Kin 300 Exam 2

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Sample

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A subset of a population.

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Descriptive statistics

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Descriptive statistics are used to describe the basic characteristics of the data in a study. They provide simple summaries about the sample being measured. They can be expressed in numerical and/or graphical form. They form the basis of virtually every quantitative analysis of data.

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Inferential statistics

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Inferential statistics are used to draw conclusions about a population based on information contained in a sample. Information is obtained from a sample and generalized to a population.

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Statistic

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A value, or quantity, that represents a characteristic of a sample such as the sample mean or standard deviation.

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Variable

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Something that can take on more than one value. A variable might be expected to vary over time. Values of a variable would probably be expected to differ between individuals.

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“Levels” of a variable

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The various values that a variable may assume. For example, red, white, and blue are among the levels of the variable “color.” Levels of the variable “G.P.A.” include: 2.5, 3.2, and 4.0.

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Population

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All members of a specified group.

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Parameter

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A value, or quantity, that represents a characteristic of a population such as the population mean or standard deviation.

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Quantitative variable

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A variable whose levels are described numerically. Examples include temperature, % body fat, and time.

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Continuous variable

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A quantitative variable that can be reduced to an infinite number of possible values, depending on the accuracy of the measuring instrument. Examples include height, weight, and distance.

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Discrete variable

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A variable, either qualitative or quantitative, with a finite number of levels that cannot be subdivided meaningfully. Examples include heart rate, IQ, and color.

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Nominal Level of Measurement

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Variables are categorical, qualitative, and discrete in nature. Although numbers can be used to represent levels of the variables, the numbers are treated as labels. Examples include brand of shoes, Social Security number, and gender.

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Ordinal Level of Measurement

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Variables are categorical and discrete in nature. Unlike variables at the nominal level, variable levels at the ordinal level of measurement can be rank-ordered meaningfully. Examples include finish position in a race (1st, 2nd, 3rd, . . .) and t-shirt size (S, M, L, XL).

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Interval Level of Measurement

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Variables at this level may be quantitative or qualitative, discrete or continuous. They possess the characteristics of ordinal level variables with the added characteristic of equal intervals between levels. Examples include temperature (F), shoe size, and IQ.

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Ratio Level of Measurement

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Ratio level variables possess all of the characteristics of interval level variables with the added characteristic of a measurement baseline. This baseline represents a zero point on the measurement scale or an absolute absence in quantity of the variable being measured. Examples, measured quantitatively, include height, weight, and distance.

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Dependent variable

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The outcome measure; the variable that is measured in a research study. It is free to vary and is affected by, or “dependent” on, the actions of other variables such as the independent variable(s).

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Independent variable

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A variable that you identify as having a potential influence on your outcome measure. This might be a variable that you control, like a treatment. It also might represent a demographic factor like age or gender.

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Random sample

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A random sample is drawn in such a way that all members of the population have an equal chance of being selected. This type of sampling is rarely used in research with human subjects.

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Biased sample

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A biased sample is drawn in such a way that some members of the population are more likely to be chosen than others.

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Convenience sample

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A convenience sample is drawn from an “intact class” or by asking people to volunteer. The sample is not randomly chosen and is typically used because of the ready availability of the subjects. This is a biased sample.

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Stratified sample

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A sample chosen from a population that has been subdivided based upon predetermined characteristics such as gender, race, and socio-economic status. This is the sampling method used for many nationwide polls.

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Systematic sample

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A sample obtained using a pre-determined system (not random); for example, choosing every 10th subject from the population.

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External validity

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The degree to which the experimental results can be generalized to the target population. The highest degree of external validity exists when all responses from subjects in the sample can be seen in the population. For example, an average loss of 10 pounds in the sample would be mirrored by an average loss of 10 pounds in the population.

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Internal validity

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The degree to which changing the level of the independent variable causes a change in the dependent variable. In an experiment, the highest degree of internal validity exists when all fluctuations in the dependent variable can be attributed to the effect of the independent variable.

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Avis Effect

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The Avis effect occurs when subjects in a control group discover they are in a control group and they react by “trying harder.” This is a threat to the internal validity of a study.

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Placebo Effect

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The measurable, observable, or felt improvement in health or behavior not attributable to a medication or treatment that has been administered.

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Hawthorne Effect

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The Hawthorne effect occurs when subjects in a treatment group improve their performance because they are aware they are being treated or tested. This is a threat to the internal validity of a study.

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Single-blind study

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A study in which the subject does not know whether he or she is in the treatment or control group.

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Rosenthal Effect

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The Rosenthal effect occurs when a researcher inadvertently influences subjects’ performances, which consequently affects the outcome of a study. This is a threat to the internal validity of a study.

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Double-blind study

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A study in which neither the subject nor the experimenter knows to which group the subject has been assigned.

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Mean

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The arithmetic average of a set of scores.

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Median

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The middle point in a set of scores.

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Mode

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The most frequently occurring score in a set of scores.

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Standard deviation

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A measure of variability around the mean. The standard deviation is in the same units of measurement as the mean. For example, if the mean represents average time in seconds, the standard deviation represents variability in seconds.

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Range

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A measure of variability representing the distance from the highest score to the lowest score.

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Skewness

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Skewness is a characteristic of an asymmetrical distribution. A distribution is “negatively” skewed when a higher frequency of scores are found above the mean than below it. A distribution is “positively” skewed when a higher frequency of scores are found below the mean than above it