Discussion (p.260) 1. Give some examples of how the results of a study might be significant statistically yet unimportant educationally. Could the reverse be true? Answer: Consider an example of the study in which the researcher studied the relationship between the intrinsic rewards on employee motivation. Results of the study indicated the correlation coefficient of 0.23 at significant level of 0.05. The relationship between two variables is statistically significant but not practically because 0.23 or 23% is a weak correlation and does not have very good predicting power in actual. Reverse of the situation can also be true. There can be studies that are practically significant but not practically. 2. Are there times when a slight difference in means …show more content…
When comparing groups, the use of frequency polygons helps us decide which measure of central tendency is the most appropriate to calculate. How so? Answer: Mean is the best measure of central tendency that can be used for frequency polygons because the highest mean is represented by the highest bar in the frequency polygon. 4. Why is it important to consider outliers in scatter plots? Answer: Outliers are necessary to be detected in the scatter plot because they are abnormal values and they can cause skewness in the data plots. They can also impact the results. 5. “When analyzing data obtained from two groups, the first thing researchers should do is construct a frequency polygon of each group’s scores.”Why is this important—or is it? Answer: Frequency polygon summarizes the data and presents in graphical form which is easier to have a look at the data trends. 6. Why is it important to use both graphs and summary indices (e.g., the means) to interpret the results of a study—or is it? Answer: Graphs are necessary to study the general trends and have a quick look of the data; however, the summary indices are necessary to interpret results accurately. Graphical presentation is not that accurate as is the summary
The data we gathered from our analysis are presented in a formal way on the following page.
2. The researcher does not want or need to generalize the results to a population.
The articles, published after 1996, contain varied methods of research attainment, but share similarities such as being a self-survey, having a small sample size, and being
The first table was titled Other Measures. It provided information on the sample size, minimum, maximum, first quartile, third quartile, given percentage, and value of percentile. These values are used to compute range and interquartile range in the measures of dispersion. The last table shows the mean plus or minus 1, 2, or 3 times the standard deviation and offers details on how many values fall within the ranges created by those calculations.
...ferred because it produces meaningful information about each data point and where it falls within its normal distribution, plus provides a crude indicator of outliers. (Ben Etzkorn 2011).
When we are introduced to statistics we either face it or deal with it head-on despite our fear with this subject and we start thinking about the time it would take us to complete a paper or statistics design bases on the extended reading we would have to do in order to understand the subject for clarification of what to expect, and take away from that subject. Therefore, this discussion will define confidence intervals, stipulate when we would need to use confidence intervals in statistical analysis, and examine why the Publication Manual of the American Psychological Association recommends the inclusion of confidence intervals in study results.
While the chart above provides us with a plethora of information, we can take it a step further by making it easier to distinguish factors such as percentages as well as totals. Starting with a frequency chart, one can appreciate the simplicity behind it.
of which will be used to find the mean, median, mode, range as well as
While accumulating data is essential to interpreting progress and making progress in future ventures, the ability to display data is critical. Displaying data allows for a superior understanding of the information. The three most common ways to visually display data is through the use of pie charts, bar charts, and line graphs. Pie charts are best used when comparing parts of a whole (Data Driven Decisions). A disadvantage of using a pie chart is that one will not be able to see changes over time. If one is trying to best track changes over time bar charts and line graphs will be used. When smaller changes exist, line graphs are better to use than bar graphs. Line graphs can also be used to compare changes over the same period of time for more than one group (How do I). Conversely, when trying to measure change over time, bar graphs are best when the changes are larger (How do I). Below is a chart with examples of data sets and the accompanying appropriate frequency chart.
Statistics is something that comes up in everyday life a lot more often than some people might realize and being able to recognize statistical information as well as knowing how to use it properly can be extremely useful to you. You use it for small things, such as checking what the weather is supposed to be like throughout the week, but it can also be used for far more important situations like in medical circumstances where you are presented with chances of survival or sometimes determining the best course of action to take in order to ensure the safety of others. We all see things like graphs, scatterplots, and other similar types of presentations everywhere when shown any kind of information, from determining your chances of getting into
Ø The “central tendency effect” is when supervisors rate their employees as meeting standards on each task they are being evaluated on. They don’t want to provide documentation when the employee is not performing to expectations. They don’t want to be the “bad guy” and choose to not “upset” the employee with negative feedback. (Neely, G.)
In addition, Leedy and Ormrod explained that a quality research problem needs "interpretation of data" and "mental struggle". The authors illustrated various examples of inappropriate problems for research: for example, (a) problems based a yes or no answer, (b) questions centered on personal inquiry, (c) using problems to calculate numbers in data, and (d) using problems to compare and contrast data (p. 45). Because the research problem is the "heart" of the entire research, it must have a direct line to the goal. In order to generate a high-quality problem statement, it is important to understand the nature of the problem.
The resulting inferences are only as good as the combination of these factors. Statistical analysis, however, can suffer from a lack of theoretical and conceptual underpinnings (Achen 2002: 424, Johnson 2006: 238). Minimalist definitions of a concept include the largest number of cases, but risk conceptual stretching, whereas maximalist definitions would include so many descriptive attributes, the number of cases dwindles. Statistical analysis tends to include the most cases possible and thus risks conceptual stretching (Sartori 1970, Munck and Verkuilen 2002). Statistical models can be underspecified and not include independent variables that impact the change in the dependent
Also, it is not only about representing the final outcome, but also applicable in understanding the raw data. It is always better to represent the data in order to get better insights and how to solve the problem or get a meaningful information out of it which influences the system.
The coefficient of variation is essentially a comparison of standard deviation to its mean. The coefficient of variation can be useful in computing standard deviation that have been computed from data with different means.