Soc 300 & 400
Research Methods
Fall 2013
Measurement: A scheme for the assignment of numbers or symbols to specify different characteristics
of a variable
Variable: An observable characteristic of an object or event that can be described according to some
well-defined classification scheme
Data (plural): the reports of observations of variables
Population: Any class of phenomena arbitrarily defined on the basis of its unique and observable
characteristics
Sample: A collection of phenomena so selected as to represent some well-defined population
Descriptive statistics: Calculated values that represent certain overall characteristics of a body of data
Sampling statistics: Calculated values that represent how sample characteristics are likely to vary from
population characteristics
Independent variable: A phenomenon that is manipulated by the researcher and that is predicted to
have an effect on another phenomenon—also indicted by the letter x.
Dependent variable: A phenomenon that is affected by the researcher’s manipulation of another
phenomenon—also indicated by the letter y.
Scale: a specific scheme for assigning numbers or symbols to designate characteristics of a variable
Nominal scale: the assignment of numbers or symbols to designate subclasses that represent unique
characteristics
Ordinal scale: the assignment of numbers or symbols to identify ordered relations of some
characteristic, the order having unspecified intervals
Interval scale: the assignment of numbers to identify ordered relations of some characteristic, the order
having arbitrarily assigned and equal intervals but an arbitrarily assigned zero point
Ratio scale: the assignment of numbers to identify ordered relations of some characteristic, the order
having arbitrarily assigned and equal intervals but an absolute zero point
Validity: the degree to which researchers measure what they claim to measure
Reliability: the external and internal consistency of measurement
Mode: the most frequent score in a distribution
Median: the midpoint or midscore in a distribution
Mean: the sum of scores in a distribution divided by the number of scores
Range: the highest score in a distribution minus the lowest score
Variance: the mean of the squared deviation scores about the mean of a distribution
Statistic (singular): a characteristic of a sample
Parameter: a characteristic of a population
Statistical inference: the process of estimating parameters from statistics
Hypothesis: a statement of a relationship between two variables.
Null hypothesis: a statement that statistical differences or relationships have occurred for no reason
other than laws of chance operating in an unrestricted manner
Research hypothesis: a statement expressing differences or relationships among phenomena, the
acceptance or nonacceptance of which is based on resolving a logical alternative with a null hypothesis
Quantitative analysis: the numerical representation and manipulation of observations for the purpose
of describing and explaining the phenomena that those observations reflect
Significance: the level of calculated probability was sufficiently low as to serve as grounds for rejection
of the null hypothesis. The level of significance set by the researcher is called the alpha level.
example: a relationship is significant at the .05 level if the likelihood of its being only a function
of sampling error is no grater than 5 out of 100.
Type I error: rejecting a null hypothesis when it should have been the acceptable alternative
Type II error: accepting a null hypothesis when it should have been the rejected alternative
Statistical Power: the probability of rejecting a null hypothesis that is, in fact, false
Continuous variable: a variable whose attributes form a steady progression, such as age or income
Discrete variable: a variable whose attributes are separate form one another, or discontinuous, such as
gender or religious affiliation
Normal distribution: a distribution whose shape is symmetrical with the mean, median, and mode
being the same. The total area under the distribution is equal to 1.
Skewness: the description of a distribution with a peak that tends to be displaced at one or the other end
of a distribution that has a tail strung out in the opposite direction.
Bimodal distribution: a distribution curve with two peaks.
Variance: the mean of the squared deviation scores about the mean of a distribution.
Standard deviation: the square root of the variance.