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SOWK 2502 (2)
Midterm

Midterm Definitions

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Department
Social Work
Course
SOWK 2502
Professor
Adje Van De Sande
Semester
Winter

Description
 Introduction to Statistical Analysis for Social Work | Midterm Definitions ­ Chapter 1 – Introduction ­  Binary Variable: a dichotomous variable whose values are 0 (reflecting absence of any quantity of the variable) and 1 (reflecting presence of the variable). Bivariate Analysis: a statistical analysis of the relationship between two variables. Causal Relationship: a relationship between two variables for which we can say that the presence or absence of one variable determines the presence or absence of the other or that values of one variable result in specific values of the other variable. Conceptualization: the first step in the measurement process, in which the researcher selects the variables to be measured; delineating the exact meaning of the independent and dependent variables. Confounding Variable: variables operating in a specific situation in such a way that their effects cannot be separated; they occur when the effects of an extraneous variable cannot be separated from the effects of the dependent variable; the effects of the extraneous variable thus confound the interpretation of research results. Constant: a characteristic that has the same value for all individuals in a research study Continuous Variable: variable that may theoretically assume any value between two points on the measurement scale; it can thus have an infinite number of possible values between those points. Correlated Variables: variables whose values are associated in a systematic way with values in the others. Correlation Analysis: statistical methods that allow us to discover, describe, and measure the strength and direction of associations between and among variables; include the various techniques of computing correlation coefficients and regression analyses. Criterion Variable: the variable whose values are predicted from measurements of the predictor variable; another term for outcome variable. Data: the numbers or scores generated by a research study; the word data is plural. Dependent Variable: the variable that we do not directly introduce or manipulate; after the different levels of the independent variable have been administered, all research participants are measured, in the same way, on the same dependent variable; a variable in which the changes are results of the level or amount of the independent variable(s); also, the variable whose variations are of most interest to the researcher; when used with correlation or regression, it is referred to as the outcome variable. Descriptive (Data) Analysis: methods used for summarizing and describing data in a clear and precise manner; strictly speaking, descriptive analyses apply only to the people (or objects) actually observed; methods for data reduction. Dichotomous Variable: a variable that can take on only one of two values. Discrete Variable: a variable that can assume only a finite number of values. Dummy Variable: a variable that is created by converting a qualitative variable into binary variables. Extraneous Variable: (see intervening variables) – a variable whose existence is inferred, bat that cannot be manipulated; a variable that may affect just what influence (if any) an independent variable has on a dependent variable; also referred to as a confounding variable; when controlled for in a research design, it is known as a control variable. In its most specific usage, a variable that may have come between (in time) the introduction of the independent variable and the dependent variable and may thus have affected the latter. Frequency: number of observations falling in a cell or value category of a specific variable. Independent Variable: the variable we believe to be associated with the different values of the dependent variable; the variable that is manipulated or introduced in a research study in order to see what effect differences in it will have on those variables proposed as being dependent on it. Inferential Analyses: statistical methods that make it possible to draw tentative conclusions about the population based on observations of a sample selected from that population and, furthermore, to make a probability statement about those conclusions to aid in their evaluation. Interval: (measurement) a measurement that, in addition to ordering scores, also establishes an equal unit so that distances between any two scores are of a known magnitude; a measurement in which objects, events, or processes are assigned to ordered categories that are separated by equal intervals; any measuring device that is capable not only of placing people (or objects) in their rank order on a characteristic but can also measure the differences between them in regard to that characteristic. Multivariate Analysis: a statistical analysis of the simultaneous relationship among three or more variables. Nominal: (measurement) a measurement that simply classifies elements into two or more mutually exclusive categories, indicating that elements are qualitatively different but not giving order or magnitude; a measurement in which objects, events, or processes are assigned to categories having no inherent order; the level of measurement whose only requirement is that each observation falls in one, and only one, measurement category; also referred to as categorical measurement; it is the lowest level of measurement. Null (Research) Hypothesis: a statement concerning one or more parameter(s) that is subjected to a statistical test; a statement that there is no relationship between the two variables of interest; the belief that any apparent relationship between or among variables in one or more research samples has been caused by sampling error; the hypothesis that is tested when seeking to gain statistical support for a one-tailed or two-tailed research hypothesis. One-tailed Research Hypothesis: a form of research hypothesis in which the researcher predicts that a statistically significant relationship between variables will be found and also predicts the direction of that relationship. Ordinal: (measurement) a measurement that classifies and ranks elements or scores; a procedure that is capable of rank ordering individuals (or objects) on a particular characteristic but that cannot distinguish how different each is from the others; a measurement in which objects, events, or processes are assigned to ordered categories; the level of measurement above nominal but below interval; the data represent at least ordinal scale measurement if each observation falls into one, and only one, category and if observation categories can be rank ordered. Outcome Variable: the variable whose values can be predicted by values of the predicator value; sometimes called the criterion variable. Parameters: a characteristic of a population determined from observations on every member of the population; population parameters of interest to us include the mean, range, median, standard deviation, and many others; also a characteristic of a mathematical relation whose value must be specified before the expression can be evaluated; a measure computed from all observations in a population. Population (Distribution): a distribution of all the scores in a population; a collection of all observations identifiable by a set of rules; a designated part of a universe from which a sample is drawn; the complete group of potential observations. Predicator Variable: the variable that, it is believed, allows us to improve our ability to predict values of the outcome variable. Ratio: (measurement) a measurement that, in addition to containing equal units, also establishes an absolute zero point within the scale; a measurement in which objects, events, or processes are assigned to ordered categories that are separated with equal intervals, and where the zero point is not arbitrary; the highest level of measurement; it is reached only when observation falls in one, and only one, category; when observation categories can be ordered; when there are equal intervals between adjacent categories on the measurement scale; and when a value of zero represents a zero quantity of the variable being measured. Reliability: the consistency of a measurement instrument. Research Hypothesis: a prediction of the relationship between two or more variables; when using one-tailed or two-tailed research hypotheses, the hypothesis to be supported if the null hypothesis rejected; also called the alternative hypothesis. Two-tailed Research Hypothesis: a form of research hypothesis in which the researcher predicts that a statistically significant relationship between variables will be found but does not predict the direction of that relationship. Univariate Analyses: statistical analysis of the distribution of values of a single variable. Validity: the degree to which a measurement instrument accurately measures what it is supposed to measure. Variable: a characteristic that takes on different values; any attribute whose value, or level, can change; any characteristic (or a person, object, or situation) that can change value or kind from observation to observation. ­ Chapter 2 – Frequency Distributions and Graphs ­  Absolute Frequency Distribution: a table that displays the frequencies for various measurements of a variable. Bar Graph: a graphical technique of descriptive statistics that uses the heights of separated bars to show how often each score occurs; graphical representation of a frequency distribution table in which each measurement category is represented by a bar that extends to the appropriate distance in the frequency dimension; usually has spaces between bars to represent nominal level data. Cumulative Frequency Distribution: a frequency distribution that gives the number of scores that occur at or below each value of a variable. Cumulative Percentage (Frequency) Distribution: a table that shows what percentage of scores occurs at or below each value of a variable. Frequency Polygon: a graphic technique of descriptive statistics that uses the height of connects dots to display the shape of the distribution in which the horizontal axis represents different values of a variable and the vertical axis represents constructing a frequency polygon, a dot is placed over each value of the variable at a height corresponding to the appropriate frequency; the dots are then connects with lines to form a polygon. Grouped Cumulative (Percentage) Distribution: an extension of a grouped frequency distribution that shows how often scores occur at or below each interval. Grouped Frequency Distribution: table or graph in which frequencies are not listed for each possible value of the variable; rather, a frequency is listed for each of a number of intervals on the measurement scale; each interval is a range of values; all observations falling within the limits of the interval add to the frequency count for that interval; grouped frequency distributions are used most often when data represent observations on a continuous variable. Histogram: a graphic representation of a frequency distribution in which the horizontal line represents values of a variable and the vertical line represents frequencies with which those values occur; a bar is constructed over each value of the variable (or the midpoint of each interval, if that data are grouped) and extended to the appropriate frequency; the term histogram usually refers to such a graph for interval or ratio data, whereas the term bar graph usually refers to such a graph for nominal or ordinal data; a graphic technique of descriptive statistics that uses the height of adjoining bars to show how often each score occurs. Ordinate: (ordinal measurement) a measurement that classifies and ranks elements or scores; a procedures that is capable of rank ordering individuals (r objects) on a particular characteristic but that cannot distinguish how different each is from the others; a measurement in which objects, events, or processes are assigned to ordered categories; the level of measurement if each observation falls into one, and only one category and if observation categories can be rank ordered. Percentage (Frequency) Distribution: a table that displays percentages of cases that were found to have each of the respective measurements of a variable. Percentile: a point on the measurement scale below which a specified th percentage of the group’s observations fall; the 20 percentiles, for instance, is the value that has 20 percent o the observations below it. Percentile Rank: a transformed score that tells us the percentage of scores falling at or below a given score. Pie Chart: (pie graph) a graph that displays the frequency distribution of a variable as portions of a circle reflecting percentages of the whole. Frequency Distribution: (Simple) a table or graph that presents the number of times (frequency) with which different values of the variable occur in a group of observations; a technique of descriptive statistics that shows how often each score occurs. Stem-and-Leaf Plot: a graph consisting of numbers that reflect the actual case values of all cases in a frequency distribution. X-variable: the variable plotted on the x-axis of a scattergram and predictor variable (used to predict the y variable) in regression; usually the independent variable in a research study. Y-variable: the variable plotted on the x-axis of a scattergram and predicted variable (predicted from the x variable) in regression; usually the dependent variable in a research study. ­ Chapter 3 – Measures of Central Tendency and Variability ­ Bimodal: a frequency distribution with two modes reflecting equal or nearly equal frequencies. Box Plot: a graph that reflects both the central tendency and variability of the distribution of a variable. In one of its most common variations, lines are used to indicate the five-number summary that is, the minimum value, th
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