Please answer 5 & 6. Explain as thoroughly as possible! Thanks This assignment w

Question

Please answer 5 & 6. Explain as thoroughly as possible! Thanks

This assignment will involve using SPSS to answer a number of research questions using the data set that is attached. To answer the questions below you will run a one-way ANOVA, a two- way ANOVA, as well as bivariate correlations, and linear regression. The data come from a fictional study of the stress experienced by All But Dissertation (ABD) students in graduate sch ools. Some of the regularly experienced stressors are work overload, writer's block, and with these issues. Data Summary: A sample of subset of 36 participants was selected for this exercise. 1. Days. The number of days between noticing a symptom and seeking assistance 2. Self-Esteem Measured with the Wellness Worksheet, a fifty-eight item inventory, with separation anxiety. Universities usufychological counseling as one way of dealing subscales that read: Like me, and Unlike me. Higher scores mean greater self-esteem 3. Age. Measured in years. 4. Status. Add value labels: 0-Non-immigrant, 1-First generation immigrant, 2- Second generation immigrant 5. Major. Add value labels: 1 = Education, 2-counseling Psychology, 3-clinical Psychology Your tasks 1. Enter the data and add value labels for status and major. Check the data - under Descriptives run crosstabs - status by major and report your results. Check the distribution of the DV "days. The mean for days should 100.58, s-34.439. Is the data skewed? If so, what is the direction of the skew? 2. Then, use one-way ANOVA to test whether Major has any significant effects on the students delay in seeking counseling. Under options include Descriptives and use a Tukey post hoc test to compare groups, if the F-test is significant. Provide a written description of the ANOVA results, including the post-hoc tests 3. Next, do a two-way ANOVA to test whether effects of major on days before seeking counseling differ between immigrant groups. Under options choose display means for major, and make a plot (with major on X, and immigrant groups as separate lines). Again use Tukey st hoc tests when F's are significant. Write a complete summary of the ANOVA results pos which effects are significant and what does the pattern of means indicate? 4. Test whether days before counseling is correlated with age, and self-esteem simple language, what these three correlations mean (the direction of the relatio describe whether the correlations are significant. (What does significant mean here significantly different than what?) Describe, in 5. Run two linear regressions (under stats choose estimates & model fit), first with da dependent variable and age as the predictor. Then run it a second time adding self-esteem score (along with age) as predictors of delay. [The second example is called multiple ys as the

Explanation / Answer

5.

Regression Analysis: Days versus Age (First regression)

The regression equation is
Days = 43.1 + 1.54 Age

Predictor Coef SE Coef T P
Constant 43.14 23.41 1.84 0.074
Age 1.5434 0.6123 2.52 0.017

S = 32.0735 R-Sq = 15.7% R-Sq(adj) = 13.3%

Analysis of Variance

Source DF SS MS F P
Regression 1 6537 6537 6.35 0.017
Residual Error 34 34976 1029
Total 35 41513

The regression equation is (2nd regression)
Days = 42.2 + 1.54 Age + 0.029 Esteem

Predictor Coef SE Coef T P
Constant 42.18 31.55 1.34 0.190
Age 1.5406 0.6242 2.47 0.019
Esteem 0.0291 0.6297 0.05 0.963

S = 32.5548 R-Sq = 15.8% R-Sq(adj) = 10.6%

Analysis of Variance

Source DF SS MS F P
Regression 2 6539 3269 3.08 0.059
Residual Error 33 34974 1060
Total 35 41513

For first regression: R-sq=15.7% and R-sq(adj)=13.3%

For second regression: R-sq=15.8% and R-sq(adj)=10.6%

i.e. 15.7% proprtion of total variation in Days is explained by regression 1 ( i.e. where age is only regressor) and 15.8% proprtion of total variation in Days is explained by regression 2 ( i.e. where age and esteem are two regressors). However adjusted R-sq for regression 2 < adjusted R-sq for regression 1. Hence variable "Esteem" has insignificant effect on "Days". Here we note that R-sq for regression 2 is slight large than R-sq for regression 1 and this is occured due to add a new variable Esteem. For this reason, for multiple regression, we look at adjusted R-sq than R-sq because vlue of adjusted R-sq is only improved if we add significant regressor. Moreover p-value=0.963 corresponding to "Esteem" and this is very large and hence we get same conclusion.  

6.

One-way ANOVA: Days versus Major

Source DF SS MS F P
Major 2 9531 4766 4.92 0.014
Error 33 31981 969
Total 35 41513

Hence population means of groups of major are not all equal. Now we perform Tukey's HSD:

Tukey 95% Simultaneous Confidence Intervals
All Pairwise Comparisons among Levels of Major

Individual confidence level = 98.04%

Major = M1 subtracted from:

Major Lower Center Upper ---+---------+---------+---------+------
M2 -59.93 -29.88 0.17 (---------*---------)
M3 -68.24 -36.61 -4.99 (----------*---------)
---+---------+---------+---------+------
-60 -30 0 30

Major = M2 subtracted from:

Major Lower Center Upper ---+---------+---------+---------+------
M3 -39.44 -6.73 25.97 (----------*----------)
---+---------+---------+---------+------
-60 -30 0 30

From the above C.I.s, we see that Major 1 and Major 3 are significantly different since the corresponding C.I. does not contain zero.

Correlations: Days, Age

Pearson correlation of Days and Age = 0.397

P-Value = 0.017
Correlations: Days, Esteem

Pearson correlation of Days and Esteem = 0.045

P-Value = 0.795

Correlations: Age, Esteem

Pearson correlation of Age and Esteem = 0.094

P-Value = 0.584

All are positively correlated however correlation between Age and Days are significant since P-Value = 0.017<0.05.

The linear regression of Days on age is significant since p-value=0.017<0.05 whereas if add one more variable "Esteem" then it becomes insignificant since the p-value is 0.059>0.05.

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