schemas

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Assignment #1: Schemas. Visit www.amazon.com and find a product that is of interest to you. Read two conflicting reviews for the product.
1) Describe the schema you might form about the product after reading the first review.
2) Describe the schema you might form about that same product after reading the second review.
3) Do you think that the person who wrote the first review would change his/her opinion if he/she read the second review?
4) What problems can arise when schemas are used?
5) Do you think reviews on sites like Amazon influence peoples perceptions? If so, how?

Provide the URL for the product from which you are obtaining the reviews. Submit your assignment (with your responses and the URL of the reviews) via E-campus by typing your assignment into the Submission textbox (or pasting it from a word document into the textbox. You assignment should be approximately 2-3 pages, typed and double spaced (not including reviews or references).

Randomized Control Trials

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Read Chapters 3 – 10 from Joseph S. Wholey, Harry P. Hatry, and Kathryn E. Newcomer, editors, Handbook of Practical Program Evaluation, (Latest Edition) Prepare a concise written response to the question(s) posed below:

Question:

a. Explain the concept of randomized controlled trials

b. Assess the usefulness of randomized controlled trials in measuring program impact

c. Discuss two ways that bias may be introduced during the implementation of randomized controlled trials

Please use public administration examples to illustrate your understanding of the questions above.

CJUS 745-Discussion Forum3-Reply2

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Reply must be at least 200-300 words. For each thread, you must support your assertions with at least 2 citations from sources such as your textbook, peer-reviewed journal articles, and the Bible. 

Field, A. P. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Los Angeles, CA: Sage.

**Justin***

Correlational Studies: Variables and Research

Correlational studies use statistical data in order to provide more accurate estimates of relationships between variables. These variables include predictor and criterion variables to conduct a more conservative test of the data to more accurately prove or disprove the hypothesis and explain the difference in the empirical results (Becker, et al., 2016). The easiest way to have spotted the correlation between the two variables, in the hypothetical scenario presented, was to view if the variables established covary. According to Field (2018), To understand what covariance is, we first need to think back to the concept of variance that we met in Chapter 1. Remember that the variance of a single variable represents the average amount that the data vary from the mean (p. 251). Therefore, if two variables are related then changes will be reflected with similar changes in the other variables present. This similar reaction will be observed with equal or similar effects regarding the mean within the statistical data (Field, 2018). Correlational research studies involve the measuring of two variables, their relationships, dependent variables, and variables that have no interaction or effect on independent variables. If one variable is known then the study can estimate or predict what will happen with the other variable (Abbott & McKinney, 2013). Correlational research design allows researchers to understand and predict relationships between variables

Correlational Research Design

 Since variables and their relationships are measured in correlation research designs then deviations, if any are present, between the variables must be measured. According to Field (2018), Also, by adding the deviations, we would gain little insight into the relationship between the variables. In the single-variable case, we squared the deviations to eliminate the problem of positive and negative deviations canceling each other out (p. 252). Therefore, one must decide how many variables are involved and if deviations exist. The next step is to overcome the issue of dependence involving a measurement scale (Field, 2018). Field continues to explain that the conversion of the covariance into a standard is called standardization and a critical step in overcoming this research design issue. One may use the standard deviation to conclude on the measurement and convert the variable and by standardizing the covariance within a range of -1 or +1 then the researcher can also validate the variance of the variables (Field, 2018). According to Field (2018), This does not mean that the change in one variable causes the other to change, only that their changes coincide (p. 253). This is considered to be a critical step in the design process due to a calculation difference greater than -1 or +1 meaning that the research has a major flaw and the variables or calculations should be revisited. 

SPSS and Correlation Research

 This author would influence the use of correlation research by using SPSS data systems. The variables can be entered into the SPSS program and make it easier for variables and calculations to be correct and utilized in order to validate the hypothesis and discover the existent or non-existent relationship between the dependent variables. Each variable would be entered into the SPSS program and the bivariate correlations can be calculated. However, bias must be discovered first. According to Field (2018), The two most important ones in this context are linearity and normality (p. 257). SPSS systems can help discover bias and correlations between the two variables. SPSS systems then can help the research by discovering and calculating the confidence intervals by using a bootstrap (Field, 2018). The discovery of the two variables and the utilization of the SPSS system can help this author and co-authors calculate and eliminate bias and influence integrity in the design and conclusions drawn from the research. 

Conclusion

 Correlation research design is critical to the recognition of two or more variables and the effect that one has on the other without the influence of an independent variable. However, the SPSS system helps reduce issues within the research design, eliminate bias, and help reduce the amount of errors that can exist within statistics and their calculations. Truth and clarity is critical not only in professional academic studies but also life. John 7:18 states, Whoever speaks on their own does so to gain personal glory, but he who seeks the glory of the one who sent him is a man of truth; there is nothing false about him (New International Version). The use of the SPSS system can help researchers discover truth in academic studies and promote unbias research, which is critical to the exploration of the truth in the field of criminology. 

References

Abbott, M., & McKinney, J. (2013). Understanding and applying research design. Seatle, WA: Wiley.

Becker, T., Atnic, G., Breaugh, J., Carlson, K., Edwards, J., & Spector, P. (2016). Statistical control in correlational studies: 10 essential reccomendations for organizational researchers. Journal of Organizational Behavior, 157-167.

Field, A. (2018). Discovering statistics using IBM SPSS statistics: North American Edition. London, UK: Sage.

CJUS 745-Discussion Forum3-Reply1

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Reply must be at least 200-300 words. For each thread, you must support your assertions with at least 2 citations from sources such as your textbook, peer-reviewed journal articles, and the Bible. 

Field, A. P. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Los Angeles, CA: Sage.

**PAUL**

In a correlational study, we look at whether or not two variables are related, not necessarily what caused them. Suppose you found a strong correlation between two variables. How would you propose to a colleague that you could investigate if there was a cause and effect? An experimental design would allow the researcher to have the most control over exposure to the treatment which would be the independent variable. Researchers use a correlational research design to measure 2 or more variables to investigate the extent to which the variables are related (Seeram, 2019). The researcher should attempt to understand and assess the statistical relationship between the two variables without any type external or internal influences.   A correlation coefficient which is a mathematical measure of the relation between two data sets would entice further investigative research into a experimental design (Mitchell, 1985). Correlation is useful for describing and establishing the statistical significance of the comovement of two variables (Mellinger & Hanson, 2017). If there is a strong correlation between two variables, I would propose investigating a cause and effect relationship through an experimental design.

In my own law enforcement experience I would relate the investigative finding between the correlation of two variables from two different data sets to writing an affidavit for an arrest warrant. Like a researcher who experiences a correlation, a law enforcement officer has more than reasonable suspicion when he or she collects a fingerprint from a crime scene that matches a suspect. Although a fingerprint match was obtained, further investigative research would be required such as processing a search warrant for the criminal instrument and identifying witnesses. Just like quantitative research, all the evidence needs to flow with the results derived from a totality of evidence involved.            

This would allow the researcher to measure the outcome or the dependent variable to see if there is a cause and effect relationship. Overall, experimental designs have the purpose of identifying this type of relationship.  

            Another example from a law enforcement perspective of correlation is the use of informants. We have to measure the information and test if the information is credible and reliable for the purpose of employing further investigative assets and resources. If an informant provides information, it is our job as investigators to test the information through corroborative efforts.  If the information proves correctly then the informant is deemed reliable and credible. Reliability refers to the consistency, stability, or repeatability of results when a particular measurement procedure or instrument is used (Mellinger & Hanson, 2017).

However, if the information provided is unsubstantiated then we attempt to understand the true intention of the information.  It is possible, for example, to obtain consistent but wrong or misleading measurements.          

From a Christian worldview perspective, aspects of Scripture that are necessarily independent within a naturalistic world view, such as the structure of the Wheel or the correlation between the sixty-six chapters of Isaiah are found to be united by highly correlated word distributions which adds value to the absolute scientific proof of God’s divine design of His Holy Word (Gareiou & Zervas, 2018).