Contemporary Research

Contemporary Research

Context:

Over the past 40 years, the topic of bullying has received increased attention within the psychological literature, and there is growing awareness of the long-term impact on bullying on psychological well-being for the individuals who have been bullied (Copeland, Wolke, Angold, & Costello, 2013). Specifically, there is an association between being the victim of bullying and poorer social adjustment and school achievement, heightened risk of substance use, depression, anxiety and interpersonal difficulties (Hawker &Boulton, 2000; Juvonen& Graham, 2014; Kawabata, Tseng, & Crick, 2014; Nakamoto& Schwartz, 2010).

Research that has examined the coping behaviours of children who are bullied is mixed, suggesting that children who are bullied either use passive, emotionally-oriented, avoidant coping (Hansen, Steenberg, Palic, &Elklit, 2012), or use ineffective problem-focused coping behaviours (Tenenbaum, Varjas, Meyers, & Parris, 2011). Contemporary research is examining how cognitive coping styles, such as rumination, catastrophising, and positive reappraisal may influence the link between bullying and psychological well-being (Garnefski&Kraaij, 2014). The present study aims to examine the relationships between bullying, depression, and rumination.

In this assignment you are required to create a data file for the provided data, and produce a lab report that includes the following sections:

Title and Abstract

Aim to produce a catchy yet professional title for your paper. Your abstract should be approximately 150 words in length (not included in your word limit) and should provide a succinct summary of the report overall (i.e. all sections should be represented in the Abstract).

Introduction

The Introduction should concisely summarise the literature you have reviewed on the topic, providing the reader with a background and the existing research evidence. When preparing your report, bear in mind that you will need to report on your assumption testing and major analyses (in later sections), so there is a need for you to write concisely in this section. One way to do this is to synthesise the results of several studies together rather than reporting on individual studies.

Your research hypothesis should also be included in your Introduction. The hypothesis should be concise and logically related to the literature you have included in your Introduction. Your goal is to write a hypothesis that is clear, logical, testable – and if the reviewed literature supports it – directional. In total, your Introduction should be no longer than 300-350 words.

Method

This section outlines the details of participants, the measures used, the procedures for recruitment and data collection, and the proposed data analysis. Please use the subheadings: Participants, Measures, Procedure, and Data Analysis. Use the following scenario information to assist you with the Method section.

Stella is an early-career researcher who has recently received grant funding to investigate bullying in high school and how it is related to adolescent depression. She is particularly interested in how the cognitive coping strategy of rumination may interact with bully vicitimisation and depression. Stella has received ethics approval for the study and has been asked to briefly introduce the project at a staff meeting. She provides the following information:

“We are working with four local high schools in metropolitan Melbourne, to survey students. Details of the study will be included in school newsletters with a participant information sheet and consent form sent home with all students in Grades 7-10. Parents who consent for their child to take part return the consent form and indicate an email address through which their child can be reached. Adolescents are then emailed a participant information sheet and a link to the online survey. It is entirely up to the adolescent whether they take part after their parent has consented, they do not have to just because their parent has consented. The online survey will be anonymous, with no identifying information collected from the adolescent. 

To be eligible to take part, the adolescent needs to be aged between 12-15 years, regularly attending school, and fluent in English as the survey is only available in English. The survey consists of some demographic items, the Beck Depression Inventory-II (Beck, 1996), the Victimisation Scale (Orpinas, 1993), and rumination subscale of the Cognitive Emotion Regulation Questionnaire (CERQ, Garnefski, Kraaij, &Spinhoven, 2002). It is estimated the survey will take 15 minutes to complete and can be completed on any device that connects to the internet. 

Results

In this section, you will need to report on the data screening and assumption testing that you perform, and the main analysis – moderation. It is anticipated that the details of the assumption testing will be concise, yet provide enough detail so your Instructor can understand the process you have undertaken and the analytic decisions you have made. It is also recommended that some analysis that let the reader know about your variables of interest and the relationship between them be included.
Discussion

In this section it is expected that you will interpret your results by restating them in layperson’s terms and link them back to the literature you included in your Introduction. Make sure you highlight what your findings add to the literature and where there might be points of difference (if any). You also need to include some comment on the strengths and limitations of the study, suggestions for future research, and consideration of the findings in a practice/real-world context.  

References

Needs to include all cited sources in APA 6th-edition format. 

Assignment Steps

Step 1 – Conduct a literature search:

You are to assume the role of researcher to conduct a literature review that will help you understand the relationships between the key variables and generate a hypothesis that aligns with the proposed analyses.

Step 2 – Generate your hypothesis:

Based on your literature review and the analyses you are required to perform you will need to generate a research hypothesis. Your hypothesis should be logically related to the literature you have reviewed in your introduction. Do not be concerned if your hypothesis is different from other students. You will all have included different sources in your Introduction, and therefore you might hypothesise different things. The main things to remember are that your hypothesis is logical, clear, and testable. 

Step 3 – Download your data and enter it into SPSS: 

The document containing the data you need to enter can be found in the PSY4401 datasets folder, located in the Before You Begin section of the subject. The document is named PSY4401 TP5 2017 Lab Report Data.pdf. Please note that this is not a datafile, but rather a pdf document containing the data that you need to enter into SPSS.  Please enter the data exactly as it is contained in this document. 

Step 4 – Caress the data:

Once you have the data entered into SPSS, your task is to check it for accuracy and missing data, and conduct the assumption tests that underpin the required analyses. You are encouraged to use your Hills (2011) textbook and review the videos/activities in the relevant modules to assist with your analyses.

You will need to report in the results section of your lab report any actions that you took/decisions you made throughout the data cleaning and assumption testing stage. As these are not the main results of the study they should be presented briefly, but in enough detail so that your instructor knows what changes you made (if any) and how you dealt with problems that arose (e.g. violations of assumptions – if any!). 

Step 5 – Run the required analyses and interpret your results:

It is anticipated that you will have a hypothesis that is assessed using a moderation analysis. 

Step 6 – Write APA-formatted sections representative of a research paper:

Your report needs to contain an Abstract, Introduction, Method, Results, and Discussion section, followed by a list of references. Further details regarding what to include in each section is detailed above. 

Step 7 – Provide appendices of various SPSS material:

You will be required to create two appendices. Appendix A will contain screen shots of your data file (variable view and data view) that will enable your instructor to assess your data entry and review the changes you make to the data as part of Step 4.  Appendix B will contain a copy of all syntax and output generated by SPSS for the analyses that you have performed. You can copy and paste syntax and output from your output file directly into word or request that SPSS convert it to a word document using the export function in the file commands. 

Solution 

Abstract

Bullying in schools is a social concern that has both education and legal implications to include death. It is exacerbated by the fact that it can occur anywhere. Anti-bullying programs help to address this concern by presenting a proactive approach for allowing both victims and perpetrators to address the vice. Still, the possibility of correlating bullying with gender and age offers an opportunity for improving effectiveness of anti-bullying programs by facilitating targeted tactics. The current research engages 20 youths of both male and female gender who are aged between 14 and 24 years in exploring this correlation. Questionnaires are used to collect primary data. The results of data analysis using SPSS software show that bullying could be correlated with gender and age. As such, it is recommended that additional research be conducted with a larger sample to ascertain whether a correlation exists and the nature of that relationship.

Correlation between Bullying, Age and Gender

Introduction

Bullying in schools is an issue of concern to both the education and legal fraternities. This is because it is a common issue that affects students as a social problem. In fact, it is a social problem that must be addressed and prevented as part of the care provided to students. Bullying takes different forms, which could include both mental and physical approaches with the commonality being its abusive nature that could have both long-term and short-term effects on the victims to include death, suicide, psychological harm, and physical injury. The most distressing aspect of bullying is the fact that it has no classical signs with most cases being ignored because they are not noticed (Antoniadou, Kokkinos &Markos, 2016). Additionally, bullying can occur in any location to include the playing field, classrooms, cafeteria, and hallway. The fact is that bullying is an acknowledged problem with the current anti-bullying programs doing very little to address the issue (Garnefski&Kraaij, 2014). As such, there is a need to characterize bullying so that anti-bullying programs are tailored for best results to justify cost and resources allocation.

Research reveals that bullying is brought about by an overexpression of the survival instinct that overrides compassion. In this case, the bully feels the driving need to better his or her peers. Usually, the victims have some peculiarity that differentiates them from others to include disability and ethnicity. Although bullying typically begins with verbal abuse, it will escalate to physical abuse and violence even as the bully seeks satisfaction. This may take the form of threats, force, rumors, and lies (Schwartz et al., 2015). The underlying concern is that bullying has a negative effect on the victims. This is because it lowers their performance with some of the victims either changing schools or dropping out of school after being victimized. In addition, there are those who develop depression symptoms, chronic ailments, run away, and commit suicide (Juvonen& Graham, 2014). Based on this awareness, there is a need to identify the unique demographic features associated with bullying with a view to develop targeted anti-bullying programs that will achieve the best results. To facilitate the development of effective anti-bullying programs, this study intends to evaluate the hypothesis that; bullying is correlated with gender and age for youths between 14 and 24 years of age.

Method

This study has applied a quantitative research approach that involved the use of a pre-structured closed-ended questionnaires to collect primary data while the secondary data will be collected from peer-reviewed articles and books relating to the research topic. In this case, the first step was to design an ordinal-polytomous questionnaire in which respondents were asked to answer four questions that included providing a first name, gender, age, and house number. These questions were designed to acquire a better understanding of demographic features associated with victims of bullying and could be answered within five minutes. The study population was then identified along with the inclusion criteria. The inclusion criteria was individuals between 14 and 25 years of age who were currently victims of bullying. The implication is that persons who were outside the required age group or who were not victims of bullying were excluded from the study (Babbie, 2016).

Next, a sample group of 20 individuals was selected, educated on what the research entailed then asked to fill questionnaires. This was based on a need for convenience, cost and time. All the participants were from the same locality and could easily be approached by the researcher. Additionally, preparing the questionnaires, recording data and conducting data analysis was not costly for this small group. Besides that, collecting data from the respondents would take very little time. Consent was sought from parents and guardians for all participants below 18 years of age. All participants older than 17 years of age were asked to sign a consent form before inclusion in the study. Also, the questionnaires were delivered to the participants, collected after completion and results tabulated in a codebook as presented in Appendix A. Finally, the data was subjected to SPSS software analysis that identified existing trends, confidence interval, and correlations between the variables. The statistics tests focused on two-tailed significance since a two-tailed test determines the possibility of a relationship regardless of its direction whereas a one-tailed test would only be interested in the relationship in one direction, while ignoring the relationship in the untested direction (Whitley, Kite & Adams, 2012).

Results

20 participants completed the questionnaires of which 50% (10/20) were of the male gender. Additionally, the mean age for the participants was 18.95 years (see Figures 1 and 2).

Figure 1. Bar graph of gender

Figure 2. Histogram of age distribution

Next, the confidence interval was calculated at 95% using Formula 1 for gender and age with the results presented in Table 1. Based on the calculated, it can be estimated with 95% confidence that 28% to 72% of bullying victims are males with the estimate based on a sample of 20 participants. In addition, it can be estimated with 95% confidence that as much as 27% of bullying victims are 14 years of age while as much as 27% are 24 years of age based on a sample of 20 participants include 14 to 24 year old individuals.

Formula 1

Where

CI – Confidence interval

Ps – Valid percentage

Z – 1.96

N – Number of participants

Pu – 0.5 at 95% CI

Table 1. Summary of confidence intervals

SPSS Variable Name Sample Statistic () N 95% Confidence Interval
Gender (male) 0.5 20 28% to 72%

(0.5  0.22)

Gender (female) 0.5 20 28% to 72%

(0.5  0.22)

Age (14) 0.05 20 -17% to 27%

(0.05  0.22)

Age (16) 0.1 20 -12% to 32%

(0.1  0.22)

Age (17) 0.15 20 -7% to 37%

(0.15  0.22)

Age (18) 0.2 20 -2% to 42%

(0.2  0.22)

Age (19) 0.1 20 -12% to 32%

(0.1  0.22)

Age (20) 0.1 20 -12% to 32%

(0.1  0.22)

Age (21) 0.15 20 -7% to 37%

(0.15  0.22)

Age (22) 0.05 20 -17% to 27%

(0.05  0.22)

Age (23) 0.05 20 -17% to 27%

(0.05  0.22)

Age (24) 0.05 20 -17% to 27%

(0.05  0.22)

The questionnaire data was subjected to a Pearson correlation with the value calculated as 0.302 while p-value was calculated as 0.195 (see Table 2). The calculated values imply that there is a positive correlation between the gender and age for bullying victims. At p-value of 0.195 (p> 0.05), we acknowledge that the Pearson correlation test result is not statistically significant. That is to say that although there may be a relationship between gender and age for bullying victims, it is not a statistically significant relationship. As such, we do not reject the hypothesis that bullying is correlated with gender and age for youths between 14 and 24 years of age since there is no evidence to direct a conclusion.

Table 2. Pearson correlation results

Gender Age
Gender Pearson Correlation 1 .302
Sig. (2-tailed) .195
N 20 20
Age Pearson Correlation .302 1
Sig. (2-tailed) .195
N 20 20

Discussion

The present study sought to determine whether bullying is correlated with gender and age for youths between 14 and 24 years of age. The results show that although there is equal distribution of participants between the male and female genders, and ages between 14 and 24 years, it was unclear whether there was a correlation between bullying, age and gender. These results are understandable since the study used a small sample size of 20 participants to imply that it was not robust enough to produce valuable results (Monette, Sullivan &DeJong, 2013).

There are a range of anti-bullying programs that can be implemented to reduce the incidence of bullying among youths. Some of these programs are focused on improving awareness and allowing both the victims and perpetrators to seek professional help while others promote good character and values. This allows the community members to understand that bullying is wrong and unacceptable. In essence, anti-bullying programs are focused on vilifying the vice without victimizing either the perpetrators or the victims by recognizing that they both require psychological help (Scott et al., 2016). Also, some programs have improved community awareness, security and monitoring. By increasing online awareness, security and surveillance, bullies are denied the opportunity to corner their victims (Notar, Padgett &Roden, 2013). A more recent measure has entailed reforming policing efforts by adopting cost-effective approaches. This has largely occurred by engaging the local community members in prevailing against the risk factors before they can lead to bullying. This occurs through two principal activities. Firstly, the reforms cultivate and distribute resources, tools, and information concerning effective bullying prevention strategies. Secondly, they provide support for evidence-based intervention strategies (Guan et al., 2016).

As a result of the programs initiated to address bullying occurrence, some benefits have been realized. It is notable that intervention efforts have been repositioned towards evidence-based intercessions with more stringent reporting and agreements, to include community-based efforts. This has ensured that the preeminent existing and accessible substantiation is deliberated in the resolution concerning whether or not to advance and implement a policy designed to reduce or prevent bullying thereby ensuring proper use of resources (Waggoner, 2013). In addition, the programs have resulted in an overall reduction in bullying rates, even as the community becomes more involved in preventing and identifying cyber bullying (Donegan, 2012). Overall, it must be accepted that bullying is a tragic reality that must be addressed with immediacy and as a priority with the focus being on applying and justifying anti-bullying programs so that the possibility of demographic correlations must be explored for effective use of resources in targeted programs.

It must be acknowledged that bullying is a concept that adds to the youth. That is because it makes use of environmental amenities that include online presence and school facilities to expedite intimidation that goes beyond physical interaction such that victims do not have to be within sight of the perpetrators. This includes targeted embarrassment, humiliation, harassment, threats, and torments using interactive communication environments. Also, one must accept that the statistics associated with bullying show that it is an extensive problem since as much as 45% of all students are victims. Sadly, the statistics make it evident that the issue of bullying is worsening (The Diana Award, 2017).

Following that, there is a need for greater focus on improving awareness about the issue, offering help to both victims and perpetrators, and engaging local community members to develop localized solutions. This awareness supports the claim made by this research that the diversity and uniqueness associated with bullying requires that an innovative solution be provided to realize meaningful change. In fact, it brings attention to the possible correlation between demographics and bullying, thereby offering an opportunity for developing tailored anti-bullying programs that would produce the best results while making efficient use of resource that include personnel, funding, and materials. Still, the inconclusive nature of the results bring attention to the need for additional research studies that would engage larger populations. The results from engaging larger populations would ascertain the true nature of the relation between age, gender and bullying. In summary, it can be accepted that bullying could be correlated with gender and age for youths between 14 and 24 years of age since the present results did not direct a conclusion. Additional research is necessary with a larger sample group to ascertain whether a correlation exists and the nature of that relationship.

References

Antoniadou, N., Kokkinos, C. &Markos, A. (2016).Possible Common Correlates Between Bullying And Cyber-Bullying Among Adolescents.PsicologiaEducativa, 22(1), 27-38.

Babbie, E. (2016). The Basics of Social Research (7thed.). Boston, MA: Cengage Learning.

Donegan, R. (2012). Bullying and Cyber bullying: History, statistics, law, prevention and analysis. The Elon Journal of Undergraduate Research in Communications,3(1), 33-42.

Garnefski, N., &Kraaij, V. (2014). Bully victimization and emotional problems in adolescents: Moderation by specific cognitive coping strategies. Journal of Adolescence, 37, 1153-1160.

Guan, C., Kanagasundram, S., Ann, Y., Hui, T. &Mun, T. (2016). Cyber Bullying – A New Social Menace. ASEAN Journal of Psychiatry,17(1), 104-115.

Juvonen, J., & Graham, S. (2014). Bullying in schools: The power of bullies and the plight of victims. Annual Review of Psychology, 65, 159-185.

Monette, D., Sullivan, T. &DeJong, C. (2013).Applied Social Research: A tool for the human services. Boston, MA: Cengage Learning.

Notar, C., Padgett, S. &Roden, J. (2013). Cyber bullying: Resources for Intervention and Prevention. Universal Journal of Educational Research, 1(3), 133-145.

Schwartz, D., Lansford, J. E., Dodge, K. A., Pettit, G. S., & Bates, J. E. (2015). Peer victimization during middle childhood as a lead indicator of internalizing problems and diagnostic outcomes in late adolescence. Journal of Clinical Child and Adolescent Psychology, 44, 393-404.

Scott, E., Dale, J., Russell, R. &Wolke, D. (2016). Young People who are being bullied – do they want general practice support? BMC Family Practice, 17(116), 1-10.

The Diana Award (2017). Facts and Statistics on Bullying and Cyber Bullying. Retrieved from http://www.antibullyingpro.com/blog/2015/4/7/facts-on-bullying

Waggoner, C. (2013). Cyber Bullying: The public school response. Administrative Issues Journal, 3(3), 43-46.

Whitley, B., Kite, M. & Adams, H. (2012).Principles of Research in Behavioral Science (3rd ed.), New York, NY: Routledge.

Appendix A – Data File

Appendix B – SPSS Syntax and Output

Descriptives

Notes
Output Created 07-Oct-2017 21:39:59
Comments
Input Data C:\Users\ SPSS Workshop Student Dataset.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 20
Missing Value Handling Definition of Missing User defined missing values are treated as missing.
Cases Used All non-missing data are used.
Syntax DESCRIPTIVES VARIABLES=Gender Age

/STATISTICS=MEAN STDDEV.

 

Resources Processor Time 00 00:00:00.032
Elapsed Time 00 00:00:00.480
Descriptive Statistics
N Mean Std. Deviation
Gender 20 1.5000 .51299
Age 20 18.95 2.544
Valid N (listwise) 20

FREQUENCIES VARIABLES=Gender Age

/STATISTICS=STDDEV MEAN

/ORDER=ANALYSIS. 

Frequencies

Notes
Output Created 07-Oct-2017 21:41:40
Comments
Input Data C:\Users\ SPSS Workshop Student Dataset.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 20
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics are based on all cases with valid data.
Syntax FREQUENCIES VARIABLES=Gender Age

/STATISTICS=STDDEV MEAN

/ORDER=ANALYSIS.

 

Resources Processor Time 00 00:00:00.000
Elapsed Time 00 00:00:00.055
Statistics
Gender Age
N Valid 20 20
Missing 0 0
Mean 1.5000 18.95
Std. Deviation .51299 2.544

Frequency Table

Gender
Frequency Percent Valid Percent Cumulative Percent
Valid Male 10 50.0 50.0 50.0
Female 10 50.0 50.0 100.0
Total 20 100.0 100.0
Age
Frequency Percent Valid Percent Cumulative Percent
Valid 14 1 5.0 5.0 5.0
16 2 10.0 10.0 15.0
17 3 15.0 15.0 30.0
18 4 20.0 20.0 50.0
19 2 10.0 10.0 60.0
20 2 10.0 10.0 70.0
21 3 15.0 15.0 85.0
22 1 5.0 5.0 90.0
23 1 5.0 5.0 95.0
24 1 5.0 5.0 100.0
Total 20 100.0 100.0

CORRELATIONS

/VARIABLES=Gender Age

/PRINT=TWOTAIL NOSIG

/MISSING=PAIRWISE. 

Correlations

Notes
Output Created 07-Oct-2017 21:48:18
Comments
Input Data C:\Users\SPSS Workshop Student Dataset.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 20
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics for each pair of variables are based on all the cases with valid data for that pair.
Syntax CORRELATIONS

/VARIABLES=Gender Age

/PRINT=TWOTAIL NOSIG

/MISSING=PAIRWISE.

 

Resources Processor Time 00 00:00:00.015
Elapsed Time 00 00:00:00.010

[DataSet1] C:\Users\SPSS Workshop Student Dataset.sav

Correlations
Gender Age
Gender Pearson Correlation 1 .302
Sig. (2-tailed) .195
N 20 20
Age Pearson Correlation .302 1
Sig. (2-tailed) .195
N 20 20

GGRAPH

/GRAPHDATASET NAME=”graphdataset”

VARIABLES=Gender[LEVEL=nominal]

MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=VIZTEMPLATE(NAME=”Bar of Counts”[LOCATION=LOCAL]

MAPPING( “categories”=”Gender”[DATASET=”graphdataset”] “Summary”=”count”))

VIZSTYLESHEET=”Traditional”[LOCATION=LOCAL]

LABEL=’Bar of Counts: Gender’

DEFAULTTEMPLATE=NO. 

Graph

Notes
Output Created 07-Oct-2017 23:02:41
Comments
Input Data C:\Users\SPSS Workshop Student Dataset.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 20
Syntax GGRAPH

/GRAPHDATASET NAME=”graphdataset”

VARIABLES=Gender[LEVEL=nominal]

MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=VIZTEMPLATE(NAME=”Bar of Counts”[LOCATION=LOCAL]

MAPPING( “categories”=”Gender”[DATASET=”graphdataset”] “Summary”=”count”))

VIZSTYLESHEET=”Traditional”[LOCATION=LOCAL]

LABEL=’Bar of Counts: Gender’

DEFAULTTEMPLATE=NO.

 

Resources Processor Time 00 00:00:00.156
Elapsed Time 00 00:00:00.301

GRAPH

/GRAPHDATASET NAME=”graphdataset”

VARIABLES=Age[LEVEL=ratio]

MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=VIZTEMPLATE(NAME=”Histogram with Normal Distribution”[LOCATION=LOCAL]

MAPPING( “x”=”Age”[DATASET=”graphdataset”]))

VIZSTYLESHEET=”Traditional”[LOCATION=LOCAL]

LABEL=’Histogram with Normal Distribution: Age’

DEFAULTTEMPLATE=NO. 

Graph

Notes
Output Created 07-Oct-2017 23:03:04
Comments
Input Data C:\Users\SPSS Workshop Student Dataset.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 20
Syntax GGRAPH

/GRAPHDATASET NAME=”graphdataset”

VARIABLES=Age[LEVEL=ratio]

MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=VIZTEMPLATE(NAME=”Histogram with Normal Distribution”[LOCATION=LOCAL]

MAPPING( “x”=”Age”[DATASET=”graphdataset”]))

VIZSTYLESHEET=”Traditional”[LOCATION=LOCAL]

LABEL=’Histogram with Normal Distribution: Age’

DEFAULTTEMPLATE=NO.

 

Resources Processor Time 00 00:00:00.172
Elapsed Time 00 00:00:00.305