Violence in the community was operationalized from various files.
The interater reliability of low, moderate, or high violence risks showed acceptable significant results as to correspondence. The final risk judgment were not only significantly predictive of community violence after release, but effect size too showed moderate to large results indicating that the study was not only significant but also weighty and therefore its results were worthy of consideration. esearchers therefore suggested that the HC-20 violence risk assessment sample could contribute meaningfully to assessment of violence risk given that certain conditions were in place.

2. Lalumiere and Quinsey (1994) used phallometric assessments in order to assess degree to which rapists could be identified from non-sex offenders, whether rapists respond more to description of rape than to consenting sex, and what are the variables that may allow one of identify rapists.

The population consisted of 415 rapists and 192 non-sex offenders who were examined…...

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References

Douglas, K., Ogloff, J., & Hart, S. (2003) Evaluation of a model of violence risk assessment, Psychiast. Serv. 54, 1372-1379

Lalumiere, M. & Quinsey, V.L. (1994) The discriminability of rapists from non- sex offenders using phallometric measures. Criminal Justice & Behavior, 21, 150-175.

tatistical ignificance in Published cientific Works
The study I chose to examine is from the field of psychology: Todd, Hanko, Galinsky, and Mussweiler (2011), "When Focusing on Differences Leads to imilar Perspectives. This study was recently published in Psychological cience, a high-impact journal in the field. The research has to do with perspective-taking in conversation; the authors hypothesize that people are better at taking another person's perspective if they are in a "difference mindset" -- i.e. are more aware of interpersonal differences than usual. The authors propose to induce this "difference mindset" by a variety of means, across five experiments. Below, I will discuss the use of statistics in their study and how well they conveyed the size and significance of the results.

Experimental Design

All five experiments used a similar design: participants were randomly assigned to a similarity-mindset, difference-mindset, or control condition. Experiment 1 tested perceptual perspective differences, Experiment 2 tested the…...

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Study Conclusions

The authors claim that the five experiments presented show "clear and consistent support" for their hypothesis that the difference mindset improves conversational perspective-taking. Speaking as a statistician, I would be hard pressed to let the strength of this claim pass. The significance of their t- and F-tests varies in strength from p = 0.05 (approaching "marginally significant") to p = 0.001 (a solid result). This indicates that the five experiments in the study have different impacts on their subjects, and may in fact operate by different mechanisms. This suspicion is upheld by the consistently low effect size obtained in each experiment. Overall, I am somewhat surprised by the study's appearance in such a high-impact journal, although the rigor and size of the experiments, literature review, and theoretical support is considerable. In addition, the topic (whether being sensitized to differences affects human interaction) is a very interesting one. I hope to see more research on this topic in future studies that explore and explain the low effect sizes found in this set of experiments.

Todd, A., Hanko, K., Galinsky, A., & Mussweiler, T (2011). When Focusing on Differences Leads to Similar Perspectives. Psychological Science, 22(1): 134-141.

The relationship between the two variables, those being statistical significance and effect size is clear. A study's result may or may not be statistically significant. If it is not, then one need not really go any further. However, if it is statistical significant, the effect size should be calculated to determine whether the statistical significance is of any import. If there is statistical significance but the effect size is less than 0.1, than the statistical significance is not all that extraordinary.

This distinction is important in the studies reviewed during this course because just because one or more studies had statistical significance does nto mean that the significance is all that big a deal. The source for this report covers such an instance where a pre-test and a post-test of an examination are off by a full point (on a scale of 100) and this is deemed to be statistically significant…...

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References

MEERA. (2013, July 7). Power Analysis, Statistical Significance, & Effect Size | My Environmental Education Evaluation Resource Assistant. Welcome to MEERA | My Environmental Education Evaluation Resource Assistant. Retrieved July 7, 2013, from http://meera.snre.umich.edu/plan-an-evaluation/related-topics/power-analysis-statistical-significance-effect-size

Statistical Significance and Meaningfulness
Scenario

Critically evaluate the sample size

To begin with, sample size is very significant. In this particular scenario, the sample size is much more 30, and therefore considered to be a large sample size. Owing to the large sample size, it is expected that the sampling error will be small. And, a small sampling error results in a significant test, in most cases. This is similar to the results proclaimed by the statements.

Critically evaluate the statements for meaningfulness

In assessing the statements for statistical significance it can be said that the statistical p value ought to be employed solely to gauge as to whether the null hypothesis has been rejected, and not whether any other hypotheses such as H1 and H2 have been accepted. Therefore, the statements are meaningful (Page, 2014).

Critically evaluate the statements for statistical significance

The statements are statistically significant. This is because as denoted, the obtained p value…...

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References

Dahiru, T. (2008). P-value, a true test of statistical significance? A cautionary note. Annals of Ibadan postgraduate medicine, 6(1), 21-26.

Page, P. (2014). Beyond statistical significance: clinical interpretation of rehabilitation research literature. International journal of sports physical therapy, 9(5), 726.

    Meaningfulness refers to the practical, real world application of a statistic.  If a statistically significant correlation between variables, for instance, has meaningfulness that correlation says something to the real world and understanding that correlation can have an impact on how people adjust to the situation from here on out.  Therefore, while statistical significance is helpful, it is not the end-all-be-all for research:  a research finding has to be meaningful for it to have importance.  It has to have some sort of impact in the real world for it to be meaningful.     For the researchers who stated that “given this research was exploratory in nature, traditional levels of significance to reject the null hypotheses were relaxed to the .10 level,” I would first suggest that statistical significance is not something that necessarily has to be considered of paramount importance in exploratory research.  Statistical significance is more appropriate for testing a…...

Statistically Significant Results
One of the most important aspects of research findings is to ensure that the results are significant or meaningful in order to influence an appropriate course of action. In this case, the significance of the findings of a study can be described as the quality of being important. Therefore, the efforts carried out by a researcher or group of researchers should be geared towards ensuring the process generates important findings. However, there have been numerous concerns regarding the statistical significance and/or practical significance of the findings of a study. These concerns are attributed to the fact that the findings of a study can be statistically significant but not meaningful.

Statistically significant findings of a study basically predict the probability that a relationship observed in the data was a by-product of chance and the likelihood that the variables are actually unrelated. In this case, the research tests attempt to generate…...

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Works Cited

"Statistical Significance and Clinical Importance." Significance vs. Importance. Faculty of Medicine - University of Ottawa, n.d. Web. 28 Feb. 2015. .

"Tests of Statistical Significance." PPA 696 RESEARCH METHODS. California State University Long Beach, n.d. Web. 28 Feb. 2015. .

In other words, p values correspond to statistical significance, while NNT corresponds to clinical significance. In clinical trials, statistical validity reflects the theoretical basis of the study, with hypotheses being formulated and quantified in terms of likelihood. Clinical significance is concerned with the practical outcome of trials, and with the results of actual treatment and how this relates to the hypotheses that are proven or void.
2.

In nursing practice, both statistical and clinical significance play an important role in research. In practice, however, it is clinical significance that should have the greatest impact upon nursing practice. Clinical significance provides actual data from research conducted to determine such effects. It concerns the outcome of trials, while statistical significance is more concerned with determining new research and the likelihood of success before trials have been conducted.

Indeed, Davidson notes that an advantage of NNT is the format of its results -- resulting from…...

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References

Davidson, Richard a. (1994) Does it Work or Not?: Clinical vs. Statistical Significance. Chest, Vol. 106, No. 3. Retrieved from http://chestjournal.chestpubs.org/content/106/3/932.long

Kain, Z.N. (2005, Nov.) the Legend of the P. Value. Anesthesia & Analgesia, Vol. 101, No. 5. Retrieved from  http://www.anesthesia-analgesia.org/content/101/5/1454.full

This analysis was deemed necessary as differences can exist on the basis of an overall group effect and not an individual variable (question) effect and visa versa.
Male Mean Per Question Analysis x Department

Source of Sum of d.f.

Mean

Variation

Squares between error

Required F. value: 4.21 ? < 0.05

Concluding Statement: With a received F. value of 1.04 and a required value of F = 4.21 the conclusion can be drawn that no statistically significant differences exist in attitude toward Career Choice, Professional Relationships and Development for male radiographer participants within the three health care departments at a probability level of 95%. Therefore, all three health care groups consisting of males perceive attitudes towards Career Choice, Professional Relationships and Development the same.

Female Mean Per Question Analysis x Department

Source of Sum of d.f.

Mean

Variation

Squares between error

Required F. Value: 4.21 ? < 0.05

Concluding Statement: With a received F. value of 0.1757 and a required value of F…...

statistical analyses used.
List the statistical procedures used to describe the sample.

Power analysis was used in order to discover power of effect. The power was set to 0.8 with a significant level of 0.05. Differences between the intervention and the control group were tested with Pearson's Chi. A t-test was used for the other perimeters. ANCOVA was used for testing changes between the two groups over time.

Was the level of significance or alpha identified? If so, indicate the level (.05, .01, or .001).

The level of significance was described. The power of the effect size was set to 0.8 with a significant level of 0.05. 0.05 was used throughout as perimeter of significance.

Complete the table below with the analysis techniques conducted in the study:

Identify the purpose (description, relationships, or differences) of each analysis technique.

Power analyses was used in order discover whether the test actually did discover a difference that did exist,…...

Independent – Samples t-Test The Independent sample t-test is among the group of inferential statistical test. This test seeks to compare the means of two unrelated samples in a survey. The t-test determines whether the mean of the two sample is significantly different statistically (Paul & Garg, 2014). According to Ruane (2005) for this test to be undertaken, both samples must have scores with one carrying nominal scale and another one a numerical scale. The sample carrying the nominal is a grouping variable and is the independent variable. The sample with the numerical scale is a test variable and is the dependent variable (Ruane, 2005).
This paper seeks to determine the statistical significance between the means of work shift (day and night shift) and the mean of the number of widgets produced in either shifts. This will be undertaken by use of the independent sample t-test. The work shift is the independent…...

health care centers (PHCC) in Stockholm County, 40 of them were randomly selected using an old-fashioned, non-probability method of basically drawing names from a hat. The author notes, "every PHCC was given a unique number that was written on a paper card and placed in a pot. For transparency, two colleagues independently drew 20 paper cards each, a total of 40." Of these 40, one declined to participate. Therefore, 39 PHCCs were selected, and one nurse from each PHCC served as contact person. The sample size is adequate and actually fairly large for the study. Although unconventional, bias was not introduced by using this method of sample selection, and the sample can be considered representative of the population given the randomness of the PHCC selection procedure. Eligibility criteria are also clearly identified, as the contact person nurse needed to comply with the study design, namely to distribute anonymous questionnaires…...

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References

Sundborg, E.M., Saleh-Stattin, N., Wandell, P. & Tornkvist, L. (2012). Nurses' preparedness to care for women exposed to Intimate Partner Violence: a quantitative study in primary health care. BMC Nursing 11(1). Retrieved online:  http://bmcnurs.biomedcentral.com/articles/10.1186/1472-6955-11-1

60.2% of white inmates were executed that had initially been removed from being under a sentence of death versus 38.9% of blacks that met these criteria as part of the sample. While this cross-tabulation shows there is not a statistically significant relationship between blacks vs. whites having their sentences commuted, Hispanic Origin will prove to have statistical significance when bivariate correlation analysis is used to analyze the data. Additional research into the strength of relationships between those receiving commuted sentences and race need to be completed however, before a statistically significant conclusion can be reached across all ethnic groups in the sample. More in-depth analysis techniques including factor analysis to either accept or refute these findings as they pertain to this data set.
The third analysis is based on bivariate correlation matrices using both Pearson's correlation coefficient for parametrically-based analysis and Kendall's Tau_b and Spearman's ho for non-parametrically based analysis.…...

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References

Mark Douglas Cunningham, Jon R. Sorensen. (2007). Capital Offenders in Texas Prisons: Rates, Correlates, and an Actuarial Analysis of Violent Misconduct. Law and Human Behavior, 31(6), 553-71. Retrieved December 18, 2008, from ABI/INFORM Global database. (Document ID: 1385818841).

Stewart JH McCann (2008). Societal Threat, Authoritarianism, Conservatism, and U.S. State Death Penalty Sentencing (1977-2004). Journal of Personality and Social Psychology, 94(5), 913. Retrieved December 19, 2008, from ABI/INFORM Global database. (Document ID: 1481555391).

Mark Peffley, Jon Hurwitz. (2007). Persuasion and Resistance: Race and the Death Penalty in America. American Journal of Political Science, 51(4), 996-1002. Retrieved December 15, 2008, from ABI/INFORM Global database. (Document ID: 1353540041). (U.S. Department of Justice, 2008)

United States Department of Justice. Bureau of Justice Statistics. Capital Punishment in the United States, 1973-1993 [Computer file]. ICPSR06512-v2. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2008-11-12. doi:10.3886/ICPSR06512. Retrieved December 18, 2008, at  http://www.icpsr.umich.edu/cocoon/NACJD/STUDY/06512.xml

Criminological Theory and Statistical Data
Introduction

Criminological theory is not always based on evidence—that is, on statistical evidence. Sometimes it is based on ideas that seem logical at the time. Theorists will notice correlations in the ways in which crime emerges in certain communities and they will base their theories of crime on these observances, though no statistical evidence is actually accumulated to verify the theory. The theory simply makes sense from a logical or rational point of view and in this manner it can be promoted. Its basis of evidence is qualitative (i.e., content-related, conceptual or thematic) rather than statistical and empirical (i.e., data that can be measured, quantified and verified through testing). Broken Windows Theory is one example of criminological theory that was based on qualitative assessments rather than on statistical data (Jean, 2008). While the theory has been embraced over the years since it was first developed, other researchers…...

¶ … ANOVA) can best be described as statistical analysis of data with a variety of different variables and variations and is oftentimes used in a medical and professional research and practices environment. Using ANOVA in research is a commonplace technique, but there are some disadvantages to this usage; the application of the analysis is often conducted in applications where it is inappropriate (Patel, Naik, Patel, 2015). Ensuring that ANOVA is used in the appropriate manner, and that it is applied in situations that call out for ANOVA requires understanding and comprehension exactly what ANOVA does and how it is used in research. The Patel (2015) concluded that "conceptual understanding and application of ANOVA and Post hoc test was lacking among bio-medical research" (p. 117). Developing the required understanding will assist the researcher(s) in implementing data analysis that will create a more valid and reliable study, along with results that…...

Without knowing what two scenarios you selected, we cannot help you specifically evaluate the sample size, evaluate the statements for meaningfulness, critically evaluate the statements for statistical significance, or provide an explanation of the implications for social change.  We can, however, provide information to you about how you can make those evaluations.

Understanding how statistics work, especially in the context of science and social science research, is very important.  That is because you can have studies that seemingly show the same results, but actually contain very different information.  One important component of any type of statistic presented is....

Sure! Here is a sample research proposal and outline for a college paper on educational reform:

Research Proposal: Exploring the Impact of Educational Reform on Student Achievement and Equity

I. Introduction
A. Background of the study
1. Briefly discuss the current state of the education system
2. Highlight the need for educational reform
B. Problem statement
1. Identify the key problems within the system that require reform
2. Discuss the negative consequences of these problems on student achievement and equity
C. Research question
- How does educational reform impact student achievement and promote equity in the education system?
D. Objectives
1. Investigate....

## Strategic Sampling Design for Supply Chain Optimization

Strategic sampling design plays a crucial role in enhancing the efficiency and performance of supply chain management. By carefully selecting a subset of data points or items for inspection, organizations can gather meaningful insights while minimizing resources and maximizing decision-making accuracy. Here's how strategic sampling design contributes to supply chain optimization:

### 1. Quality Control and Inspection:

Strategic sampling allows organizations to inspect a limited number of products or components to assess their quality. By focusing on specific characteristics or attributes, such as defects, dimensions, or composition, inspectors can identify potential issues with the manufacturing....

1. The genre of a thesis significantly impacts the relevance of Barthes authorial intent, as different genres prioritize different aspects of writing, such as research, argumentation, or creative expression.

2. In a scientific thesis, Barthes authorial intent may be less relevant, as the focus is primarily on presenting empirical data and logical analysis rather than the subjective interpretation of the author.

3. On the other hand, in a literature thesis, Barthes authorial intent may hold greater significance, as the analysis of the authors intentions and the texts meaning are central to the study of literature.

4. In....