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Alternative hypothesis

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The hypothesis used in hypothesis testing that is contrary to the null hypothesis / Effect is present

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Between-group design

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A type of experimental design in which two or more groups of subjects are tested by a different testing factor simultaneously

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Between-subject design aka

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Between-group design / Independent design

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Bimodal

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Describes a frequency distribution having two modes

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Binary variable

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A categorical variable that can take on one of two categories

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Boredom effect

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Participants may perform differently in the second condition because they are tired or bored from having completed the first condition

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Categorical variable

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A variable that can take on one of a limited, and usually fixed, number of possible values

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Central tendency

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The centre of a distribution. Three common measures of central tendency are mean, median and mode.

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Confounding variable

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An extraneous variable in a statistical model that correlates (directly or inversely) with both the dependent variable and the independent variable

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Content validity

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aka Logical validity, refers to the extent to which a measure represents all facets of a given construct

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Continuous variable

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A variable that gives a score to each entity and can take on any value on the measurement scale that we are using / Two types are Interval variables and Ratio variables

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Correlational research aka

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Cross-sectional research

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Counterbalancing

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A method for controlling order effects in a repeated measures design. Randomization is used to determine the order in which conditions are completed.

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Criterion validity

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A measure of how well a variable or set of variables measures what they claim to measure. Ideally this is assessed by relating outcomes to known real-world observations

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Cross-sectional research

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Observational research where we observe what naturally goes on in the world without directly interfering with it

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Dependent variable

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A variable (often denoted by y ) whose value we suspect depends on that of another / Proposed outcome / Outcome variable

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Discrete variable vs. Continuous variable

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The distinction between a discrete and continuous variable can be blurred. Age is continuous, but usually described in discrete terms. The number of children is discrete, but the average number may be meaningfully described in continuous terms.

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Ecological validity

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A measure of how much the methods, materials and setting of the study approximate the real-world that is being examined

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Experimental hypothesis aka

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Alternative hypothesis

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Experimental research

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Where one variable is directly manipulated to test it's effect on another

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Falsification

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The act of disproving a hypothesis or theory.

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Frequency distribution

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A mathematical function showing the number of instances in which a variable takes each of its possible values

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Histogram

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A diagram consisting of rectangles whose area is proportional to the frequency of a variable and whose width is equal to the class interval

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Hypothesis

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A prediction from a theory / Used to test a theory

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Independent design aka

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Between-group design / Between-subject design

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Independent variable

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A variable (often denoted by x ) whose variation we suspect does not depend on that of another / Proposed cause / Predictor variable

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Interquartile range

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The middle fifty. IQR = Q3 - Q1

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Interval variable

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Equal intervals on the scale represent equal differences in the property being measured

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Kurtosis

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The sharpness of the peak of a frequency distribution curve

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Leptokurtic

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Having a greater kurtosis than the normal distribution (sharper)

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Level of measurement aka

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Scales of measure

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Lower quartile

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The median of the lower half of the dataset. Q1

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Mean aka

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Expected value. Mean is to a frequency distribution what Expected value is to a random variable.

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Measurement error

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The difference between a measured value of quantity and its true value

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Median

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The value lying at the midpoint of a frequency distribution

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Mode

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The score that occurs most frequently in a dataset

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Multimodal

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Describes a frequency distribution having more than two modes

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Negative skew

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Tail on the left

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Nominal variable

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A categorical variable that can take on one of more than two categories

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Normal distribution

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A function that represents the distribution of many random variables as a symmetrical bell-shaped graph

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Null hypothesis

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A statistical hypothesis that is tested for possible rejection under the assumption that it is true (usually that observations are the result of chance) / Effect is absent

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Ordinal variable

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A step up from nominal variables in that the categories have a logical order. A step down from continuous variables.

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Outcome variable aka

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Dependent variable

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Platykurtic

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Having a lesser kurtosis than the normal distribution. (flatter)

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Positive skew

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Tail on the right

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Practice effect

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Participants my perform differently in the second condition because of familiarity with the experimental situation and/or the measures being used

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Predictor variable aka

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Independent variable

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Probability distribution (Rules)

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1. The events listed must be disjoint. 2. Each probability might, must be between 0 and 1. 3. The sum of the probabilities listed in the probability distribution must total 1.

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Qualitative methods

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Methods that rely primarily on words as its unit of analysis and its means of understanding

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Quartile

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Each of the three values of the random variable that divide a population into four groups

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Randomization is used to

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keep unsystematic variation to a minimum in order to get a more sensitive measure of the experimental manipulation.

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Range

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The difference between the lowest and highest values in a frequency distribution

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Ratio variable

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A step up from Interval variables also requiring that ratios of scores on the scare also make sense. For this to be true the scare must have a true and meaningful zero point

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Reliability

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The degree to which a tool can be interpreted consistently across different situations. A prerequisite to Validity.

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Repeated measures design aka

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Within-subject design

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Second quartile

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The median

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Skew

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Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean

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Systematic variation

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Variability due to specific experimental manipulation / Applies to both Independent-measures design and Repeated-measures design

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Tertium quid

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A third thing that is indefinite and undefined but is related to two definite or known things

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Test-retest reliability aka

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Repeatability

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Theory

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A supposition or a system of ideas intended to explain something, especially one based on general principles independent of the thing to be explained

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Unsystematic variation

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Variability is due to random factors / Bigger in Independent-measures design than in Repeated-measures design

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Upper quartile

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The median of the upper half of the dataset. Q3

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Validity

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The degree to which a tool measures what it claims to measure

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Within-subject design

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A type of experimental design in which all participants are exposed to every treatment or condition.

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Z-scores (in R code)

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(X - mean(X)) / sd(X)

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Quantitative methods

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Methods that rely primarily on numbers as the main unit of analysis

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Positive kurtosis

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Peak is sharper than that of the normal curve

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Negative kurtosis

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Peak is flatter than that of the normal curve

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The only way a nominal variable can be used is to consider _______

answer

frequencies

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Unsystematic variation is bigger in Independent-measures design than in Repeated-measures design, therefore,

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the effect of experimental manipulation is likely to be more apparent in Repeated-measures design

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95 percent of z-scores lie between

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-1.96 and 1.96

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99 percent of z-scores lie between

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-2.58 and 2.58

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99.9 percent of z-scores lie between

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-3.29 and 3.29

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The null hypothesis is needed because

answer

we can't prove the alternative hypothesis using statistics, but we can reject the null hypothesis.

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