1 Chapter population = the set of all the individuals of interest in a particular study - parameter: a characteristic that describes a population sample = a set of individuals selected from a population, usually intended to represent the population in a research study - statistic: a characteristic that describes a sample descriptive statistics = statistical procedures used to summarize, organize, and simplify data inferential statistics = techniques that allow us to study samples and then make generalizations about the populations from which they were selected independent variable = the variable that is manipulated by the researcher dependent variable = the variable that is observed in order to assess the effect of the treatment attribute (subject) variables = these are characteristics such as age, gender, and intelligence that vary from one individual to another correlational method = two variables are observed to determine whether there is a relationship between them - observe the two variables as they exist naturally for a set of individuals - correlation DOES NOT imply causation experimental method = used to demonstrate a cause-and-effect relationship between two variables - the researcher manipulates one variable by changing its value to determine whether the second variable is effected quasi-experimental design = nonexperimental study that compares groups that were not created by manipulating an independent variable - these groups are instead determined by a participant variable (male vs. female) or a time variable (before treatment vs. after treatment) scales of measurement: 1. nominal scale = consists of a set of categories that have different names o label and categorize observations, but do not make any quantitative distinctions between observations 2. ordinal scale = consists of a set of categories that are organized in an ordered sequence o measurements rank observations in terms of size or magnitude (rank) 3. interval scale = consists of ordered categories that are all intervals of exactly the same size o equal differences between numbers on the scale reflect equal differences in magnitude, but ratios of magnitude are not meaningful 4. ratio scale = an interval scale with the additional feature of an absolute zero point o ratios of numbers do reflect ratios of magnitude continuous variable = a variable with an infinite number of possible values that fall
that fall between any two observed values - divisible into an infinite number of fractional parts discrete variable = a variable that consists of separate, indivisible categories - no values can exist between two neighboring categories basic statistical symbols = population mean M = sample mean = population variance s = sample variance = population standard deviation s = sample standard deviation
Chapter 2 frequency distributions - proportion = f/N interval width for a grouped frequency distribution: (highest score lowest score) + 1 10 real limits = the boundaries of intervals for scores that are represented on a continuous number line - used in cumulative frequency distributions and binomial - distributions not used in sample distributions histogram and polygon - interval or ratio data bar graph - ordinal or nominal data
Chapter 3 measures of central tendency 1. mean 2. median o extreme scores or skewed distributions o undetermined values o open-ended distributions o ordinal scale 3. mode o nominal scale o discrete variables o describing shape weighted mean = the overall mean of a combined group of scores X + X.
n +n adding/subtracting a constant to each raw score - the same constant will be added/subtracted to the mean multiplying/dividing each raw score by a constant - the mean will be multiplied/divided by the same constant positive skew - mode (at peak) median mean negative skew - mean median mode (at peak)
Chapter 4 measures of variablilty 1. range URL maximum value LRL minimum value 2. semi-interquartile range Q Q. 2 o extreme scores o sample size o stability under sampling o open-ended distributions 3. variance o sample size o stability under sampling 4. standard deviation o sample size o stability under sampling deviation = distance from the mean X sum of squares = sum of the squared deviation scored variance = the mean of the squared deviation scores standard deviation = describes how variable, or how spread out, the scores are in a distribution - measures the distance from the mean - allows us to interpret individual scores degrees of freedom = n 1 - the sample standard deviation will always be larger than the population standard deviation, so n 1 corrects that in order to make it unbiased
a sample statistic is unbiased if the average value of the sample statistic, obtained over many different samples, is equal to the population parameter adding/subtracting a constant to each raw score - standard deviation: remains the same - variance: remains the same - range: remains the same multiplying/dividing each raw score by a constant - standard deviation: multiplied/divided by the same constant - variance: changes according to the standard deviation - range: multiplied/divided by the same constant
Chapter 5 and 6 z-score = specifies the precise location of each X value within a distribution - the numerical value specifies the distance from the mean by counting the number of standard deviations between X and - used to standardize an entire distribution o mean = 0 o standard deviation = 1 probability - forms a direct link b/t samples and the populations from which they come from - random sampling = requires that each individual in the population has an equal chance of being selected, and the probability of being selected must stay constant from one selection to the next - binomial values = when a variable is measured on a scale consisting of exactly two categories n = number of events = pn = npq p + q = 1.00 o pn and qn must be greater than 10 in order to assume you have a normal distribution o must be done through random sampling in order to assure the selection process in unbiased o normal distribution in continuous and provides a modal for computing binomial distributions normal distribution approximates binomial distribution