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Evidence #6

test 2

quantitative research designs: experimental vs non-experimental experimental is actually changing/manipulating something. with non, nothing is manipulates, you are just looking to see what the variables are (strictly descriptive)
non-experimental: types descriptive, correlation, case-control, cohort studies.
Descriptive portray a phenomenon as accurately as possible via statistics instead of language.usually involves a tool/survey/something with numbers
correlation describe possible interrelationships among variables.
case-control usually retrospective (the care has already been done). compare one group with situation to another without.
Cohort studies follow group of subject longitudinally over time to describe or predict.
quasi-experimental vs experimental answer questions involving prediction and effects of manipulation. difference is the amount of control.
Experimental ALWAYS includes an intervention group, control group, and random assignment to groups. (has to have all 3 of these)
Quasi-experimental lacks a control group and/or random assignment. still manipulates independent variable. can have two groups, but not randomly assigned.
quantitative research design types pretest-postest; quasi-experimental repeated measures design; survey studies; comparative studies (retrospective vs prospective); methodological studies; secondary analysis.
experimental pretest-posttest design measure what we know (r), teach (O1), another test (post-test - x), the clients after (O2). looking to see if the information makes a difference.
quasi-experimental repeated measures design how long do participants remmeber information that I teach them? O1, O2, O3 (measure, measure, measure), X (re-teach), O4, O5, O6 (measure, measure, measure).
comparative studies retrospective vs perspective. what is my rate before an intervention, apply the intervention, then see how the rate changes.
methodological studies measurement design. want to institute a new measurement of something. still looking at the same type of phenomenon, just proposing a different way of measuring it.
secondary analysis used especially with national data. utilizing information that has been gather, then reanalyze the data based on a particle finding.
internal validity controlling for things that make the sample different. downfall: can't generalize. strong: you have the understand that the data is very consistent for THAT population. means you have a homogenous sample.
external validity looking to control for MAJOR variables. looking to increase the validity, b/c you are moving to more of a heterogenous group. (larger sample to washout bias). strong: b/c sample is really large.
hypothesis: purpose gives us an understanding for what the researchers think the variables they are measuring are going to be able to tell us.
simple hypothesis includes 2 variables only. most commonly give you the null hypothesis.
complex hypothesis looking at > 2 variables
null hypothesis proposes no difference between the groups. A = B. allows us to look at both ends statistically
research hypothesis proposes a relationship between 2 or more variables.
directional hypothesis tells us that group A is greater than group B. moves all statistics to one end or the other (based on where we are going with our data). we are sure there is a particular direction this will go.
non-directional hypothesis still looking at both ends. uses terms like "great", "less", directional because it predicts that there will be a difference between the two groups and it specifies how the two groups will differ
purpose of hypothesis testing rejection of the null hypothesis.
hypothesis testing based on rules of negative inference (null hypothesis) - there is no difference in the two group
data collection what type of data. how was the data collected. timeframe of data collection.
types of data collection physiological/biological measures. observational measures. interviews. questionnaires. records/available data.
physiological/biological measures use of specialized equipment to determine the physical and biological status of subjects. tend to be objective, precise, and sensitive.
observational measures standardized and systematic plan for the observation and recording of data. must determine what is to be observed and how it will be recorded and coded.
interviews data collector questions subjects verbally. open- or close-ended questions.
questionnaires paper/pencil instruments designed to gather data from individuals about knowledge, beliefs, and feelings.
rating scales list of ordered series of variables. may or may not have underlying continuum.
Likert scale measures opinions or attitudes about a concept. a number associated with a level of agreement, frequency, or evaluation.
Visual analog scale line 100mm long with verbal anchors at either end to depict opposite feelings. person marks on the line where they are. measure with a rules to "quantify" the data.
number rating scales person marks a number where they are. may use verb anchors. most commonly used in nursing (ie. pain scale).
reliability & validity ensure the results of the study are "accurate".
reliability: quantitative consistency of the measures. different types of reliability (interrater reliability - equivalence; test-restest reliability - stability; internal consistency - homogeneity).
Cronbach's Alpha have established itnernal consistency. .7-.9 = very reliable. <.7 = inconsistencies in the way they observed/tested.
test-retest .6-.8 = the test is pretty strong.
validity: quantiative measure is accurate. comprised of several degrees of several dimensions. types: Criterion-related; construct; content.
Criterion-related validity linked it to research or some type of data that supports this being part of the question
construct validity have an expert in the filed read it and make sure it is clear. make sure it is at a level that all people can understand.
content validity has everything been covered? did we miss anything? everything that is related to the topic is included in the questionnaire.
qualitative reliability & validity - rigor credibility, auditability, fittingness, reflexivity.
credibility would participants recognize the experience as there own?
auditability are measures taken that would allow another person to follow the researcher's thinking.
fittingness are the finding applicable in other situations?
reflexivity does the author discuss their effect on research process or findings?
correlation statistics statistical test to examine how must 2 variables are connected to consistent changes in one another. (r). range from -1 to +1.
positive correlation if one values goes up, the other goes up as well, OR, if one value goes down, the other does as well.
negative correlation if one value goes up, the other goes down, or vice versa.
correlation statistics: < .20 slight, almost negligible
correlation statistics: .20 - .40 weak correlation; definite by small relationship
correlation statistics: .40 - .60 moderate correlation; substantial relationship
correlation statistics: .60 - .80 strong correlation, marked relationship
correlation statistics: > .80 very strong correlation; very dependable relationship
Created by: malysab14