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How a degree from this school can shape me to become a better person.

i am applying for my masters at Texas woman university( Healthcare Administration program).  this need to be a page and half.

these are key point i want listed on this paper.

 

· At an early age Texas woman’s university has been the school of my dreams.

· My dream is to become a Hospital Administrator (oversee financial aspects of the hospital, Set budget goals and costs for service, and direct fundraising and fees for special cases, and how a degree from this school can make my dreams come true.

· How a degree from this school can shape me to become a better person.

·  health field is in my family ( mother is CNA(Certified nursing assistant), sister is a LVN (Licensed vocational nurse)

·  I promise to work hard*/ me being a hard worker

Create a list of three best practices to follow in the field of health services administration.

· 1

“Sum It Up” Please respond to the following:

· Create a list of three best practices to follow in the field of health services administration. Rank the best practices in order of importance (one being the most important) and provide a rationale for your ranking.

· Rate the three most important concepts that you learned in this course in order of importance (one being the most important). Provide a rationale for your ratings.

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Maladaptive Responses to Immune Disorders

 

Maladaptive Responses to Immune Disorders

Maladaptive responses to disorders are compensatory mechanisms that ultimately have adverse health effects for patients. For instance, a patient’s allergic reaction to peanuts might lead to anaphylactic shock, or a patient struggling with depression might develop a substance-abuse problem. To properly diagnose and treat patients, advanced practice nurses must understand both the pathophysiology of disorders and potential maladaptive responses that some disorders cause.

Consider immune disorders, such as HIV, psoriasis, inflammatory bowel disease, and systemic lupus E. What are resulting maladaptive responses for patients with these disorders?

To prepare:

  • Review Chapter 5 and Chapter 7 in the Huether and McCance text, as well as the Yi, et al, article in the Learning Resources. Reflect on the concept of maladaptive responses to disorders.
  • Select two of the following immune disorders: HIV, psoriasis, inflammatory bowel disease, and/or systemic lupus E (SLE).
  • Think about the pathophysiology of each disorder you selected. Consider the compensatory mechanisms that the disorders trigger. Then, compare the resulting maladaptive and physiological responses of the two disorders.
  • Consider the types of drugs that would be prescribed to patients to treat symptoms associated with these disorders and why.
  • Select one of the following patient factors: genetics, gender, ethnicity, age, or behavior. Consider how your selected factor might impact the disorder. Then, reflect on how your selected factor might impact the effects of prescribed drugs, as well as any measures you might take to help reduce any negative side effects.

Discuss how the instructions found in 2 Timothy 2:15-16 can be used to  inspire Christian healthcare workers to consider quality in their work. 

Please answer each question separately. Each question must be 250-300  words each. Please be plagiarism free and also, make sure sources are  cited APA.

1.  How does the biblical teaching of the plumb line found in Amos 7:7-8  provide guidance to the Christian health administrator in the  measurement of quality?

2. Discuss how the instructions found in 2 Timothy 2:15-16 can be used to  inspire Christian healthcare workers to consider quality in their work.

Examine your ideas about the biblical concept of the Golden Rule and  present your philosophy of how understanding this scriptural context  might result in increased healthcare quality. 

Must be APA format, plagiarism free, avoid using contractions, and incorporate the christian view. 500 words minimum. Please check grammatical errors.

Examine your ideas about the biblical concept of the Golden Rule and  present your philosophy of how understanding this scriptural context  might result in increased healthcare quality.

Create a new form for the patient to sign, acknowledging receipt of the above documents

cenario

You have recently been promoted to Health Services Manager at Three Mountains Regional Hospital, a small hospital located in a mid-size city in the Midwest. Three Mountains is a general medical and surgical facility with 400 beds. Last year there were approximately 62,000 emergency visits and 15,000 admissions. More than 6,000 outpatient and 10,000 inpatient surgeries were performed.

The CEO and the Board of Directors have tasked you with developing an intake packet for new patients that will help establish patient trust in the facility and its employees. The patient packet will address new patient concerns by including information about HIPAA, informed consent, a confidential health history report, and a living will. The new packet will also include the values of the organization and a code of ethics.

Instructions

You are now ready to take the basic components you have created so far and, using those as a foundation, create the final Intake Packet the hospital will use during admissions. The Intake Packet will be comprised of the following elements:

  • A New Patient Letter to accompany the Intake Packet
    • The letter should be in business letter format (Here is a library resource for help writing a business letter.)
    • The letter should address the following points for the patient:
      • An explanation of the importance of ethics
      • Why each part of the packet is included
      • How the packet is to be used
    • The letter should also include a HIPAA/Confidentiality statement
    • The letter should also include a Privacy Pledge
  • The Code of Ethics
    • Based on your PowerPoint Presentation, create a one-page bulleted Code of Ethics
  • A sample Living Will
    • Make any necessary changes to your Living Will template and include it as part of the Intake Packet
  • Create a new form for the patient to sign, acknowledging receipt of the above documents

In addition, you will craft an email to the CEO and the Board of Directors, explaining the purpose of the Intake Packet and all its components. Your email should use proper email formatting (including subject line description) and contain language appropriate to the receiver. (Here is a library resource for help writing a professional email.)

Finally, develop a PowerPoint presentation with audio for placement on the Facility website. (Here is a library resource for help creating a PowerPoint presentation.) Your audience includes past, current, and future patients. In the PowerPoint presentation, you will address the following:

  • The Ethics and Values of the organization and an overview of the Code of Ethics
  • The Purpose of the Intake Packet
  • Explanation of the Privacy Policy
  • Explanation of HIPAA, and its goals and purpose
  • Description and encouragement to sign the form acknowledging receipt

The PowerPoint presentation (or other shareable Webware/software you prefer) should be done with narration in which you explain each component of the Intake Packet.

  1. The PowerPoint should be between 10 and 15 slides.
  2. Describe each component of the Intake Packet.
  3. Use the notes area on each slide as needed to expand on the key points.
  4. You may use a free screen capture site such as Screencast-O-Matic to record a video of your presentation. Screencast-O-Matic is a site and program that can perform screen desk and audio capture up to 15 minutes for free, and can be utilized on a Windows or Mac computer. (Note: You can use another, similar program if you prefer. Screencast-O-Matic is only a suggestion). Make sure that both your voice and the PowerPoint slides are captured on the video.

NOTE – APA formatting for the reference list, and proper grammar, punctuation, and form required. APA help is available here.

ANOVA Interpretation Exercise

ANOVA Interpretation Exercise

Prior to beginning work on this assignment, read the scenario and ANOVA results provided in an announcement by your instructor and the Analysis of Variance (ANOVA) and Non-Normal Data: Is ANOVA Still a Valid Option? articles, review the required chapters of the Tanner textbook and the Jarman e-book, and watch the One-Way ANOVA video. In your paper, identify the research question and the hypothesis being tested in the assigned scenario. Consider the following questions: What are the independent and dependent variables, sample size, treatments, etcetera? What type of ANOVA was used in this scenario? What do the results mean in statistical and practical terms?

In your paper,

  • Determine what question(s) the researchers are trying to answer by doing this research.
  • Determine the hypotheses being tested. Is the alternative hypothesis directional or nondirectional?
  • Identify the independent variable(s), the dependent variable, and the specific type of ANOVA used.
  • Determine the sample size and the number of groups from information given in the ANOVA table.
  • Discuss briefly the assumptions and limitations that apply to ANOVA.
  • Interpret the ANOVA results in terms of statistical significance and in relation to the research question.

The ANOVA Interpretation Exercise assignment

  • Must be two to three double-spaced pages in length (not including title and references pages) and formatted according to APA Style as outlined in the Ashford Writing Center’s APA Style (Links to an external site.)
  • Must include a separate title page with the following:
    • Title of paper
    • Student’s name
    • Course name and number
    • Instructor’s name
    • Date submitted

For further assistance with the formatting and the title page, refer to APA Formatting for Word 2013 (Links to an external site.).

  • Must utilize academic voice. See the Academic Voice (Links to an external site.) resource for additional guidance.
  • Must use the course text and document any information used from sources in APA Style as outlined in the Ashford Writing Center’s APA: Citing Within Your Paper (Links to an external site.)
  • Must include a separate references page that is formatted according to APA Style as outlined in the Ashford Writing Center. See the APA: Formatting Your References List (Links to an external site.) resource in the Ashford Writing Center for specifications.

References:

https://digital-films-com.proxy-library.ashford.edu/p_ViewVideo.aspx?xtid=111550

María J. Blanca, Rafael Alarcón, Jaume Arnau, Roser Bono and Rebecca Bendayan

552

One-way analysis of variance (ANOVA) or F-test is one of the most common statistical techniques in educational and psychological research (Keselman et al., 1998; Kieffer, Reese, & Thompson, 2001). The F-test assumes that the outcome variable is normally and independently distributed with equal variances among groups. However, real data are often not normally distributed and variances are not always equal. With regard to normality, Micceri (1989) analyzed 440 distributions from ability and psychometric measures and found that most of them were contaminated, including different types of tail weight (uniform to double exponential) and different classes of asymmetry. Blanca, Arnau, López-Montiel, Bono, and Bendayan (2013) analyzed 693 real datasets from psychological variables and found that 80% of them presented values of skewness and kurtosis ranging between -1.25 and 1.25, with extreme departures from the normal

distribution being infrequent. These results were consistent with other studies with real data (e.g., Harvey & Siddique, 2000; Kobayashi, 2005; Van Der Linder, 2006).

The effect of non-normality on F-test robustness has, since the 1930s, been extensively studied under a wide variety of conditions. As our aim is to examine the independent effect of non-normality the literature review focuses on studies that assumed variance homogeneity. Monte Carlo studies have considered unknown and known distributions such as mixed non-normal, lognormal, Poisson, exponential, uniform, chi-square, double exponential, Student’s t, binomial, gamma, Cauchy, and beta (Black, Ard, Smith, & Schibik, 2010; Bünning, 1997; Clinch & Kesselman, 1982; Feir-Walsh & Thoothaker, 1974; Gamage & Weerahandi, 1998; Lix, Keselman, & Keselman, 1996; Patrick, 2007; Schmider, Ziegler, Danay, Beyer, & Bühner, 2010).

One of the fi rst studies on this topic was carried out by Pearson (1931), who found that F-test was valid provided that the deviation from normality was not extreme and the number of degrees of freedom apportioned to the residual variation was not too small. Norton (1951, cit. Lindquist, 1953) analyzed the effect of distribution shape on robustness (considering either that the distributions had the same shape in all the groups or a different shape in each group)

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Non-normal data: Is ANOVA still a valid option?

María J. Blanca1, Rafael Alarcón1, Jaume Arnau2, Roser Bono2 and Rebecca Bendayan1,3 1 Universidad de Málaga, 2 Universidad de Barcelona and 3 MRC Unit for Lifelong Health and Ageing, University College London

Abstract Resumen

Background: The robustness of F-test to non-normality has been studied from the 1930s through to the present day. However, this extensive body of research has yielded contradictory results, there being evidence both for and against its robustness. This study provides a systematic examination of F-test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences. Method: We conducted a Monte Carlo simulation study involving a design with three groups and several known and unknown distributions. The manipulated variables were: Equal and unequal group sample sizes; group sample size and total sample size; coeffi cient of sample size variation; shape of the distribution and equal or unequal shapes of the group distributions; and pairing of group size with the degree of contamination in the distribution. Results: The results showed that in terms of Type I error the F-test was robust in 100% of the cases studied, independently of the manipulated conditions.

Keywords: F-test, ANOVA, robustness, skewness, kurtosis.

Datos no normales: ¿es el ANOVA una opción válida? Antecedentes: las consecuencias de la violación de la normalidad sobre la robustez del estadístico F han sido estudiadas desde 1930 y siguen siendo de interés en la actualidad. Sin embargo, aunque la investigación ha sido extensa, los resultados son contradictorios, encontrándose evidencia a favor y en contra de su robustez. El presente estudio presenta un análisis sistemático de la robustez del estadístico F en términos de error de Tipo I ante violaciones de la normalidad, considerando una amplia variedad de distribuciones frecuentemente encontradas en ciencias sociales y de la salud. Método: se ha realizado un estudio de simulación Monte Carlo considerando un diseño de tres grupos y diferentes distribuciones conocidas y no conocidas. Las variables manipuladas han sido: igualdad o desigualdad del tamaño de los grupos, tamaño muestral total y de los grupos; coefi ciente de variación del tamaño muestral; forma de la distribución e igualdad o desigualdad de la forma en los grupos; y emparejamiento entre el tamaño muestral con el grado de contaminación en la distribución. Resultados: los resultados muestran que el estadístico F es robusto en términos de error de Tipo I en el 100% de los casos estudiados, independientemente de las condiciones manipuladas.

Palabras clave: estadístico F, ANOVA, robustez, asimetría, curtosis.

Psicothema 2017, Vol. 29, No. 4, 552-557 doi: 10.7334/psicothema2016.383

Received: December 14, 2016 • Accepted: June 20, 2017 Corresponding author: María J. Blanca Facultad de Psicología Universidad de Málaga 29071 Málaga (Spain) e-mail: blamen@uma.es

Non-normal data: Is ANOVA still a valid option?

553

and found that, in general, F-test was quite robust, the effect being negligible. Likewise, Tiku (1964) stated that distributions with skewness values in a different direction had a greater effect than did those with values in the same direction unless the degrees of freedom for error were fairly large. However, Glass, Peckham, and Sanders (1972) summarized these early studies and concluded that the procedure was affected by kurtosis, whereas skewness had very little effect. Conversely, Harwell, Rubinstein, Hayes, and Olds (1992), using meta-analytic techniques, found that skewness had more effect than kurtosis. A subsequent meta-analytic study by Lix et al. (1996) concluded that Type I error performance did not appear to be affected by non-normality.

These inconsistencies may be attributable to the fact that a standard criterion has not been used to assess robustness, thus leading to different interpretations of the Type I error rate. The use of a single and standard criterion such as that proposed by Bradley (1978) would be helpful in this context. According to Bradley’s (1978) liberal criterion a statistical test is considered robust if the empirical Type I error rate is between .025 and .075 for a nominal alpha level of .05. In fact, had Bradley’s criterion of robustness been adopted in the abovementioned studies, many of their results would have been interpreted differently, leading to different conclusions. Furthermore, when this criterion is considered, more recent studies provide empirical evidence for the robustness of F-test under non-normality with homogeneity of variances (Black et al., 2010; Clinch & Keselman, 1982; Feir-Walsh & Thoothaker, 1974; Gamage & Weerahandi, 1998; Kanji, 1976; Lantz, 2013; Patrick, 2007; Schmider et al., 2010; Zijlstra, 2004).

Based on most early studies, many classical handbooks on research methods in education and psychology draw the following conclusions: Moderate departures from normality are of little concern in the fi xed-effects analysis of variance (Montgomery, 1991); violations of normality do not constitute a serious problem, unless the violations are especially severe (Keppel, 1982); F-test is robust to moderate departures from normality when sample sizes are reasonably large and are equal (Winer, Brown, & Michels, 1991); and researchers do not need to be concerned about moderate departures from normality provided that the populations are homogeneous in form (Kirk, 2013). To summarize, F-test is robust to departures from normality when: a) the departure is moderate; b) the populations have the same distributional shape; and c) the sample sizes are large and equal. However, these conclusions are broad and ambiguous, and they are not helpful when it comes to deciding whether or not F-test can be used. The main problem is that expressions such as “moderate”, “severe” and “reasonably large sample size” are subject to different interpretations and, consequently, they do not constitute a standard guideline that helps applied researchers decide whether they can trust their F-test results under non-normality.

Given this situation, the main goals of the present study are to provide a systematic examination of F-test robustness, in terms of Type I error, to violations of normality under homogeneity using a standard criterion such as that proposed by Bradley (1978). Specifi cally, we aim to answer the following questions: Is F-test robust to slight and moderate departures from normality? Is it robust to severe departures from normality? Is it sensitive to differences in shape among the groups? Does its robustness depend on the sample sizes? Is its robustness associated with equal or unequal sample sizes?

To this end, we designed a Monte Carlo simulation study to examine the effect of a wide variety of distributions commonly

found in the health and social sciences on the robustness of F-test. Distributions with a slight and moderate degree of contamination (Blanca et al., 2013) were simulated by generating distributions with values of skewness and kurtosis ranging between -1 and 1. Distributions with a severe degree of contamination (Micceri, 1989) were represented by exponential, double exponential, and chi-square with 8 degrees of freedom. In both cases, a wide range of sample sizes were considered with balanced and unbalanced designs and with equal and unequal distributions in groups. With unequal sample size and unequal shape in the groups, the pairing of group sample size with the degree of contamination in the distribution was also investigated.

Method

Instruments

We conducted a Monte Carlo simulation study with non- normal data using SAS 9.4. (SAS Institute, 2013). Non-normal distributions were generated using the procedure proposed by Fleishman (1978), which uses a polynomial transformation to generate data with specifi c values of skewness and kurtosis.

Procedure

In order to examine the effect of non-normality on F-test robustness, a one-way design with 3 groups and homogeneity of variance was considered. The group effect was set to zero in the population model. The following variables were manipulated:

1. Equal and unequal group sample sizes. Unbalanced designs are more common than balanced designs in studies involving one-way and factorial ANOVA (Golinski & Cribbie, 2009; Keselman et al., 1998). Both were considered in order to extend our results to different research situations.

2. Group sample size and total sample size. A wide range of group sample sizes were considered, enabling us to study small, medium, and large sample sizes. With balanced designs the group sizes were set to 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, and 100, with total sample size ranging from 15 to 300. With unbalanced designs, group sizes were set between 5 and 160, with a mean group size of between 10 and 100 and total sample size ranging from 15 to 300.

3. Coeffi cient of sample size variation (Δn), which represents the amount of inequality in group sizes. This was computed by dividing the standard deviation of the group sample size by its mean. Different degrees of variation were considered and were grouped as low, medium, and high. A low Δn was fi xed at approximately 0.16 (0.141 – 0.178), a medium coeffi cient at 0.33 (0.316 – 0.334), and a high value at 0.50 (0.491 – 0.521). Keselman et al. (1998) showed that the ratio of the largest to the smallest group size was greater than 3 in 43.5% of cases. With Δn = 0.16 this ratio was equal to 1.5, with Δn = 0.33 it was equal to either 2.3 or 2.5, and with Δn = 0.50 it ranged from 3.3 to 5.7.

4. Shape of the distribution and equal and unequal shape in the groups. Twenty-two distributions were investigated, involving several degrees of deviation from normality and with both equal and unequal shape in the groups. For equal shape and slight and moderate departures from normality,

María J. Blanca, Rafael Alarcón, Jaume Arnau, Roser Bono and Rebecca Bendayan

554

the distributions had values of skewness (γ 1 ) and kurtosis (γ

2 )

ranging between -1 and 1, these values being representative of real data (Blanca et al., 2013). The values of γ

1 and γ

2

are presented in Table 2 (distributions 1-12). For severe departures from normality, distributions had values of γ

1 and

γ 2 corresponding to the double exponential, chi-square with

8 degrees of freedom, and exponential distributions (Table 2, distributions 13-15). For unequal shape, the values of γ

1

and γ 2 of each group are presented in Table 3. Distributions

16-21 correspond to slight and moderate departures from normality and distribution 22 to severe departure.

5. Pairing of group size with degree of contamination in the distribution. This condition was included with unequal shape and unequal sample size. The pairing was positive when the largest group size was associated with the greater contamination, and vice versa. The pairing was negative when the largest group size was associated with the smallest contamination, and vice versa. The specifi c conditions with unequal sample size are shown in Table 1.

Ten thousand replications of the 1308 conditions resulting from the combination of the above variables were performed at a signifi cance level of .05. This number of replications was chosen to ensure reliable results (Bendayan, Arnau, Blanca, & Bono, 2014; Robey & Barcikowski, 1992).

Data analysis

Empirical Type I error rates associated with F-test were analyzed for each condition according to Bradley’s robustness criterion (1978).

Results Tables 2 and 3 show descriptive statistics for the Type I error

rate across conditions for equal and unequal shapes. Although the tables do not include all available information (due to article length limitations), the maximum and minimum values are suffi cient for assessing robustness. Full tables are available upon request from the corresponding author.

All empirical Type I error rates were within the bounds of Bradley’s criterion. The results show that F-test is robust for 3 groups in 100% of cases, regardless of the degree of deviation from a normal distribution, sample size, balanced or unbalanced cells, and equal or unequal distribution in the groups.

Discussion

We aimed to provide a systematic examination of F-test robustness to violations of normality under homogeneity of variance, applying Bradley’s (1978) criterion. Specifi cally, we sought to answer the following question: Is F-test robust, in terms of Type I error, to slight, moderate, and severe departures from normality, with various sample sizes (equal or unequal sample size) and with same or different shapes in the groups? The answer to this question is a resounding yes, since F-test controlled Type I error to within the bounds of Bradley’s criterion. Specifi cally, the results show that F-test remains robust with 3 groups when distributions have values of skewness and kurtosis ranging between -1 and 1, as well as with data showing a greater departure

from normality, such as the exponential, double exponential, and chi-squared (8) distributions. This applies even when sample sizes are very small (i.e., n= 5) and quite different in the groups, and also when the group distributions differ signifi cantly. In addition, the test’s robustness is independent of the pairing of group size with the degree of contamination in the distribution.

Our results support the idea that the discrepancies between studies on the effect of non-normality may be primarily attributed to differences in the robustness criterion adopted, rather than to the degree of contamination of the distributions. These fi ndings highlight the need to establish a standard criterion of robustness to clarify the potential implications when performing Monte Carlo studies. The present analysis made use of Bradley’s criterion, which has been argued to be one of the most suitable criteria for

Table 1 Specifi c conditions studied under non-normality for unequal shape in

the groups as a function of total sample size (N), means group size (N/J), coeffi cient of sample size variation (Δn), and pairing of group size with the degree of distribution contamination: (+) the largest group size is associated

with the greater contamination and vice versa, and (-) the largest group size is associated with the smallest contamination and vice versa

n Pairing

N N/J Δn + –

30 10 0.16 0.33 0.50

8, 10, 12 6, 10, 14 5, 8, 17

12, 10, 8 14, 10, 6 17, 8, 5

45 15 0.16 0.33 0.50

12, 15, 18 9, 15, 21 6, 15, 24

18, 15, 12 21, 15, 9 24, 15, 6

60 20 0.16 0.33 0.50

16, 20, 24 12, 20, 28 8, 20, 32

24, 20, 16 28, 20, 12 32, 20, 8

75 25 0.16 0.33 0.50

20, 25, 30 15, 25, 35 10, 25, 40

30, 25, 20 35, 25, 15 40, 25, 10

90 30 0.16 0.33 0.50

24, 30, 36 18, 30, 42 12, 30, 48

36, 30, 24 42, 30, 18 48, 30, 12

120 40 0.16 0.33 0.50

32, 40, 48 24, 40, 56 16, 40, 64

48, 40, 32 56, 40, 24 64, 40, 16

150 50 0.16 0.33 0.50

40, 50, 60 30, 50, 70 20, 50, 80

60, 50, 40 70, 50, 30 80, 50, 20

180 60 0.16 0.33 0.50

48, 60, 72 36, 60, 84 24, 60, 96

72, 60, 48 84, 60, 36 96, 60, 24

210 70 0.16 0.33 0.50

56, 70, 84 42, 70, 98 28, 70, 112

84, 70, 56 98, 70, 42 112, 70, 28

240 80 0.16 0.33 0.50

64, 80, 96 48, 80, 112 32, 80, 128

96, 80, 64 112, 80, 48 128, 80, 32

270 90 0.16 0.33 0.50

72, 90, 108 54, 90, 126 36, 90, 144

108, 90, 72 126, 90, 54 144, 90, 36

300 100 0.16 0.33 0.50

80, 100, 120 60, 100, 140 40, 100, 160

120, 100, 80 140, 100, 60 160, 100, 40

Non-normal data: Is ANOVA still a valid option?

555

examining the robustness of statistical tests (Keselman, Algina, Kowalchuk, & Wolfi nger, 1999). In this respect, our results are consistent with previous studies whose Type I error rates were within the bounds of Bradley’s criterion under certain departures from normality (Black et al., 2010; Clinch & Keselman, 1982; Feir-Walsh & Thoothaker, 1974; Gamage & Weerahandi, 1998; Kanji, 1976; Lantz, 2013; Lix et al., 1996; Patrick, 2007; Schmider et al., 2010; Zijlstra, 2004). By contrast, however, our results do not concur, at least for the conditions studied here, with those classical handbooks which conclude that F-test is only robust if the departure from normality is moderate (Keppel, 1982; Montgomery, 1991), the populations have the same distributional shape (Kirk, 2013), and the sample sizes are large and equal (Winer et al., 1991).

Our fi ndings are useful for applied research since they show that, in terms of Type I error, F-test remains a valid statistical procedure under non-normality in a variety of conditions. Data transformation or nonparametric analysis is often recommended when data are not normally distributed. However, data transformations offer no additional benefi ts over the good control of Type I error achieved by F-test. Furthermore, it is usually diffi cult to determine which transformation is appropriate for a set of data, and a given transformation may not be applicable when

groups differ in shape. In addition, results are often diffi cult to interpret when data transformations are adopted. There are also disadvantages to using non-parametric procedures such as the Kruskal-Wallis test. This test converts quantitative continuous data into rank-ordered data, with a consequent loss of information. Moreover, the null hypothesis associated with the Kruskal-Wallis test differs from that of F-test, unless the distribution of groups has exactly the same shape (see Maxwell & Delaney, 2004). Given these limitations, there is no reason to prefer the Kruskal-Wallis test under the conditions studied in the present paper. Only with equal shape in the groups might the Kruskal-Wallis test be preferable, given its power advantage over F-test under specifi c distributions (Büning, 1997; Lantz, 2013). However, other studies suggest that F-test is robust, in terms of power, to violations of normality under certain conditions (Ferreira, Rocha, & Mequelino, 2012; Kanji, 1976; Schmider et al., 2010), even with very small sample size (n = 3; Khan & Rayner, 2003). In light of these inconsistencies, future research should explore the power of F-test when the normality assumption is not met. At all events, we encourage researchers to analyze the distribution underlying their data (e.g., coeffi cients of skewness and kurtosis in each group, goodness of fi t tests, and normality graphs) and to estimate a priori the sample size needed to achieve the desired power.

Table 2 Descriptive statistics of Type I error for F-test with equal shape for each combination of skewness (γ

1 ) and kurtosis (γ

2 ) across all conditions

Distributions γ1 γ2 n Min Max Mdn M SD

1 0 0.4 = ≠

.0434

.0445 .0541 .0556

.0491

.0497 .0493 .0496

.0029

.0022

2 0 0.8 = ≠

.0444

.0458 .0534 .0527

.0474

.0484 .0479 .0487

.0023

.0016

3 0 -0.8 = ≠

.0468

.0426 .0512 .0532

.0490

.0486 .0491 .0487

.0014

.0024

4 0.4 0 = ≠

.0360

.0392 .0499 .0534

.0469

.0477 .0457 .0472

.0044

.0032

5 0.8 0 = ≠

.0422

.0433 .0528 .0553

.0477

.0491 .0476 .0491

.0029

.0030

6 -0.8 0 = ≠

.0427

.0457 .0551 .0549

.0475

.0487 .0484 .0492

.0038

.0024

7 0.4 0.4 = ≠

.0426

.0417 .0533 .0533

.0487

.0486 .0488 .0487

.0031

.0026

8 0.4 0.8 = ≠

.0449

.0456 .0516 .0537

.0483

.0489 .0485 .0489

.0019

.0020

9 0.8 0.4 = ≠

.0372

.0413 .0494 .0518

.0475

.0481 .0463 .0475

.0033

.0026

10 0.8 1 = ≠

.0458

.0463 .0517 .0540

.0494

.0502 .0492 .0501

.0017

.0023

11 1 0.8 = ≠

.0398

.0430 .0506 .0542

.0470

.0489 .0463 .0485

.0028

.0029

12 1 1 = ≠

.0377

.0366 .0507 .0512

.0453

.0466 .0451 .0462

.0042

.0032

13 0 3 = ≠

.0443

.0435 .0517 .0543

.0477

.0490 .0479 .0489

.0022

.0024

14 1 3 = ≠

.0431

.0462 .0530 .0548

.0487

.0494 .0486 .0499

.0032

.0017

15 2 6 = ≠

.0474

.0442 .0524 .0526

.0496

.0483 .0497 .0488

.0017

.0022

María J. Blanca, Rafael Alarcón, Jaume Arnau, Roser Bono and Rebecca Bendayan

556

As the present study sought to provide a systematic examination of the independent effect of non-normality on F-test Type I error rate, variance homogeneity was assumed. However, previous studies have found that F-test is sensitive to violations of homogeneity assumptions (Alexander & Govern, 1994; Blanca, Alarcón, Arnau, & Bono, in press; Büning, 1997; Gamage & Weerahandi, 1998; Harwell et al., 1992; Lee & Ahn, 2003; Lix et al., 1996; Moder, 2010; Patrick, 2007; Yiǧit & Gökpinar, 2010; Zijlstra, 2004), and several procedures have been proposed for dealing with heteroscedasticity (e.g., Alexander & Govern, 1994; Brown-Forsythe, 1974; Chen & Chen, 1998; Krishnamoorthy, Lu, & Mathew, 2007; Lee & Ahn, 2003; Li, Wang, & Liang, 2011; Lix & Keselman, 1998; Weerahandi, 1995; Welch, 1951). This suggests that heterogeneity has a greater

effect on F-test robustness than does non-normality. Future research should therefore also consider violations of homogeneity.

To sum up, the present results provide empirical evidence for the robustness of F-test under a wide variety of conditions (1308) involving non-normal distributions likely to represent real data. Researchers can use these fi ndings to determine whether F-test is a valid option when testing hypotheses about means in their data.

Acknowledgements

This research was supported by grants PSI2012-32662 and PSI2016-78737-P (AEI/FEDER, UE; Spanish Ministry of Economy, Industry, and Competitiveness).

Table 3 Descriptive statistics of Type I error for F-test with unequal shape for each combination of skewness (γ

1 ) and kurtosis (γ

2 ) across all conditions

Distributions Group γ1 γ2 n Min Max Mnd M SD

16 1 2 3

0 0 0

0.2 0.4 0.6

= ≠

.0434

.0433 .0541 .0540

.0491

.0490 .0493 .0487

.0029

.0025

17 1 2 3

0 0 0

0.2 0.4 -0.6

= ≠

.0472

.0409 .0543 .0579

.0513

.0509 .0509 .0510

.0024

.0033

18 1 2 3

0.2 0.4 0.6

0 0 0

= ≠

.0426

.0409 .0685 .0736

.0577

.0563 .0578 .0569

.0077

.0072

19 1 2 3

0.2 0.4 -0.6

0 0 0

= ≠

.0481

.0449 .0546 .0574

.0501

.0497 .0504 .0499

.0020

.0024

20 1 2 3

0.2 0.4 0.6

0.4 0.6 0.8

= ≠

.0474

.0433 .0524 .0662

.0496

.0535 .0497 .0545

.0017

.0057

21 1 2 3

0.2 0.6 1

0.4 0.8 1.2

= ≠

.0462

.0419 .0537 .0598

.0503

.0499 .0501 .0502

.0024

.0025

22 1 2 3

0 1 2

3 3 6

= ≠

.0460

.0424 .0542 .0577

.0490

.0503 .0494 .0499

.0027

.0029

References

Alexander, R. A., & Govern, D. M. (1994). A new and simpler approximation for ANOVA under variance heterogeneity. Journal of Educational and Behavioral Statistics, 19, 91-101.

Bendayan, R., Arnau, J., Blanca, M. J., & Bono, R. (2014). Comparison of the procedures of Fleishman and Ramberg et al., for generating non- normal data in simulation studies. Anales de Psicología, 30, 364-371.

Black, G., Ard, D., Smith, J., & Schibik, T. (2010). The impact of the Weibull distribution on the performance of the single-factor ANOVA model. International Journal of Industrial Engineering Computations, 1, 185-198.

Blanca, M. J., Alarcón, R., Arnau, J., & Bono, R. (in press). Effect of variance ratio on ANOVA robustness: Might 1.5 be the limit? Behavior Research Methods.

Blanca, M. J., Arnau, J., López-Montiel, D., Bono, R., & Bendayan, R. (2013). Skewness and kurtosis in real data samples. Methodology, 9, 78-84.

Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical Psychology, 31, 144-152.

Brown, M.B., & Forsythe, A.B. (1974). The small sample behaviour of some statistics which test the equality of several means. Technomectrics, 16, 129-132.

Büning, H. (1997). Robust analysis of variance. Journal of Applied Statistics, 24, 319-332.

Chen, S.Y., & Chen, H.J. (1998). Single-stage analysis of variance under heteroscedasticity. Communications in Statistics – Simulation and Computation, 27, 641-666.

Clinch, J. J., & Kesselman, H. J. (1982). Parametric alternatives to the analysis of variance. Journal of Educational Statistics, 7, 207-214.

Feir-Walsh, B. J., & Thoothaker, L. E. (1974). An empirical comparison of the ANOVA F-test, normal scores test and Kruskal-Wallis test under violation of assumptions. Educational and Psychological Measurement, 34, 789-799.

Ferreira, E. B., Rocha, M. C., & Mequelino, D. B. (2012). Monte Carlo evaluation of the ANOVA’s F and Kruskal-Wallis tests under binomial distribution. Sigmae, 1, 126-139.

Non-normal data: Is ANOVA still a valid option?

557

Fleishman, A. I. (1978). A method for simulating non-normal distributions. Psychometrika, 43, 521-532.

Gamage, J., & Weerahandi, S. (1998). Size performance of some tests in one-way ANOVA. Communications in Statistics – Simulation and Computation, 27, 625-640.

Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972). Consequences of failure to meet assumptions underlying the fi xed effects analyses of variance and covariance. Review of Educational Research, 42, 237-288.

Golinski, C., & Cribbie, R. A. (2009). The expanding role of quantitative methodologists in advancing psychology. Canadian Psychology, 50, 83-90.

Harvey, C., & Siddique, A. (2000). Conditional skewness in asset pricing test. Journal of Finance, 55, 1263-1295.

Harwell, M. R., Rubinstein, E. N., Hayes, W. S., & Olds, C. C. (1992). Summarizing Monte Carlo results in methodological research: The one- and two-factor fi xed effects ANOVA cases. Journal of Educational and Behavioral Statistics, 17, 315-339.

Kanji, G. K. (1976). Effect of non-normality on the power in analysis of variance: A simulation study. International Journal of Mathematical Education in Science and Technology, 7, 155-160.

Keppel, G. (1982). Design and analysis. A researcher’s handbook (2nd ed.). New Jersey: Prentice-Hall.

Keselman, H. J., Algina, J., Kowalchuk, R. K., & Wolfi nger, R. D. (1999). A comparison of recent approaches to the analysis of repeated measurements. British Journal of Mathematical and Statistical Psychology, 52, 63-78.

Keselman, H. J., Huberty, C. J., Lix, L. M., Olejnik, S., Cribbie, R. A., Donahue, B.,…, Levin, J. R. (1998). Statistical practices of educational researchers: An analysis of their ANOVA, MANOVA, and ANCOVA analyses. Review of Educational Research, 68, 350-386.

Khan, A., & Rayner, G. D. (2003). Robustness to non-normality of common tests for the many-sample location problem. Journal of Applied Mathematics and Decision Sciences, 7, 187-206.

Kieffer, K. M., Reese, R. J., & Thompson, B. (2001). Statistical techniques employed in AERJ and JCP articles from 1988 to 1997: A methodological review. The Journal of Experimental Education, 69, 280-309.

Kirk, R. E. (2013). Experimental design. Procedures for the behavioral sciences (4th ed.). Thousand Oaks: Sage Publications.

Kobayashi, K. (2005). Analysis of quantitative data obtained from toxicity studies showing non-normal distribution. The Journal of Toxicological Science, 30, 127-134.

Krishnamoorthy, K., Lu, F., & Mathew, T. (2007). A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models. Computational Statistics & Data Analysis 51, 5731-5742.

Lantz, B. (2013). The impact of sample non-normality on ANOVA and alternative methods. British Journal of Mathematical and Statistical Psychology, 66, 224-244.

Lee, S., & Ahn, C. H. (2003). Modifi ed ANOVA for unequal variances. Communications in Statistics – Simulation and Computation, 32, 987- 1004.

Li, X., Wang, J., & Liang, H. (2011). Comparison of several means: A fi ducial based approach. Computational Statistics and Data Analysis, 55, 1993-2002.

Lindquist, E. F. (1953). Design and analysis of experiments in psychology and education. Boston: Houghton Miffl in.

Lix, L.M., & Keselman, H.J. (1998). To trim or not to trim: Tests of mean equality under heteroscedasticity and nonnormality. Educational and Psychological Measurement, 58, 409-429.

Lix, L. M., Keselman, J. C., & Keselman, H. J. (1996). Consequences of assumption violations revisited: A quantitative review of alternatives to the one-way analysis of variance F test. Review of Educational Research, 66, 579-619.

Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd ed.). Mahwah: Lawrence Erlbaum Associates.

Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156-166.

Moder, K. (2010). Alternatives to F-test in one way ANOVA in case of heterogeneity of variances (a simulation study). Psychological Test and Assessment Modeling, 52, 343-353.

Montgomery, D. C. (1991). Design and analysis of experiments (3rd ed.). New York, NY: John Wiley & Sons, Inc.

Patrick, J. D. (2007). Simulations to analyze Type I error and power in the ANOVA F test and nonparametric alternatives (Master’s thesis, University of West Florida). Retrieved from http://etd.fcla.edu/WF/ WFE0000158/Patrick_Joshua_Daniel_200905_MS.pdf

Pearson, E. S. (1931). The analysis of variance in cases of non-normal variation. Biometrika, 23, 114-133.

Robey, R. R., & Barcikowski, R. S. (1992). Type I error and the number of iterations in Monte Carlo studies of robustness. British Journal of Mathematical and Statistical Psychology, 45, 283-288.

SAS Institute Inc. (2013). SAS® 9.4 guide to software Updates. Cary: SAS Institute Inc.

Schmider, E., Ziegler, M., Danay, E., Beyer, L., & Bühner, M. (2010). Is it really robust? Reinvestigating the robustness of ANOVA against violations of the normal distribution assumption. Methodology, 6, 147- 151.

Tiku, M. L. (1964). Approximating the general non-normal variance-ratio sampling distributions. Biometrika, 51, 83-95.

Van Der Linder, W. J. (2006). A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics, 31, 181- 204.

Welch, B.L. (1951). On the comparison of several mean values: An alternative approach. Biometrika, 38, 330-336.

Weerahandi, S. (1995). ANOVA under unequal error variances. Biometrics, 51, 589-599.

Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical principles in experimental design (3rd ed.). New York: McGraw-Hill.

Yiğit, E., & Gökpınar, F. (2010). A Simulation study on tests for one-way ANOVA under the unequal variance assumption. Communications Faculty of Sciences University of Ankara, Series A1, 59, 15-34.

Zijlstra, W. (2004). Comparing the Student´s t and the ANOVA contrast procedure with fi ve alternative procedures (Master’s thesis, Rijksuniversiteit Groningen). Retrieved from http://www.ppsw.rug. nl/~kiers/ReportZijlstra.pdf

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Analyze the argument from Part I. Identify and label the logical fallacies used in the argument. Be specific, using and citing sources to support your definitions of each fallacy.

Assignment Details

This Assignment has two parts. In Part I, you will test your understanding of fallacies by creating an argument with fallacies in it. In Part II, you will identify the fallacies and explain how they operate.

Part I:

Consider a scenario where someone is using errors in argument. For example, the argument may focus on errors that customers or colleagues make in your field of study. It could be how the public perceives your field, how a newcomer to your field may make errors, or addressing a controversial topic in your field. If you would like to write about your life at home instead of at work, this may be the sort of argument that teenagers give to their parents during a disagreement. It could be a disagreement you have had with friends or family members. You get to choose the situation.

The Assignment should be 1–2 pages and should be composed in first person as the person making the errors. You should be deliberately employing at least four logical fallacies in the argument. The person making the argument likely does not know they are making mistakes. You will be correcting these errors in Part II. However, in Part I, have fun showing how errors in argument make their way into daily life. Some may be exaggerated, but some may be subtle.

In the sections of the paper where you are using a logical fallacy, you should highlight the error in bold. This will demonstrate that the fallacies are deliberately used in your composition.

While the paper will have errors in argument, aim not to have errors in spelling or grammar. The work should still be composed to demonstrate college-level writing, clarity, and organization.

Part II:

The content for Part II should be at least 2 pages, and it will require a reference page, which is not included in the page requirement.

In Part II, you will analyze the purposely flawed argument that you created in Part I. You will take on the role of a leader in your profession or of someone offering guidance to help a friend or family member understand their errors.

In the scenario for Part II, you have been asked to address the errors with the person voicing the argument in Part I. You should not be harsh with the person who made the mistakes, but you will use leadership, knowledge, and compassion to help make corrections. Offer diplomatic guidance that encourages without chastising the subject. Use your critical thinking and analytical skills to evaluate the Part I arguments, explain how they should be corrected, and offer guidance for improved argumentation in the future.

  1. Analyze the argument from Part I. Identify and label the logical fallacies used in the argument. Be specific, using and citing sources to support your definitions of each fallacy.
  2. Using clear argumentation, explain the implications of those fallacies in the workplace or daily life and why the fallacies would be problematic. What could be a consequence of this reasoning? Why do you believe the person making the argument used these tactics?
  3. Offer guidance: How could the person constructing this argument avoid making those mistakes in argumentation? What would have strengthened each of the claims?

Save Parts I and II in the same document and submit the work to the Dropbox.

Identify the use of ethos, pathos, and logos in the commercial.

You have had a chance to see tools of persuasion in the Three Proofs and to examine visual arguments in methods of persuasion. For this Assignment, you will compose a critical thinking analysis of two television commercials. The paper should be at least 600 words long.

You will select two television or online commercials for the project. If you do not regularly watch television, you can still complete the Assignment by searching YouTube® or individual company websites to find advertisements for products, goods, or services. Choose one commercial that you think is effective and persuasive, and another that you think fails to make a strong argument.

In the same document, you will conduct two analyses. Part I is on the effective and persuasive commercial, and Part II will cover the less persuasive commercial. You should answer the following four sections for each. Answer all of the questions for Part I first, then answer all of the questions for Part II.

  1. First, you will describe what happens in the commercial. Assume that the reader has not seen it. Explain the use of words, images, and sounds. What do you see?
  2. Next, use the premises–conclusion format to offer about three to four premises that lead to what you believe is the conclusion of the commercial. Label each premise and the conclusion. Then explain why you labeled each part as such. What makes each section a reflection of a premise (evidence, reason) or the conclusion?
  3. Identify the use of ethos, pathos, and logos in the commercial. Use and cite the definitions of each term from the reading or your research. Discuss what the commercials have done well or what is lacking.
  4. Finally, offer two suggestions that would strengthen the commercial. What would make the effective commercial even stronger? What would cause this ineffective commercial to become persuasive? Explain why this would make a difference in the outcome for the commercial.

Save Part I and Part II in the same document and submit it to the Dropbox for grading.