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PSYC 2005: Research Methods and Data Analysis I > Exam 1 Vocab > Flashcards

Exam 1 Vocab Flashcards

Familiarize myself with relevant terms and equations

1
Q

Statistics

A

Branch of mathematics that focuses on the organization, analysis, and interpretation of a group of numbers.

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2
Q

Descriptive Statistics

A

Procedures for summarizing a group of scores or otherwise making them more comprehensible.

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3
Q

Inferential Statistics

A

Procedures for drawing conclusions based on the scores collected in a research study but going beyond them.

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4
Q

Variable

A

A characteristic that can have different values.

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5
Q

Values

A

Possible numbers or categories that a score can have.

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6
Q

Score

A

A particular person’s value on a variable.

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7
Q

Numeric Variable

A

A variable whose values are numbers (as opposed to a nominal variable). Also called a quantitative variable.

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8
Q

Equal-Interval Variable

A

A variable in which the numbers stand for approximately equal amounts of what is being measured.

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9
Q

Ratio Scale

A

An equal-interval variable is measured on a ratio scale if it has an absolute zero point, meaning that the value of zero on the variable indicates a complete absence of the variable.

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10
Q

Discrete Variable

A

A variable that has specific values and that cannot have values between these specific values.

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11
Q

Continuous Variable

A

A variable for which, in theory, there are an infinite number of values between any two values.

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12
Q

Rank-Order Variable

A

A numeric variable in which the values are ranks, such as class standing or place finished in a race. Also called an ordinal variable.

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13
Q

Nominal Variable

A

A variable with values that are categories (that is, they are names rather than numbers). Also called a categorical variable.

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14
Q

Levels of Measurement

A

Types of underlying numerical information provided by a measure, such as equal-interval, rank-order, and nominal (categorical).

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15
Q

Frequency Table

A

A listing of the number of individuals having each of the different values for a particular variable.

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16
Q

Interval

A

A range of values in a grouped frequency table that are grouped together. (For example, if the interval size is 10, one of the intervals might be from 10-19.)

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17
Q

Grouped Frequency Table

A

A frequency table in which the number of individuals (frequency) is given for each interval of values.

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18
Q

Histogram

A

A bar-like graph of a frequency distribution in which the values are plotted along the horizontal axis and the height of each bar is the frequency of that value; the bars are usually placed next to each other without spaces, giving the appearance of a city skyline.

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19
Q

Frequency Distribution

A

A pattern of frequencies over the various values; what a frequency table, histogram, or frequency polygon describes.

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20
Q

Unimodal Distribution

A

A frequency distribution with one value clearly having a larger frequency than any other.

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21
Q

Bimodal Distribution

A

A frequency distribution with two approximately equal frequencies, each clearly larger than any of the others.

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22
Q

Multimodal Distribution

A

A frequency distribution with two or more high frequencies separated by a lower frequency; a bimodal distribution is the special case of two high frequencies.

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23
Q

Rectangular Distribution

A

A frequency distribution in which all values have approximately the same frequency.

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24
Q

Symmetrical Distribution

A

A distribution in which the pattern of frequencies on the left and right side are mirror images of each other.

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25
Q

Skewed Distribution

A

A distribution in which the scores pile up on one side of the middle and are spread out on the other side; distribution that is not symmetrical.

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26
Q

Floor Effect

A

A situation in which many scores pile up at the low end of a distribution (creating skewness) because it is not possible to have a lower score.

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27
Q

Ceiling Effect

A

A situation in which many scores pile up on the high end of a distribution (creating skewness) because it is not possible to have a higher score.

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28
Q

Normal Curve

A

A specific, mathematically defined, bell-shaped frequency distribution that is symmetrical and unimodal; distributions observed in nature and in research commonly approximate it.

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29
Q

Kurtosis

A

The extent to which a frequency distribution deviates from a normal curve in terms of whether its curve in the middle is more peaked or flat than the normal curve.

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30
Q

Central Tendency

A

A typical or most representative value of a group of scores.

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31
Q

Mean

A

An arithmetic average of a group of scores; the sum of the scores divided by the number of scores. Written as M= SigmaX/N where SigmaX is the sum of all scores in the distribution of the variable X and N is the number of scores.

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32
Q

Mode

A

The value with the greatest frequency in a distribution.

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33
Q

Median

A

The middle score when all the scores in a distribution are arranged from lowest to highest.

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34
Q

Outlier

A

A score with an extreme value (very high or very low) in relation to the other scores in the distribution.

35
Q

Variance

A

The measure of how spread out a set of scores are; the average of the squared deviations from the mean. Written as SD^2=Sigma(X-M)^2/N where SD is the standard deviation, SD^2 is the variance, Sigma(X-M)^2 is the sum of squared deviations from the mean, and N is the number of scores.

36
Q

Deviation Score

A

A score minus the mean. Written as X-M where X is a score and M is the mean.

37
Q

Squared Deviation Score

A

The square of the difference between a score and the mean. Written as (X-M)^2 where X is a score and M is the mean.

38
Q

Sum of Squared Deviations (SS)

A

The total of all the scores of each score’s squared difference from the mean. Written as Sigma(X-M)^2 where X is a score and M is the mean and Sigma is “the sum of” what follows or SS=Sigma(X-M)^2. Computational Formula: Sigma(X^2)-((SigmaX)^2/N)/N.

39
Q

Standard Deviation

A

The square root of the average of the squared deviations from the mean; the most common descriptive statistic for variation; approximately the average amount that scores in a distribution vary from the mean. Written as SD=SquareRoot(Sigma(X-M)^2/N) where SD is standard deviation, Sigma is “the sum of” what follows, X is a score, M is the mean, and N is the number of scores. Computational Formula: SD=SquareRoot(Sigma(X^2)-((SigmaX)^2/N)/N).

40
Q

Computational Formula

A

An equation mathematically equivalent to the definitional formula. Easier to use for figuring by hand, it does not directly show the meaning of the procedure.

41
Q

Definitional Formula

A

The equation for a statistical procedure directly showing the meaning of the procedure.

42
Q

Z-Score

A

The number of standard deviations (SD) that a score is above (or below, if it is negative) the mean of the distribution; it is thus an ordinary score transformed so that it better describes the score’s location in a distribution. The formula for changing a raw score to a Z-score is written as Z=X-M/SD where Z is the Z-score, X is the raw score, M is the mean, and SD is the standard deviation.

43
Q

Raw Score

A

An ordinary score (or any number in a distribution before it has been made into a Z score or otherwise transformed). The formula for changing a Z-score to a raw score is X=(Z)(SD)+M where Z is the Z-score, X is the raw score, M is the mean, and SD is the standard deviation.

44
Q

Normal Distribution

A

The frequency distribution that follows a normal curve.

45
Q

Normal Curve Table

A

A table showing percentages of scores associated with the normal curve; the table usually includes percentages of scores between the mean and various numbers of standard deviations above the mean and percentages of scores more positive than various numbers of standard deviations above the mean.

46
Q

Population

A

An entire group of people to which a researcher intends the results of a study to apply; a larger group to which inferences are made on the basis of a particular set of people (sample) studied.

47
Q

Sample

A

Scores of the particular group of people studied; usually considered to be representative of the scores in some larger population.

48
Q

Random Selection

A

A method for selecting a sample that uses truly random procedures (usually meaning that each person in the population has an equal chance of being selected); one procedure is for the researcher to begin with a complete list of all the people in the population and select a group of them to study using a table of random numbers.

49
Q

Population Parameter

A

The actual value of the mean, standard deviation, and so on, for the population; usually population parameters are not known, though often they are estimated based on information in samples. mu=population mean, lowercase sigma^2=population variance, lowercase sigma=population standard deviation

50
Q

Sample Statistics

A

A descriptive statistic, such as the mean or standard deviation, figured from the scores in a group of people studied.

51
Q

Probability

A

The expected relative frequency of an outcome, the proportion of successful outcomes to all outcomes.

52
Q

Outcome

A

A term used in discussing probability for the result of an experiment (or almost any event, such as a coin coming up heads or it raining tomorrow).

53
Q

Expected Relative Frequency

A

The number of successful outcomes divided by the number of total outcomes you would expect to get if you repeated an experiment a large number of times.

54
Q

Long-Run Relative-Frequency Interpretation of Probability

A

The understanding of probability as the proportion of a particular outcome that you would get if the experiment were repeated many times.

55
Q

Subjective Interpretation of Probability

A

The way of understanding probability as the degree of one’s certainty that a particular outcome will occur.

56
Q

Hypothesis Testing

A

A procedure for deciding whether the outcomes of a study (results for a sample) support a particular theory or practical innovation (which is thought to apply to a population).

57
Q

Hypothesis

A

A prediction, often based on informal observation, previous research, or theory, that is tested in a research study.

58
Q

Theory

A

A set of principles that attempts to explain one or more facts, relationships, or events; psychologists often derive specific predictions from theories that are then tested in research studies.

59
Q

Research Hypothesis

A

A statement in hypothesis testing about the predicted relation between populations (often a prediction of a difference between population means).

60
Q

Null Hypothesis

A

A statement about the relation between populations that is the opposite of the research hypothesis; a statement that in the population there is no difference (or a difference opposite to that predicted) between populations; a contrived statement set up to examine whether it can be rejected as a part of hypothesis testing.

61
Q

Comparison Distribution

A

A distribution used in hypothesis testing. It represents the population situation if the null hypothesis is true. It is the distribution to which you compare the score based on your sample’s results.

62
Q

Cutoff Sample Score

A

A point in hypothesis testing, on the comparison distribution at which, if reached or exceeded by the sample score, you reject the null hypothesis. Also called a critical value.

63
Q

Conventional Levels of Significance (p

A

Levels of significance widely used in psychology

64
Q

Statistically Significant

A

The conclusion that the results of a study would be unlikely if in fact the sample studied represents a population that is no different than the population in general; an outcome of hypothesis testing in which the null hypothesis is rejected.

65
Q

Directional Hypothesis

A

A research hypothesis predicting a particular direction of difference between populations–for example, a prediction that the population like the sample studied has a higher mean than the population in general.

66
Q

One-Tailed Test

A

A hypothesis-testing procedure for a directional hypothesis; a situation in which the region of the comparison distribution in which the null hypothesis would be rejected is all on one side (tail) of the distribution.

67
Q

Nondirectional Hypothesis

A

A research hypothesis that does not predict a particular direction of difference between the population like the sample studied and the population in general.

68
Q

Two-Tailed Test

A

A hypothesis-testing procedure for a nondirectional hypothesis; the situation in which the region of the comparison distribution in which the null hypothesis would be rejected is divided between the two sides (tails) of the distribution.

69
Q

Distribution of Means

A

The distribution of means of samples of a given size from a population (also called a sampling distribution of the mean); a comparison distribution when testing hypotheses involving a single sample of more than one individual.

70
Q

Mean of a Distribution of Means

A

The mean of a distribution of means of samples of a given size from a population; the same as the mean of the population of individuals. Written as mu subscript M

71
Q

Variance of a Distribution of Means

A

The variance of the population divided by the number of scores in each sample. Written as lowercase sigma subscript M ^2 = lowercase sigma ^2 / N where N is the number of scores in each sample, lowercase signma ^2 is the population variance, and lowercase sigma subscript M ^2 is the variance of the distribution of means.

72
Q

Standard Deviation of a Distribution of Means

A

The square root of the variance of a distribution of means; also called standard error of the mean (SEM) and standard error (SE).

73
Q

Z-Test

A

A hypothesis-testing procedure in which there is a single sample and the population variance is known.

74
Q

Confidence Interval (CI)

A

Roughly speaking, the range of scores (that is, the scores between an upper and lower value) that is likely to include the true population mean; more precisely, the range of possible population means from which it is not highly that you could have obtained your sample mean.

75
Q

Confidence Limit

A

The upper or lower value of a confidence interval.

76
Q

95% Confidence Interval

A

The confidence interval in which, roughly speaking, there is a 95% chance that the population mean falls within this interval.

77
Q

99% Confidence Interval

A

The confidence interval in which, roughly speaking, there is a 99% chance that the population mean falls within this interval.

78
Q

Decision Error

A

The incorrect conclusion in hypothesis testing in relation to the real (but unknown) situation, such as deciding the null hypothesis is false when it is actually true.

79
Q

Type 1 Error

A

Rejecting the null hypothesis when in fact it is true; getting a statistically significant result when in fact the research hypothesis is not true.

80
Q

Alpha (Greek Letter Lowercase Alpha)

A

The probability of making a Type 1 error; same as significant level.

81
Q

Type 2 Error

A

Failing to reject the null hypothesis when in fact it is false; failing to get a statistically significant result when in fact the research hypothesis is true.

82
Q

Beta (Greek Letter Uppercase Beta)

A

The probability of making a Type 2 error.

83
Q

Effect Size

A

The standardized measure of difference (lack of overlap) between populations. Effect size increases with greater differences between means. Written as d= mu subscript 1 (pop. mean 1) - mu subscript 2 (pop. mean 2) / lowercase sigma (standard deviation). Essentially the measure of the difference between two population means.

84
Q

Effect Size Conventions

A

Standard rules about what to consider a small (d=.20), medium (d=.50), and large (d=.80) effect size, based on what is typical in psychology research; also known as Cohen’s conventions.

PSYC 2005: Research Methods and Data Analysis I (2 decks)

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