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## How many people drink?

How many people drink?

Number of people who drink is 91+67+51+25=234

How many people have clinically significant CAGE findings that should be addressed?

 Frequency Percent 0 917 80% 1 91 8% 2 67 6% 3 51 4% 4 25 2%

Number of people with clinically significant CAGE findings that should be addressed is 67+51+25=143

The test-retest reliability was found to range from 0.80 to 0.95. What is this type of reliability and what does this indicate?

Internal consistency; Different questions, same construct.

The value indicates a high internal consistency (reliability) of the variables and proves that this research instrument was reliable.

The CAGE has been tested by comparing it to other alcohol screening tests, the AUDIT (Alcohol Use Disorders Identification Test) and the SMAST (short Michigan Alcoholism Screening Instrument) and found to have correlations of .70.

This instrument testing is an example of what form of validity testing?

This is an example of concurrent or convergent validity, a form of construct validity.

What does this form of testing tell us about the instrument?

It suggests a moderate to strong correlation supporting the construct validity of the instrument(r = 0.7).

Is there a need to reverse score any of the items in this survey: If so, which ones?

No there is no need to reverse score for any of the items in this survey

Provide the mean sum score & SD, median sum score, mode, and Interquartile Range.

 Statistics sum_score N Valid 1163 Missing 0 Mean 18.47 Median 14.00 Mode 13.00 Std. Deviation 7.88 Percentiles 25 13.00 50 14.00 75 22.00 Interquartile range 9.00

Is the data normally skewed? Support your answer. If not normally, skewed be sure to interpret your findings?

The data is not normally skewed but is right skewed (having a longer tail to the right).

Write a short paragraph presenting and interpreting the descriptives on engagement scores- you do not need to include every single descriptive measure, rather those that are most relevant to this data set so that the reader can understand the meaning of the findings.

The average sum engagement score was found to be 18.47 with a sum median score of 14 while the most frequent sum score was 13. The interquartile range was 9. With a mean of 18.47 (which is a lower value) it implies that on average there is a greater provider engagement. Similarly, with a sum score of 13 being the most frequent it means that majority of the respondents always feel a greater provider engagement.

You want to determine whether HCP scores vary by gender.

What is the test you will select? Give your rationale.

Since the data was observed to be non-normally distributed Kruskal–Wallis test   will be the most ideal test.

Is there a difference in HCP scores by gender? Give your rational, provide the data.

 Descriptive Statistics N Mean Std. Deviation Minimum Maximum hcp_sum_score 1163 18.4695 7.87716 13.00 52.00 subject gender 1161 1.70 .482 1 3
 Ranks subject gender N Mean Rank hcp_sum_score female 365 532.44 male 784 603.98 transgender 12 556.96 Total 1161
 Test Statisticsa,b hcp_sum_score Chi-Square 12.601 df 2 Asymp. Sig. .002 a. Kruskal Wallis Test b. Grouping Variable: subject gender

There was a statistically significant difference between the HCP sum score by different gender types (H(2)=18.47, p=0.002), with  a mean rank of 532.44 for the female respondents, 603.98 for the male respondents and 556.96 for the transgender respondents.

Which groups differ from each other? Give your rationale, provide the data and interpretation of the data.

Post-Hoc test showed that there was significant difference in the scores of the females and transgender individuals. However, there was no difference among the males and the females nor between the males and the transgender individuals.

 Multiple Comparisons Dependent Variable:   sum_score LSD (I) subject gender (J) subject gender Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound female male -1.40514* .49669 .005 -2.3797 -.4306 transgender -.07648 2.29966 .973 -4.5885 4.4355 male female 1.40514* .49669 .005 .4306 2.3797 transgender 1.32866 2.28002 .560 -3.1448 5.8021 transgender female .07648 2.29966 .973 -4.4355 4.5885 male -1.32866 2.28002 .560 -5.8021 3.1448 *. The mean difference is significant at the 0.05 level.

## How many people have clinically significant CAGE findings that should be addressed?

There are 8 items that need to be reverse-coded. What are these 8 items?

1a.satisfied with physical activity

2a.enjoyed living

2b.control of life

2c.satisfied with socially active

8a.i could see my hcp whenever i needed to

8b.hcp involves me in decision making

8c.hcp cares about me

Reverse code the 8 items.

Done

Using the recoded items:

What is the total mean score for HAT?

 Descriptive Statistics N Minimum Maximum Mean Std. Deviation hat_sum_score 1163 33.00 145.00 99.1006 21.77465 Valid N (listwise) 1163

Assume the data to be normally distributed. You want to determine if there is a difference in scores by gender.

What test will you use? Give your rationale.

Analysis of Variance (ANOVA) test; this is because the factors to be compared are more than two and also we are aware that the data is assumed to be normally distributed. The dependent variable is also continuous hence all the above reasons suffice on using ANOVA.

Is there a difference in scores by gender? (respond in a complete sentence providing data).

 ANOVA hat_sum_score Sum of Squares df Mean Square F Sig. Between Groups 21839.843 51 428.232 .897 .680 Within Groups 521599.268 1092 477.655 Total 543439.111 1143

Analysis of variance (ANOVA) showed that there was no significant main effect for treatment (gender), F(51, 1092) = 0.897, p = .680. Thus we concluded that there no significant difference in HAT scores by gender (p-value > 0.05).

Assume the data to be normally distributed. You want to determine if Quality of Life Scores differ by literacy level (high and low).

What test will you use? Give your rationale.

Independent t-test would be ideal for use; the test compares the means between two unrelated groups (high and low) on the same continuous, dependent variable (Quality of Life Scores).

Are the scores significantly different by literacy level? (Respond in a complete sentence providing data).

 Group Statistics Literacy level N Mean Std. Deviation Std. Error Mean hat_sum_score Low 159 102.6289 21.22053 1.68290 High 520 101.5346 20.14166 .88327
 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper hat_sum_score Equal variances assumed .162 .687 .592 677 .554 1.09432 1.84856 -2.53529 4.72392 Equal variances not assumed .576 251.234 .565 1.09432 1.90061 -2.64884 4.83747

No the scores are not significantly different by the literacy levels (p-value > 0.05).  An independent samples t-test was done to compare the mean HAT sum scores based on literacy levels (low or high) of the respondents. Results showed that respondents with low literacy levels (M = 102.62, SD = 21.22, N = 159) had no significant difference in scores as compared to those with higher literacy levels (M = 101.53, SD = 20.14, N = 520), t (677) = 0.592, p =0.554, two-tailed.  Essentially results showed that education literacy level of the respondents has no effect on the HAT scores.

Based on the hypotheses, would you use a 1- or 2-tailed significance test? State your rationale.

1-tailed significance test would be ideal to test the hypothesis; the reason is we are testing whether the intervention performs better

Identify the independent and dependent variables in the study…. For each identified variable, identify how they were measured.

Independent:

External cold and vibration stimulation

How it was measured:

External cold and vibration stimulation via Buzzy

Dependent:

Fear and anxiety levels;

How it was measured:

The pre-procedural and procedural fear and anxiety levels of the children were assessed via self-parental and observer reports. Pre-procedural anxiety was assessed using the Children’s Fear Scale, along with reports by the children, their parents, and an observer

Procedural pain was assessed using the Wong Baker Faces Scale and the visual analog scale self-reports of the children.

## The test-retest reliability was found to range from 0.80 to 0.95. What is this type of reliability and what does this indicate?

What is the population of interest? Discuss the strengths and weaknesses of the sampling and assignment methods.

Children during peripheral intravenous (IV) cannulation

The study employed Prospective randomized controlled trial

The strengths of this method are;

• The method eliminates bias in treatment assignment
• The method also facilitates blinding (masking) of the identity of treatments from investigators, participants, and assessors
• The method permits the use of probability theory to express the likelihood that any difference in outcome between treatment groups merely indicates chance.

The strengths of this method are;

• Conflict of interest may endanger the study e.g. if for instance the nurse had some conflict of interest then it is possible that this may influence the results in a negative way
• Time and cost; the method requires a time commitment and cost as well which may not be available.

In Table 1, the authors are comparing the 2 groups for similarity in demographic characteristics.

Why is this important to do and what do the results tell us?

This was important in determining group equivalency

Why was chi square used for the variable sex and a t-test for the remaining characteristics?

Sex is  a nominal variable and can be analyzed using Chi-Square test. T-test was used for the other characteristics since they were continuous variables.

Based on the data in Table 1, were the groups similar or different?

The groups were similar

What is the null hypothesis for observer-reported procedural anxiety for the two groups? Was this null hypothesis accepted or rejected in this study? Provide a rationale for your answer.

The null hypothesis is: There is no difference in observer-reported procedural anxiety levels between the Buzzy intervention and the control groups for school-age children. The t = -6.745 for observer-reported procedural anxiety levels, p = 0.000, which is less than alpha of 0.05 set for this study. Since the study result was significant, the null hypothesis was rejected.

What is the t-test result for BMI? Is this result statistically significant? Provide a rationale for your answer? What does this result mean for the study?

The mean BMI for the buzzy group was 25.41 (SD = 6.74) while that of the control group was 26.94 (SD = 8.68); conditions t(88) = –1.309, p = 0.192. This result is not statistically significant (p-value > 0.05). The results means that buzzy has no influence on the BMI.

Assuming the t-tests presented in Table 2 and Table 3 are all the t tests performed to analyze the dependent variables’ data, calculate a Bonferroni procedure for this study.

The Bonferroni procedure is calculated by alpha divided by number of t-tests conducted on study variable’s data. Note the researchers do not always report all t-tests conducted, especially if they were not statistically significant. The t-tests conducted on demographic data are not of concern. Canbulat reported the results of four t-tests conducted to examine the differences between the intervention and control groups for the dependent variables procedural self-reported pain with WBFS, procedural self-reported pain with VAS, parent-reported anxiety levels, and observer-reported anxiety levels. The Bonferroni calculation for this study: 0.05 (alpha) divided by number of t-tests conducted = 0.05/4 = 0.0125. The new alpha set for the study is 0.0125.

Would the t-test for observer-reported procedural anxiety be significant based on the more stringent alpha calculated using the Bonferroni procedure in question 7? Provide a rationale.

No; Based on the Bonferoni results = 0.0125, the t = -6.745, p = 0.000 is still significant since it is less than 0.0125.

Reference

Dancey, C., Reidy, J., & Rowe, R. (2012). Statistics for the health sciences: A non-mathematical introduction. Thousand Oaks, CA: Sage.

American Psychological Association (APA). (2010). Publication manual of the American Psychological Association, 6th Ed. Washington, DC: APA.

Dhalla, S., & Kopec, J. A. (2007). The CAGE Questionnaire for Alcohol Misuse: A Review of Reliability and Validity Studies. Clinical & Investigative Medicine,30(1), 33.

doi:10.25011/cim.v30i1.447

Canbulat, N., Ayhan, F., & Inal, S. (2015). Effectiveness of External Cold and Vibration for Procedural Pain Relief During Peripheral Intravenous Cannulation in Pediatric Patients. Pain Management Nursing,16(1), 33-39. doi:10.1016/j.pmn.2014.03.003

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250 words