Flashcards in Research and Program Development Deck (66):

1

## Dependent Variable

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

2

## Independent Variable

### What is controlled, utilized, given in the experiement

3

## Parsimony and Occam’s Razor

### Interpreting results in the simplest way

4

## Confounding/Flawed Research

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

5

## 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)

15

## 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

17

## 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

38

## 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)

39

## 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

66