ASSIGNMENT 5 XXXXXXXXXX Assignment 5: Analysis of Variance CES 714 Advanced Statistics Assignment 5 – 20 points Analysis of Variance Please, conduct Two-Way ANOVA with provided file for Assignment 5....

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ASSIGNMENT 5 1 Assignment 5: Analysis of Variance CES 714 Advanced Statistics Assignment 5 – 20 points Analysis of Variance Please, conduct Two-Way ANOVA with provided file for Assignment 5. Please, submit your SPSS and Word files when you submit your assignment. DEFINITION OF ANOVA: 2-Way Factor Analysis DV = DEPRESSIONsubDASS from DASS Scale4 DEPR ESS is made up of the following individual questions in the DASS scale questionnaire: , 5, 10, 13, 16, 17, 21C SIV1 (or Factor 1) = Sex_Gender (level = Male, Female, Trans) IV2 (or Factor 2) = PWI_health_cat (level = low, medium, high) 1. State the purpose of your study that would be linked to your Two-Way ANOVA research question. Read class notes on how to state the purpose statement (2 points). ReseaRQ1 = Does levels of depression differ? (Only comparing mean scores across Gender groups) Ho: There is no significant difference in mean DEPRESSIONsubDASS score between Genders Ha: There is a significant difference in mean DEPRESSIONsubDASS score between Genders by Gender (male, female, trans)? RQ2 = (Only comparing mean scores across Health groups) Ho: There is no significant difference in mean DEPRESSIONsubDASS score between Health groups Ha: There is a significant difference in mean DEPRESSIONsubDASS score between Health groups Does levels of depression differ by Health? RQ3 = (Determine if mean scores across gender groups differ by health groups) Ho: There is no significant interaction in means DEPRESSIONsubDASS score between Gender and Health groups Ha: There is a significant interaction in means DEPRESSIONsubDASS score between Gender and Health groups 2. Describe your sample in a sentence format (write at least3 sentences), include demographics table for this assignment – see example provided in the assignment 5 folder (1 point). 3. State research question and two hypotheses (null and directional) for Two-Way ANOVA. Be careful to use a Two-Way ANOVA research language (3 points). RQ1 = Does depression differ by Gender? (Only comparing mean scores accross Gender groups) Ho: There is no significant difference in mean DEPRESSIONsubDASS score between Genders Ha: There is a significant difference in mean DEPRESSIONsubDASS score between Genders RQ2 = Does depression differ by Health (Only comparing mean scores across Health groups) Ho: There is no significant difference in mean DEPRESSIONsubDASS score between Health groups Ha: There is a significant difference in mean DEPRESSIONsubDASS score between Health groups RQ3 = Is there an interaction between Gender and Health (Determine if mean scores across gender groups differ by health groups) Ho: There is no significant interaction in means DEPRESSIONsubDASS score between Gender and Health groups Ha: There is a significant interaction in means DEPRESSIONsubDASS score between Gender and Health groups 4. State how your data meets the assumptions for Two-Way ANOVA. Use graphs, descriptive statistics, and additional tests (e.g., test for normality) to support your conclusions. Please, read the example of assumptions write up for that provided in assignment 5 folder (2 points). ** Tried to do log transformations for Sex/Gender variable to no avail. There is a violation of normality for levene’s test a. Interval level DV b. Normally distributed DV in each grouping · The distribution is not normal according to shapiro-wilkes, and kolmogrov smirnov · Tried to transform with log (n+1) and ln(n+1) and failed c. Equal variance in groupings (Levene’s test) d. * meet the equal variance assumption since Levene’s test is not significant 3. Equal Variance in groupings (Levene's test)n assignment 5 folder (2 points) 5. Run Two-Way ANOVA statistics and past* Paste tables in here · The main factors are both significant (p <.05) a.="" gender="" (f(1,189)="," p=".022)" b.="" pwi="" healthcat="" (f(2,189)="9/198," p="">< .001="" ·="" interaction="" of="" the="" factors="" (pwi="" healthcat="" x="" gender)="" is="" not="" significant="" (p=""> .05) · (F(2,189) = 1.398, p = .250) 6. Run a post-hoc analysis and explain what post-hoc test you chose to run and why, paste your output (2 points). · Since PWI HealthCat is significant, we ran a posthoc. We did not run a posthoc for Gender since there are only 2 groupings and there is no need. PWI HealthCat: Low - High: p < .001="" medium="" -="" high:="" p=".001" low="" -="" medium:="" not="" significant="" 7.="" report="" your="" findings="" in="" apa,="" write="" them="" up="" in="" a="" paragraph="" format="" (3="" points).="" 8.="" state="" practical="" significance="" of="" your="" test="" by="" reporting="" eta="" squared="" and="" what="" it="" means="" (2="" points)="" gender:="" η2=".028" (small="" because="" between="" .01="" and="" .06)="" pwi="" health="" cat:="" η2=".089" (medium="" because="" between="" .06="" and="" .14)="" cohen="" (1988)="" has="" provided="" benchmarks="" to="" define="" small="" (η2="0.01)," medium="" (η2="0.06)," and="" l="" effect="" size="" interpretation="" for="" η2="" large="" (η2="0.14)" effects.="" small="" (η2="" -="" .01="" to="" .06)="" medium="" (η2="0.06" to="" .14)="" large="" (η2="0.14" or="" greater)="" 9.="" state="" whether="" or="" not="" you="" retained="" your="" null="" hypothesis="" or="" rejected="" it="" (1="" point).="" main="" factors="" and="" interactions:="" ·="" for="" gender="" we="" rejected="" the="" ho:="" (f(1,189="," p=".022)" ·="" for="" pwi="" health="" category="" we="" rejected="" the="" ho;="" (f(2,189)="9.198," p="">< .001)="" ·="" for="" the="" interaction="" we="" accepted="" the="" ho="" :="" posthoc:="" ·="" for="" the="" pairs="" low="" -="" high="" (p="">< .001)="" and="" medium="" -="" high="" (p=".001)" we="" reject="" the="" ho:="" ·="" *="" for="" the="" pair="" low="" -="" high="" we="" accepted="" the="" ho:="" 10.="" compute="" the="" power="" of="" your="" test,="" you="" may="" use="" this="" calculator="" http://www.datavis.ca/online/power/="" or="" find="" another="" one="" and="" explain="" what="" it="" means,="" see="" the="" example="" of="" power="" reporting="" in="" the="" assumptions="" and="" power="" write="" up="" file="" in="" the="" assignment="" 5="" folder="" (2="" points).="" **="" did="" not="" know="" how="" to="" do.="" outputviewer0000000000.xml="" output="" log="">
COMPUTE DepressionSubDASS=(DASS_3 + DASS_5 + DASS_10 + DASS_13 + DASS_16 + DASS_17 + DASS_21) * 2. EXECUTE. DATASET ACTIVATE DataSet1. SAVE OUTFILE='/Users/susanasolomon/Desktop/Calibate Gay Christians Data Set for class.200 '+     'cases(2).sav'   /COMPRESSED. EXAMINE VARIABLES=DepressionSubDASS BY Sex_Gender   /PLOT BOXPLOT HISTOGRAM NPPLOT   /COMPARE GROUPS   /STATISTICS DESCRIPTIVES   /CINTERVAL 95   /MISSING LISTWISE   /NOTOTAL. 00000000011_lightNotesData.bin 000000000131_lightTableData.bin 000000000132_lightTableData.bin 000000000133_lightTableData.bin 00000000013411__chartData.bin 00000000013411__chart.xml DepressionSubDASS Frequency Histogram for Sex_Gender= Female Mean = Std. Dev. = N = 00000000013412__chartData.bin 00000000013412__chart.xml DepressionSubDASS Frequency Histogram for Sex_Gender= Male Mean = Std. Dev. = N = 00000000013413__chartData.bin 00000000013413__chart.xml
Answered Same DayDec 29, 2021

Answer To: ASSIGNMENT 5 XXXXXXXXXX Assignment 5: Analysis of Variance CES 714 Advanced Statistics Assignment 5...

Shweta answered on Jan 13 2021
135 Votes
1. Analysis of variance is a statistical test used to analyze the difference between the means
of more than two groups. A two-way ANOVA is used to estimate how the mean of a
quantitative variable change according to the levels of two categorical variables. We use
ANO
VA when we want to know how two independent variables, in combination, affect a
dependent variable.
We have given the data of gay Christians celibate. We will use this data to learn about
depression level of gay based upon their gender and health. We are using two-way
ANOVA to understand whether there is an interaction between gender and health level
on the depression among gay celibate. Here “gender” with two levels – male/female
and “health” with three levels - low/medium/high are independent variables, and
“depression” is the dependent variable.
2. The sample which we are using is of gay Christians who choose Celibacy. Celibacy is a
vow to remain abstinent over an extended period of time. Data contain 200 number of cases
or samples with 210 variables in total. Data contain details about their age, gender, marital
status, race, education level, employment, income, health, mental health, religion, sexual
attraction and many more.
The table below shows the descriptive statistics of some variables.
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Variance
Celibate from all, celibate
from sex, mixed orientation
marriage
200 1 3 1.93 .695 .483
Celibacy_Years_Months_
Together
100 .50 53.00 18.3033 12.93084 167.207
Age 200 16 68 33.15 12.229 149.555
Sex/Gender: 198 1 2 1.82 .387 .150
Race/Ethnicity: 200 1 7 1.42 1.136 1.290
Marital Status: 200 1 4 1.47 1.084 1.175
What percentage of the time
do you feel satisfied with the
following areas: Your health?
200 1 3 2.60 .643 .413
DepressionSubDASS 197 .00 42.00 13.3198 11.52479 132.821
Valid N (listwise) 99
3. RQ1 = Does level of depression differ by Gender?
Ho: There is no significant difference in the mean DEPRESSIONsubDASS score
between Genders
Ha: There is a significant difference in the mean DEPRESSIONsubDASS score
between Genders
RQ2 = Does level of depression differ by Health?
Ho: There is no significant difference in the mean DEPRESSIONsubDASS score
between Health groups
Ha: There is a significant difference in the mean DEPRESSIONsubDASS score
between Health groups
RQ3 = Does the impact of health on depression level depends on gender or, simply, Is
there an interaction between Gender and Health
Ho: There is no significant interaction in the mean DEPRESSIONsubDASS score
between Gender and Health groups
Ha: There is a significant interaction in the mean...
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