NCE. Research Methods and Program Evaluation Questions and Answers 2024

NCE. Research Methods and Program Evaluation Questions and Answers 2024 experimental research the process of gathering data in order to make evaluative comparisons regarding different situations quasi-experiment the researcher uses pre-existing groups, so the independent variable cannot be controlled or when the groups are not picked at random; cannot assign causality Previous Play Next Rewind 10 seconds Move forward 10 seconds Unmute 0:13 / 0:15 Full screen Brainpower Read More ex post facto research quasi-experiment; the IV was administered before the research began; researcher did not have control over the independent variable because the research occurred in the past threats to internal validity maturation, mortality, instruments, statistical regression maturation psychological and physical changes including fatigue due to the time involved attrition/experimental mortality subjects withdrawing from a study statistical regression to the mean the notion that extremely high and low scores woul dmove toward the mean if the measure is used again internal validity whether the DVs were truly influenced by the experimental IVs or whether other factors had an impact external validity whether the experimental research results can be generalized to larger population (other people, settings, or conditions) factor analysis statistical procedures that use the important or underlying factors in an attempt to summarize a lot of variables (e.g., 3 most important variables/factors that make an effective helper) parsimony interpreting the results in the simplest way; aka Occam's Razor confound undesired variable which is not controlled by the researcher is introduced in the experiment; all correlational research is said to be confounded APA Journal of Counseling publishes more counseling research articles than any other periodical in the counseling field variable a behavior or circumstance that can exist on at least 2 levels or conditions; a factors that varies or is capable of change independent variable the variable that the researcher manipulates dependent variable expresses the outcome or the data discrete variable e.g., brand of counseling continuous variable e.g. height or weight causal comparative design a true experiment except that groups were not randomly assigned; can be analyzed with a test of significance (e.g., t test or ANOVA) research ethics 1) subjects are informed of any risks; 2) negative after effects are removed; 3) confidentiality of subjects must be maintained; 4) research report results will be presented in an accurate format that is not misleading; 5) researcher will only use techniques that s/he is trained in; 6) subjects are allowed to withdraw at any time; (INCR-TW) control group does not receive the independent variable experimental group receives the independent variable minimum number of research participants true experiment: at least 30 people; correlational research: 30 subjects per variable; surveys: at least 100 people organismic variable one the researcher cannot control yet exists, such as height, weight or gender R.A. Fisher pioneered hypothesis testing hypothesis a statement which can be tested regarding the relationship of the IV & DV; a hunch or educated guess which can be tested using the experimental model null hypothesis asserts that the samples will not change (i.e. they will still be the same) even after the experimental variable is applied; the control group & the experimental group will not differ at the end of the experiment; the IV does not affect the DV meta-analysis a study that analyses the findings of numerous studies in order to examine a hypothesis; originally used to overcome severe limitations of a small sample size in one experiment; drawback is that a meta-analysis may be relying on poorly designed studies which will yield inaccurate results; Karl Pearson unleashed this technique and is also associated with the concept of correlation; he created the first statistics department in a university in 1911 experimental hypothesis aka alternative hypothesis/affirmative hypothesis; denoted as H sub"zero" or H sub"A"; a difference will be evident between the control group & the experimental group descriptive statistics merely describes data; central tendency like the mean, mode, median, range, quartiles, variance, and standard deviation inferential statistics needed to compare 2 groups; infers something about the population percentile rank a descriptive statistic telling what percentage of cases fell at or below a certain level; distributions will be rectangular & flat tests of significance used to determine whether a difference in the groups' scores is significant or just due to chance factors t test determines if a significant difference between two means exist; t value statistic has to be higher than the t value in the table to be significant independent group comparison design two groups in a study were independent of each other (change or lack of change in one group did not effect the other group) between-subjects design different subjects used for each condition; each subject receives only one value of the IV (e.g., independent group comparison design) within-subjects design subjects are in both the control group and the experimental group; counterbalancing is used; two or more values/levels of the IV are administered to each subject (e.g., repeated measures comparison design) repeated measures comparison design measures the same group of subjects with the IV and without the IV; is a within-subjects design parameter a value obtained from a population; summarizes a characteristic of a population statistic drawn from a sample, not the whole population (P) probability/level of significance aka level of confidence (e.g., 95% confidence interval); set at .05 or lower (.01, .001); .05 means that differences would occur via chance only 5 times in 100; differences truly exist; the experimenter will obtain the same result 95 out of 100 times; significance level must be set before the experiment begins; sometimes connoted as alpha level Type I error/alpha error researcher rejects the null hypothesis when it is true; probability of committing = level of significance; lowering level of significance lowers Type I errors, but raises Type II errors