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Flashcards in Research and Program Development Deck (66):
1

Dependent Variable

The variable that is (HOPEFULLY) changed based on your experiment, the outcome variable

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Independent Variable

What is controlled, utilized, given in the experiement

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Parsimony and Occam’s Razor

Interpreting results in the simplest way

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Confounding/Flawed Research

Multiple testing (seeing multiple counselors, receiving multiple treatments) that is not controlled for in the experiment)

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Basic Research

Advances our theory

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Applied Research

Advances our practices

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Control group

Does not receive the experimental treatment, no exposure to the independent variable

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Hypothesis Testing

Developing an experiment in order to explore a hunch or idea developed by R. A. Fisher

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Null Hypothesis

States that the treatment or IV will not have an affect

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Alternative Hypothesis

States that the treatment or IV does affect the outcome of the experiement

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Between Subjects Design

Different subjects get exposure or lack of exposure to different things

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In Subjects Design

One pool of subjects receive or don’t receive the treatment (pre-test post-test design)

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Parameter

Property that defines a sample (age, sex, etc.)

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Probability

The likelihood that something will happen, also known as significance level, P for our field is generally .05 but can range from .000 to .01 and still be considered significant
findings or not due to chance, P can be translated into a percentage that describes the portion of the sample whose results were achieved by chance (i.e. .05 = 5% of the sample’s scores were obtained by chance – not your experimental design)

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Type I Error or Alpha Error

Rejecting the null when it is true (saying there is significance in your treatment when there isn’t), increasing P levels will reduce this error, increasing sample size will reduce this error

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Type II Error or Beta Error

Accepting the null when it is not true (saying there isn’t significance in your treatment when there is), increasing sample size will reduce this error, increasing P will increase the chance of this error

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T Test

Used for two samples to compare means, you obtain a single t score and compare it to the critical t value based on the sample size and your significance level and if the t value you found is greater than the critical t you have significance

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F Statistic

Used for more than two groups, represents and ANOVA test, same process used as with the t test

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Two Way ANOVA or MANOVA

Used for more than two groups and more than one IV

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Correlation

Represents a relationship between two variables, ranges from -1.00 to 1.00, the closer to -1 or 1 the stronger the relationship, can have negative correlation or positive, a score close to 0 represents no or low correlation, strong correlation does not imply causality

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Baseline Measure

Testing before any IV has been performed

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Single-Blind Study

Either the researchers or the participants (but not both) are unaware of what group each represents

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Double-Blind Study

Neither the researchers nor subjects know what category or group they belong to

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Normal Curve

Bell shaped, mean, median, and mode all fall on the same line, 68% of scores fall in -1 to +1 standard deviation, 95% fall within -2 to +2 standard deviations, 99.7% fall into -3 to +3 standard deviations

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Negatively Skewed or Left Skewed

Distribution with outliers towards the negative side of the x axis

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Positively Skewed or Right Skewed

Distribution with outliers to the postive side of the x axis

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Mean

Average of scores, most commonly used statistic, represented with X with a bar over it, strongly effected when dealing with a skewed distribution

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Median

Middle score

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Mode

Most common number/score, score obtained most frequently

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Bimodal Distribution

Has 2 modes or peaks

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Factorial Design

Has more than one IV

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Raw Score

Simplest view of a score, need more information to compare or evaluate the score

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X Axis or Abscissa

Horizontal axis where IV scores are recorded

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Y Axis or Ordinate

Vertical axis where you plot the frequency of the dv (deviation)

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Replication

Equates to increased reliability

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Range

Measures a spread of scores by subtracting lowest score from highest

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Scatterplot or Scattergram

Each score represents a point on the graph, can give a visual representation of correlation

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Variance

Measure of how scores are arranged around a measure of central tendency (mean, median, or mode), this is standard deviation squared (if SD for a sample is 4 then the variance is 16)

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Z Scores

The same thing as a standard deviation, also known as standard scores

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Platykurtic Distribution

Low, long, flat curve

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Leptokurtic Distribution

High, spiked, narrow curve

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Nominal Scale

Simplest type, catagorical (i.e. male, female, democrat, republican, etc)

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Ordinal Scale

Ordered scale (i.e. 1st, 2nd, 3rd most important, etc.)

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Interval Scale

No true zero, numbers represent true, distinct, equal distances (i.e. IQ score, temperature in Celsius or Fahrenheit , etc.)

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Ratio Scale

Has a true zero, numbers are true, distinct, and equal distances (i.e. Kelvin, height, weight, etc.)

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Naturalistic Observation

Researcher does not manipulate or control variables, just watches/observes/records

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Survey

Simplest form of research, need a 50-75% return rate to establish accuracy

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Placebo Effect

Showing an effect or reaction to a treatment that you believe you are getting but are not really beign exposed to

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Hawthorne Effect

If subjects know they are being observed, they tend to perform better

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Rosenthal Effect or Experimenter Expectancy Effect

If the experimenter provides other observers with information (they will excel or they will do worse, etc.) then the observers notice changes

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Halo Effect

When a trait which is not being evalutated impacts the observer’s rating

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Statistical Regression

Implies that the more a test is administered, the more scores will move to the central mean

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Standardized Test

Are normed and have specific proceedures for scoring and administering

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Counterbalancing

Changing the order that iv are administered

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Random Sampling

Made by change, every member of the population has an equal opportunity

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Stratified Sampling

Allows for specific characteristics to be represented in random sampling to mimic the overall population

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Cluster Sampling

Used when the population whole is not known, not as accurate as a random sample

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Horizontal Sampling

Subjects are selected from single socioeconomic group

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Vertical Sampling

Subjects are selected from two or more socioeconomic groups

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Systematic Sampling

Pulling every nth person from the sample (2000 in your population, you pull every 5th, 5, 10, 15, 20, 25, etc)

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Parametric Test

Scores are normally distributed

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Nonparametric Tes

Scores are skewed

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Inductive Logic

From specific example to generalized

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Deductive Logic

From generalized knowledge to specific

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Standard Error of Measurement

Allows you to predict a person’s score if they were to retake a test

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Likert Scale

Numerical range that represents how someone feels or their opinion