Scenario: A guidance counselor wanted to know if anxiety differs based on time (first day of class in relation to the last day of class), and if this differs based on year in school (as a freshman, sophomore, junior, or senior). She collected data on a single group of students across their entire high school career at these eight time points — first and last day of freshman, sophomore, junior, and senior year. Anxiety was measured using a standardized scale ranging from 0 — 50, with higher scores indicating greater anxiety. Question: If p = .014 for the interaction of Time x Academic Year, what should the researcher conclude? Group of answer choices The interaction is not significant; no additional analyses are needed to interpret it. The interaction is significant; no additional analyses are needed to interpret it. The interaction is not significant; run correlated groups t-tests for post hoc analyses. The interaction is significant; for post hoc analyses, split the file based on the factor "Time" and conduct one-way ANOVAs with LSD post hocs on the factor "Academic Year" to further interpret. The interaction is significant; for post hoc analyses, split the file based on the factor "Academic Year" and conduct correlated groups t — tests to further interpret.
Scenario: A guidance counselor wanted to know if anxiety differs based on time (first day of class in relation to the last day of class), and if this differs based on year in school (as a freshman, sophomore, junior, or senior). She collected data on a single group of students across their entire high school career at these eight time points — first and last day of freshman, sophomore, junior, and senior year. Anxiety was measured using a standardized scale ranging from 0 — 50, with higher scores indicating greater anxiety. Question: If p = .014 for the interaction of Time x Academic Year, what should the researcher conclude? Group of answer choices The interaction is not significant; no additional analyses are needed to interpret it. The interaction is significant; no additional analyses are needed to interpret it. The interaction is not significant; run correlated groups t-tests for post hoc analyses. The interaction is significant; for post hoc analyses, split the file based on the factor "Time" and conduct one-way ANOVAs with LSD post hocs on the factor "Academic Year" to further interpret. The interaction is significant; for post hoc analyses, split the file based on the factor "Academic Year" and conduct correlated groups t — tests to further interpret.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Scenario: A guidance counselor wanted to know if anxiety differs based on time (first day of class in relation to the last day of class), and if this differs based on year in school (as a freshman, sophomore, junior, or senior). She collected data on a single group of students across their entire high school career at these eight time points — first and last day of freshman, sophomore, junior, and senior year. Anxiety was measured using a standardized scale ranging from 0 — 50, with higher scores indicating greater anxiety. Question: If p = .014 for the interaction of Time x Academic Year, what should the researcher conclude?
Group of answer choices
The interaction is not significant; no additional analyses are needed to interpret it.
The interaction is significant; no additional analyses are needed to interpret it.
The interaction is not significant; run correlated groups t-tests for post hoc analyses.
The interaction is significant; for post hoc analyses, split the file based on the factor "Time" and conduct one-way ANOVAs with LSD post hocs on the factor "Academic Year" to further interpret.
The interaction is significant; for post hoc analyses, split the file based on the factor "Academic Year" and conduct correlated groups t — tests to further interpret.
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