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Measuring And Evaluating Teaching

Subject : teaching
Instructions:
  • Answer 50 questions in 15 minutes.
  • If you are not ready to take this test, you can study here.
  • Match each statement with the correct term.
  • Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.

This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. Means probably true (not by chance) in statistics.






2. Variable that make it possible to rank order items measured in terms of which has less and which has more of the quality represented by the variable.






3. A model for measuring effectiveness through four perspectives: the customer perspective - the innovation and learning perspective - the internal business perspective - and the financial perspective.






4. The multiple dependent variables in a study with multiple independent variables.






5. Make it possible to rank order the items measured and quantify and compare the sizes of differences between them.






6. Is a particular way in which observation tend to pile up around a particular value rather than be spread evenly across a range of values.






7. A variable that falls into one of two possible classifications (for example - number of children - number of defects).






8. The treatment group; those participants who receive the 'treatment.'






9. The extent to which the assessment can predict or agree with external constructs. Criterion validity is determined by looking at the correlation between the instrument and the criterion measure.






10. A measure of the relationship between two or more variables; if one changes - the other is likely to make a corresponding change. If such a change moves the variables in opposite directions - it is a negative correlation.






11. A data point that's far removed in value from others in the data set.






12. The term researchers and statisticians use to define the 'manipulated' variable in an experiment. An 'experiment group' receives a treatment (for example - attends a training program) - and a control group does not.






13. A variable whose quantification can be broken down into extremely small units (for example - time - speed - distance).






14. The process of drawing the sample of people for a study from the population.






15. A commonly used measure or indicator of the amount of variability of scores from the mean. The standard deviation is often used in formulas for advanced or inferential statistics.






16. Measures the success of the learner's ability to transfer and implement the learning back on the job.






17. Frequently thought of as the 'outcome.' Or treatment variable. The dependent variable's outcome depends on the independent variable and covariates.






18. Qualitative measures are more intangible - anecdotal - personal - and subjective - as in opinions - attitudes - assumptions - feelings - values - and desires. Qualitative data can't be objectified - and that characteristic makes this type of data val






19. Is information that can be difficult to express in measures or numbers.






20. Another name for a solution or set of solutions - usually a combination of (outliners) - of the three types of central tendency because each number in the data set has an impact on its (mean) value.






21. A nickname for the instructor and class training evaluation forms used in Level 1 evaluation.






22. Involves looking at participant's opinions - behaviors - and attributes and is often descriptive.






23. Evaluators to make inferences about data from the sample to a compare the sixes of differences between them.






24. Objective and measurable quantitative measures - whether stated in terms of frequency - percentage - proportion - or time.






25. An assessment done when while its being formed.






26. The process of organizing an experiment properly to ensure that the right type of data - and enough of it - is available to answer questions of interest as clearly and efficiently as possible.






27. Involves measuring what the practitioner intended to measure.






28. The ability to achieve consistent results from a measurement over time.






29. Show the actual number of observations falling in each range or percentage of observations.






30. Asymmetry in the distribution of sample data values.






31. An unknown or uncontrolled variable that produces an effect in experimental setting. A confounding variable is an independent variable that the evaluator didn't somehow recognize or control. It becomes a variable that confounds the experiment.






32. Undesirable variables that influence the relationship between variables an evaluator is examining.






33. Assess the impact of a training program on learning.






34. The range where something is expected to be.






35. Archival or existing records - reports - and data that may be available inside or outside an organization. Examples include - job descriptions - competency models - benchmarking reports - annual reports - financial statements - strategic plans - miss






36. Numbers or variables used to classify a system - as in digits in a telephone number or numbers on a football player's jersey.






37. A type of test reliability in which one test is split into two shorter ones.






38. The error of distorting a statistical analysis be pre-or post selecting the samples.






39. Each person in the population has an equal chance of being chosen for the sample. Choosing every tenth person from an alphabetical list of names - for example - creates a random sample.






40. The best-fitting straight line through all value pairs of correlation coefficients.






41. A method that helps diffuses the covariates across the experimental and control groups. Researchers in organizations often have multiple dependent variable with one independent variable (for example - performance






42. A measure of how spread out a distribution is. It's calculated as the average squared deviation of each number from the mean of a data set






43. The variable that influences the dependent variable. Age - seniority - gender - shift - level of education - and so on may all be factors (independent variables) that influence a person's performance (the dependent variable).






44. The process of assigning the sample that's drawn to different groups or treatments in the study.






45. The most robust - or least affected by the presence of extreme values (outliers) - of the three types of central tendency because each number in the data set has an impact on its (mean) value.






46. A way of quantifying the difference - using standard deviation - between two groups. For example - if one group (the treatment group) has had an experimental treatment and the other (the control group) has not - the effect size is a measure of the ef






47. A group of participants in an experiment that's equal in all ways to the experimental group - except the control group doesn't receive the experimental treatment.






48. A variable in which the units are in the whole numbers - or 'discrete' units (for example - number of children - number of defects).






49. Numbers or variables that make it possible to rank order items measured in terms of which has less and which has more of the quality represented by the variable.






50. Dividing the population into constituent parts - and then choosing sample members randomly choosing people from each age group creates a stratified random sample.