CLASSMATE #-1—-K. P.I decided to use my previous research question for this week’s discussion: “If elementary aged children watch or play violent video games for at least 30 minutes every day, i

CLASSMATE #-1—-K. P.

I decided to use my previous research question for this week’s discussion: “If elementary aged children watch or play violent video games for at least 30 minutes every day, is there an increase in their violence at school?”. For this type of case study, researchers would want to utilize the T-tests because they would need to compare the mean of the two variables, watching or playing violent and increased violence in school. Also, researchers would need to compare the child’s behaviors before playing the violent video games and after playing the violent video games. I feel that this is the correct type of test because my study is comparing and observing the differences between two different means. A null hypothesis can be defined as having no statistical significance (Sukal, 2013). An alternative hypothesis can be defined as having statistical significance between the two variables (Sukal, 2013).

     There are some potential issues or errors that could arise in my concept for a study of violent behavior. As someone mentioned in our previous week’s discussion, violence can be difficult to define because it can vary depending on the person and personality of the individual. Some people may view cussing as being verbally violent but others may not. In the study, there would need to be clear examples of what it considered violence and what is not. For example, the study can focus on clear violent acts such as kicking another person. They can track the number of times a child kicks another person in a day before the violent video games and then after the violent video games. This will avoid any miscalculations of the violence and will more accurately display what types of violent behaviors have an increase after the exposure.

Sukal, M. (2013). Research methods: Applying statistics in research. San Diego, CA: Bridgepoint Education, Inc.

Reply Reply to Comment ————————————————————————————————–

CLASSMATE #2—M. R.

Over the past couple of weeks, I played around with the ideal of using one of my questions which completely had me lost.  Nevertheless, I am using another question which I feel would be more attached to this week’s discussion.   My question derives from what seems to be a problem within my line of work concerning performance and rewards (bonuses):  “Do monetary incentive strategies drive and increase performance?”  People are motivated by many different things and money may not drive everyone identified within a population of a collections department. 

But if I were to take a sample of the main population (which continues to achieve monthly performance metrics), how would I know whether or not the sample taken actually belongs to some other population?  It is commonly accepted that monetary incentives drives performance, so I consider this the main consensus, or the population which I would ideally extract my sample from.  The “z-test” would be appropriate for my revised question because according to Sukul (2013), “the sample data we analyze sometimes does not fit well with the population presumed to be the source, the z-test provides a way to determine whether the sample belongs to some other population, an outcome related to the concept of statistical significance” (Sukul, 2013).  There could potentially be those within a sample that are motivated by providing stellar service thus providing a positive customer orientation adding to the achieved performance.  The z-test would also be instrumental in informing organizational decisions makers whether or not their strategy using monetary incentives actually makes a difference or not. 

The dependent (outcome) variable here would be increased performance, and our independent (predictor) would be monetary incentives. These variables Type of scales that could be used would be interval scales because if we continued with research in this area, we would be dealing with interval estimates based on discrete data as our sample of a point estimate, being an estimate of our population’s value.   The null hypothesis in this research seeks to disprove that monetary incentives increase performance, with our alternative hypothesis being that monetary incentives are subjective, and that people are motivated by different things.  According to JYOTHI (2016), “The MASLOW’S Theory of need hierarchy clearly states that at initial level the money incentives plays a greater role, but once the physiological (food, water, shelter, clothing and sleep) and safety needs(health, employment, property, family and social stability) are fulfilled the other needs like love and belonging (friendship and sense of connection), self-esteem (confidence, achievement, respect from others and the need to be unique individuals) and self-actualization (morality, acceptance and your purpose) can be satisfied only though the non-monetary benefits” (JYOTHI, 2016, p. 45).  Error types which can be found would potentially be type II errors,  because it is possible that our sample would render some similarities.  

References

JYOTHI J. (2016). Non-Monetary Benefits & Its Effectiveness in Motivating Employees. CLEAR International Journal of Research in Commerce &  Management, 7(5), 45–48. Retrieved from http://search.ebscohost.comproxy-library-dev.rockies.edu/login.aspx?direct=true&AuthType=ip,url,uid&db=buh&AN=119728661&site=ehost-live (Links to an external site.)Links to an external site.

Sukal, M. (2013). Research methods: Applying statistics in research. San Diego, CA: Bridgepoint Education, Inc.

Reply Reply to Comment