Glossary | |
| These is glossary of research key terms. | |
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| Term | Definition |
| Inductive reasoning | 1. A logical process of reasoning used to develop more general rules from specific observations; this type of reasoning moves from the specific to the more generalised. 2. a logical process of developing generalizations "theories" based on specific observations e.g. one sees ducks flying south together for the winter and generalizes "Birds of a feather flock together" or for criminal behavior "Juvenile delinquents associate with other juvenile delinquents" [Note: this example is used to encourage you to think about possible errors such as over generalization, inaccurate observation, etc. in this type of reasoning]. |
| Inferential statistics | 1. Statistics that allow a researcher to make inferences about whether relationships observed in a sample are likely to occur in the wider population from which that sample was drawn. Inferential statistics use logic and mathematical processes in order to test hypotheses relating to a specific population based on data gathered from a sample of the population of interest. 2. techniques designed to specify estimates and confidence in estimates, of a population based on data collected from a sample. |
| Informed consent | The process of obtaining voluntary participation of individuals in research based on a full understanding of the possible benefits and risks. |
| Interactionism | Meaning is created by interactions between people. eg. the Looking-glass self. |
| Internal validity | Refers to the extent to which changes in the dependent variable (the observed effects) can be attributed to the independent variable rather than to extraneous variables. |
| Interpretation | 1. A term used in the analysis of the relationship between three variables, the elaboration model, to refer to the situation where a control variable is found to be the mediating factor in a bivariate relationship. 2. outcome of elaboration in which the control variable shows how one variable is related to another. |
| Interrater reliability (interobserver re | A measure of the consistency between the ratings/values assigned to an attribute that is being rated or observed; it is usually expressed as a percentage of agreement between two raters/observers or as a coefficient of agreement which may then be expressed as a probability. Usually employed by researchers using structured observation techniques. |
| Intersubjectivity | By sharing time and space, two actors communicate in a process of understanding. |
| Intertextuality | Meaning is constantly being produced by the relationship between texts. |
| Interval level of measurement | 1. A level of measurement in which the ratios of measured values are not meaningful, which is to say that they do not correspond to similar relationships among the objects measured. The classic example of interval measurement is the measurement of temperature using Fahrenheit and Centigrade scales. If it is 80ºF in Tulsa and 40ºF in Juneau, you cannot say it is twice as hot in Tulsa. Here is one clue that this is not meaningful. Suppose we measured the temperature in Centigrade instead of Fahrenheit. The formula for converting Fahrenheit to Centigrade is C=(F-32)*5/9. So in Tulsa it is 27 C and in Juneau it is 4 C. Now it looks like Tulsa is 4 times as hot as Juneau! Yet Fahrenheit and Centigrade are perfectly equivalent and equally valid measuring scales. So you know ratios are not meaningful in interval measurement. 2. The ratios of differences (intervals) among measured values is meaningful. For example, suppose it is 70 F in L.A. and 50 F in San Francisco. The difference in temperature between Tulsa and Juneau (40 F) is twice as much as the difference in temperature between L.A. and San Francisco (20 F). This statement is still true if we measure the temperatures in Centigrade, so interval measurements preserve ratios of differences in measured values. |
| Interval measure | The level of measurement that is characterized as being mathematically isomorphic with arithmetic. All math functions for nominal and ordinal measures plus an arbitrary zero point, and equal distance between units of measurement. Statistically appropriate techniques include arithmetic mean, standard deviation, r2 Z,T,F . Examples include temperature and time measurement. |
| Interval scale | The categories are ordered and there are equal intervals between points on the scale, but the zero point on the scale is arbitrary so that a particular measure cannot be said to be 'twice as' large as another measure on the same scale (e.g. degrees Centigrade). |
| Intervening Variable | 1. An intervening or intermediary variable is one that is affected by the independent variable and in turn affects the dependent variable. 2. a third variable that is causally prior to the dependent variable, but not to the independent variable. |
| Interview | 1. A method of data collection involving an interviewer asking questions of another person (a respondent) either face-to-face or over the telephone. 2. a research method in which the researcher (interviewer) asks questions to another (respondent). Interviews are person to person and are conducted either face to face or telephone. |
| Judgment Sampling | A non-probability sampling scheme in which you make use of special expertise to select elements for the study. Typically, this is used to obtain a balance of viewpoints or to select knowledgable respondents. |
| Glossary V2.0 | |
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