen for the sample.

Subjects who receive the treatment or manipulation of the independent variable.

Subjects who do not receive any treatment or manipulation.

experimental; control

A between-subjects design uses an experimental group and a control group to compare the effect of the independent variable.

Random assignment is used to assign the sample participants into groups (e.g., experimental drug or placebo).

Random assignment means neither the experimentor nor the participants decide in which group the participants will be, and each participant has an equal chance of being assigned to a given study groups (e.g., treatment vs. placebo).

• Any difference between the experimental group and the control group, besides the effect of the independent variable.

• a.k.a. third variable

• It makes the phenomenon at hand even more difficult to study because of complex interaction effects.

1. experimenter bias

2. demand characteristics

3. placebo effect

4. lack of counterbalancing

This occurs when a researcher’s expectations or preferences about the results of the study influence the experiment.

These are clues the participants discover about the intention of the study that alter their responses.

Responding to an inactive drug with a change in behavior because the subject believes it contains the active ingredient.

Individuals who are being experimented on behave differently than in their everyday life.

A within-subjects design exposes each participant to the treatment and compares their pre-test and post-test results. This design can also compare the results of two different treatments administered.

Research design in which the subjects are unaware if they are in the control or experimental group.

Research design in which neither the experimenter nor the subjects are aware who is in the control or experimental group.

demand characteristics

experimenter bias

Random assignment is not possible in quasi-experiments.

Differences in behavior between:

• males and females

• various age groups

• students in different classes

• establishes a relationship between two variables

• does not determine cause and effect

• used to make predictions and generate future research

1. naturalistic observation

2. surveys

3. tests

1. The researcher manipulates the independent variable.

2. All participants are randomly assigned to the experimental and control condition.

So, for instance, a study that compares how men versus women do on a given task would not be a true experiment because it is not possible to assign people to group (gender). (This example would be a quasiexperiment.)

It consists of field observation of naturally occuring behavior, such as the way students behave in the classroom. There is no manipulation of variables.

• It is a type of correlational research.

• Questionnaires and interviews given to a large group of people about their thoughts or behavior.

• Individuals aim to be politically correct and socially accepted, leading them to give false answers.

Research method that measures individual traits at a specific time and place.

Ex post facto

• Reliable – consistent. When administered properly, does a test give similar results when used on different occasions?

• Valid – useful, meaningful. Does it measure what it claims to measure?

In order to be valid, a measure must be reliable. However, a measure can be reliable without being valid. For instance, imagine a scale that always reads 212 pounds, no matter what the weight is of the person who stands on it. That scale would be a reliable measure, but not a valid measure.

• Detailed examination of one person or a small group.

• Beneficial for understanding rare and complex phenomena in clinical research.

• Not always representative of the larger population.

Strengths:

• determine cause and effect relationship between variables

• control over confounding variables

Weaknesses:

• no real-world generalizability

• expensive

• time-consuming

Strengths:

• easy to administer surveys or tests

• inexpensive

• minimal time needed

• substantial real-world generalizability

Weaknesses:

• no control over confounding variables

• skewed or biased results

• establishes a relationship, not causation

Analysis of numerical data regarding representative samples.

Quantitative

Qualitative

1. nominal

2. ordinal

3. interval

4. ratio

Data that are categorical: Numbers have no meaning except for convenience as labels.

Examples:
Hair Color (possibly coded red = 1; grey = 2; black = 3; brown = 4; blond = 5…)
Political Party (possibly coded Democrat = 1; Republican = 2; Independent/Other =3)
Gender (Male = 1; Female = 2; Prefer not to reply = 3).

Numbers are used as ranks.

Examples:
The runner who wins the race is scored as 1, the runner who comes in second is scored as 2, the third is scored as 3, and so on.

Numbers that have a meaningful difference between them.

Example:
Temperature: The difference between 10°F and 20°F is the same as between 30°F and 40°F.

Numbers that have a meaningful ratio between them on a scale with a real zero point.

Example:
Weight and height: If you weight zero pounds, you have no weight. 100 pounds is twice as heavy as 50 pounds.

interval scale

If the temperature is 0°F, there is not "no temperature." There is not a meaningful ratio between values. 100°F is not twice as hot as 50°F.

Numbers that summarize a set of research data from a sample.

An orderly arrangement of scores indicating the frequency of each score.

• A histogram is a bar graph.

• A frequency polygon is a line graph or a bell curve.

Measures of central tendency describe the most typical scores for a set of research data.

1. mode

2. median

3. mean

Most frequently occurring score in the data set.

The middle score when the data is ordered by size.

Arithmetic average of the scores in the data set.

bimodal

multimodal

• Mean is usually most representative, unless there are extreme outliers that pull the mean in a particular direction.

• Median is less sensitive to outliers, but is a weak statistic.

• Mode is the least representative.

A bell-shaped, symmetrical curve that represents data about many characteristics, including the distribution of many human characteristics.

In a normal distribution, approximately two thirds of the population will be within plus or minus one standard deviation of the norm (mean). Approximately 95% of the population will be within plus or minus two standard deviations of the mean. Over 99% of the population will fall within plus or minus three standard deviations of the mean.

skewed

Positively skewed distributions include a lot of small values and negatively skewed distributions include a lot of large values.

They describe the dispersion of scores for a set of research data.

1. range

2. variance

3. standard deviation

Difference between the largest score and the smallest score.

Average difference between each score and the mean of the data set.

Taller, narrow curves have less variance than short, wider curves.

• allows for comparison between different scales

• subtract mean from each score and divide by standard deviation

• mean has a z score of zero

Percentage of scores at or below a particular score between 1 and 99.

Example:
If you are in the 70th percentile, 70% of the scores are the same as or below yours.

• statistical linear measure of the relationship between two sets of data

• varies from -1 to +1

• helps to make predictions about variables

1. r = +1
   direct relationship: as one variable increases or decreases, the other does the same

2. r = 0
   no relationship

3. r = -1
   inverse relationship: as one variable increases or decreases, the other does the opposite

scatterplot

line of best fit or regression line

• Null hypotheses state that a treatment had no effect.

• Alternative hypotheses state the treatment did have an effect in the experiment.

• Type I errors, or false positives, occur if the researcher rejects a true null hypothesis.

• Type II errors, or false negatives, occur if the researcher fails to reject a false null hypothesis.

it lets you know if the finding is statistically significant, i.e., the likelihood of the findings being the result of chance. The lower the p score, the less likely it is that the findings are due to chance.

In order for a finding to be considered statistically significant, the p score must be less than or equal to .05; in other words, a %5 or less likelihood that the finding is due to chance.

In psychology, a finding is considered statistically significant if the probability (alpha) that the finding is due to chance is less than 1 in 20 (p is less than or equal to 0.05).

meta-analysis

• Guidelines were set in place in the late 20th century to stress responsibility and morality in research and clinical practice.

• Dangerous and inhumane experiments such as Harlow's rhesus monkeys, Zimbardo's prison role-playing, and Milgram's shock test led to the implementation of rules.

• Approve research being conducted at their particular institution.

• Require participants give informed consent after hearing the risks and procedures.

• Require debriefing of participants afterward with results of research.

• Require humane and ethical treatment of animal and human subjects.

Applied

Basic

Wilhelm Wundt

Hermann Ebbinghaus

Kulpe was one of the earliest experimental psychologists who performed numerous experiments to prove his "imageless thought" to try and combat Titchener's work and prove that there were some thoughts that did not have images to be analyzed.

James McKeen Cattell

Created by Simon and Binet in 1905 for the purposes of ranking the intelligence of French children to select for mentally retarded children.

Intelligence quotient

| (IQ)

Lewis Terman

stratified random sampling

matched-subjects design

This is an experimental technique in which we make sure both the experimental and control group will experience both levels of the independent variable, just at different times.

nonequivalent group design

external validity

inferential statistics

outlier

A normal distribution is represented by a normal curve. The scores will exist such that 68% of the scores are within 1 standard deviation of the mean and 96% of the scores will fall within 2 standard deviations of the mean.

Similar to a Z-score, a T-score sets up a curve such that the mean is always 50 and each standard deviation is 10. You simply convert each number to the T-score value for easy comparison and analysis.

• A positive correlation is one in which if one value increases, the other value will increase.

• A negative correlation is one in which if one value decreases, the other value increases.