PSYCH 610 Histogram Lecture Notes PDF

Preview

Summary

LECTURE NOTES...

Description

PSYCH 610 HISTOGRAM LECTURE NOTES "A histogram uses bars to display a frequency distribution for a quantitative variable" (Cozby, p.247). Histograms are something that are used often in my job, and it's a good measuring tool. Most frequently, it's used to track our physical training assessment results. I like histograms because you can display different components over several years at different points in the graph. In example, we are tested on our waist measurement, push-ups within a minute, sit-ups within a minute, and our time on a mile and a half run. That's four comments that have separate dots not the graph, and it is tracked over the years that each individual is taking the test. Each line is color coated according to the year that the test was taken. The histogram can be used to quickly see where improvements or drops in performance for each component quickly. histograms are useful because they easily display a lot of information efficiency and at-a-glance. Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. For instance, if we have data on the height of men and women and we notice that, on average, men are taller than women, the difference between the height of men and the height of women is known as the effect size. The greater the effect size, the greater the height difference between men and women will be. Statistic effect size helps us in determining if the difference is real or if it is due to a change of factors. In hypothesis testing, effect size, power, sample size, and critical significance level are related to each other. In Meta-analysis, effect size is concerned with different studies and then combines all the studies into single analysis. In statistics analysis, the effect size is usually measured in three ways: (1) standardized mean difference, (2) odd ratio, (3) correlation coefficient. Unlike most, I know why I ended up this way and it has been a battle to overcome. I was in third grade when the sol called "new" math came into vogue. Like most, I just didn't get it. It was not that i would fail to come up with the correct answer, I did that just fine. It was the equations I had trouble and I ended up with bad grades. I dreaded the time when I had to go to the board and write an equation because I was subject to major ridicule. The teacher would say things like "I don't know why you can't understand this. everyone else got it but you. You are dumb, etc" I swore that if I survived this I would avoid math for the rest of my life. No one likes ridicule and no one likes being called names. It was racially motivated and Kansas is another state I would not be caught dead it. The just of this is that I would probably need a tutor for the rest of this course and the next one. The most interesting topic from this week was the different types of graphs and the different data that coincides with each type of graph. The frequency polygon to me is the most comprehensive visual representation of relationships of data as it shows a clear distinction between the two samples or sets of data. According to Cozby, (2015), “Frequency polygons use a line to represent the distribution of frequencies of scores” (p.248). A frequency polygon has a clear-cut difference especially when two sets of variables are overlapped with a simple line, symbols, dashes and clear points to mark the score on the graph. Learning about graphs and the data that can be represented gives me a better understanding of how to read each one, what they are used for, how to understand if the data is correct. I will use the information from chapter twelve to create my own graphs to give visual representations of the findings in my research, I will be using the graphs in statistics class and the capstone course. It is unclear to me if the correlation coefficient

should be included when there appears to be a connection, but it is not definitive. If the correlation does not show an independent variable’s manipulation changing the dependent variable should it still be included, and an explanation of the discrepancies follow the results. I would like to learn more about power, the bell curve, the best way to calculate my findings and turn the results into usable and understandable data. “The levels of nominal scale variables have no numerical, quantitative properties” (Cozby and Bates, 2015, pg. 243). The levels are basically distinctive categories. Most of the independent variables in studies are nominal, for illustration, in a study that compares behavioral and cognitive treatments for depression (Cozby and Bates, 2015)....