Regression Analysis

On 08.29.10, In statistics, By Dan Calloway

by DAN CALLOWAY
Published on 29 August 2010

WEAVERVILLE, NC – Regression analysis is a statistical process used to examine why an independent variable does not fully explain or predict the dependent variable in a study whereby the researcher looks to answer three basic questions of what is: (1) the total contribution of all independent variables together, (2) the comparative importance of the different variables, and (3) the role a particular independent variable plays mutually exclusive of the effects that other independent variables have on the dependent or outcome variable (Vogt, 2007, p. 145; p. 147). The role of the researcher in using regression analysis is to decide whether to use all the predictor or independent variables to make predictions of the dependent variable or whether to explain the separate effects of the independent variables in making the predictions of the dependent variable; that is, the questions that researchers ask of regression analyses are shaped by the goals of their research and not be the technicalities or complexities of their computations (Vogt, p. 147).

Giving consideration to Project 2, I would use regression analysis to answer the three basic questions discussed earlier. Regression analysis would be used to determine the total contributions of all the independent variables taken together in my problem statement under consideration or study, to identify the comparative importance of the different variables chosen, and to investigate the role of each predictor variable in predicting the outcome variable when examined mutually exclusively of the effects of the other identified predictor variables on the outcome variable. In my regression analysis, the decision as to the independent variables and the dependent variable would be predicated on which variables were predictors and which variable(s) were outcomes in the analysis or problem statement. Those variables identified as predictors or whose values were allowed to vary independently would be selected as the independent variables (IVs) and the variable(s) that were dependent on the effects of the predictors would be classified as the (DVs) or dependent or outcome variable(s).

When conducting research, the researcher could reasonably assume that important variables (such as mediating variables) have been omitted from consideration of the problem under study if the effects of the existing predictor variables were not able to fully explain the outcome or criterion variable. The use of regression analysis is a good means of determining that important variables may have been omitted from the research especially if the regression coefficient of the focus IV is less than the regression coefficient with controls or controls with mediators are added. If the current predictor variables are inadequate to explain the effects on the outcome variable, then it can be logically assumed that there are other predictor variables as yet unidentified that are playing a role either through their interaction with other independent variables or their own direct effect on the outcome variable (Vogt, 2007).

Thus, the research problem I have identified is: “I would like to investigate whether there is a positive correlation between sexual and physical abuse of a child in his/her early childhood development and whether s/he was raised in a loving or abusive single-parent or traditional mixed parental environment, and the propensity of the child to become a criminal outcast in his/her adolescent or adult life as viewed by society.” The independent or predictor variables identified are: environmental upbringing, gender, ethnicity, age, and parental guidance. The outcome or criterion variable identified is adolescent or adult criminal affiliation.

Reference:

Vogt, W. P. (2007). Quantitative Research Methods for Professionals (Custom., p. 334). Boston: Pearson Education, Inc.

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Twitter Weekly Updates for 2010-08-15

On 08.15.10, In blogging, By dancalloway
  • Starting a new job on Monday. #

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The Worldwide Shadow War Against Terrorism

On 08.15.10, In blogging, Middle East, Terrorism, U.S. Military, By Dan Calloway

by DAN CALLOWAY
Published on 15 August 2010

WASHINGTON, DC – The Obama administration has launched a shadow war against Al Qaeda and terrorism on a worldwide scale. From the deserts of North Africa, to the desolate mountains of Pakistan, even to former Soviet republics that have been crippled by ethnic and religious strife, and covering more than a dozen countries throughout the world, the United States has expanded its worldwide war against terrorism and its military and intelligence operations, pursuing the enemy through the use of commando teams and robotic drones, paying civilian contractors to spy on terrorists and training local operatives to defeat terrorism wherever it exists.

On May 25th of this year, an airstrike hit a suspected group of Al Qaeda operatives in the remote desert region of Marib Province. The airstrike, unfortunately, also hit and killed the province’s deputy governor, a well-respected leader who Yemeni officials claim was attempting to talk Al Qaeda into halting their war on terrorism in the region. The President of Yemen, Ali Abdullah Selah, took responsibility for the attack and paid the terrorists blood money as compensation for the loss.

As it turns out, the airstrike in Marib Province was not the work of President Selah’s forces, but was a secret mission launched by the United States, and was the fourth such mission against Al Qaeda launched by the United States military in the desert and mountainous regions of Yemen since December, 2009.

The White House and the Obama administration has increased its efforts against terrorism by strengthening the CIA’s drone missile campaign in Pakistan, approved secret raids against Al Qaeda in Samalia, and launched clandestine operations in Kenya. Working with its European allies, the United States has helped to dismantle known terrorist groups in North Africa, and assisted the French in removing terrorists in Algeria. In addition, the United States is paying contractors to spy on terrorist group activities in Pakistan and other locations and report back to the government on what they have uncovered.

Unlike his predecessor, George W. Bush, President Obama’s secret shadow war against terrorism in Yemen and other parts of the world have never been publicly acknowledged. The troop buildup in Afghanistan is the only publicly announced campaign against terrorism that the White House has officially admitted is underway.

The Obama administration has chosen to take a different approach to fighting terrorism; one that does not boast about what it is doing, but secretly accomplishes the same mission and ultimately saving the American taxpayer millions of dollars by not involving the United States in an all-out war against any particular group or country, such as the war on Iraq, toppling huge governments and resulting in years of occupation. The “scalpel” rather than “hammer” approach taken by the White House in its fight against worldwide terrorism is seen as an advantage in “getting the job done” without the aftereffects that come from public acknowledgement of the activities.

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Unsafe Driving

On 08.15.10, In blogging, Highway Safety, By Dan Calloway

by DAN CALLOWAY
Published on 15 August 2010

WEAVERVILLE, NC – This powerful video depicts the hazards that one finds on the roadways today, including the dangers to pedestrians on our busy city streets. The accidents are staged in the video but it is still difficult to watch.

Please slow down and pay attention to what you’re doing and where you’re driving. It can save a life.


Please slow down and save the life of someone today!

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Analysis of Variation (ANOVA)

On 08.13.10, In blogging, statistics, By Dan Calloway

by DAN CALLOWAY
Published 13 August 2010

WEAVERVILLE, NC – The t-test is a statistical test for two means. It is most often used to compare the means of two experimental conditions and, thus, two applications can be distinguished. The first one applies to experiments with two independent groups; that is, when subjects are assigned randomly to an experimental group and also when subjects are assigned at random to an experimental group and a control group. The null hypothesis is then that the population mean scores are equal for the two conditions. That is to say, there is no difference if we could compare the entire population of scores for the experimental and the control conditions. H0 : μE – μC = 0, where μE and μC represent the population means of the experimental and the control groups, respectively. Here, the alternative hypothesis would be Ha: μE – μC ≠ 0. If the direction of difference were detected ahead of time, a one-tailed version of Ha would be shown as μE – μC > 0. The t-test assumes that the scores in each condition are normally distributed and that the two distributions have equal variance (Levin, 1999)⁠.

When more than two means are analyzed, such as the case with more than two independent variables, then the t-test becomes an inappropriate test and gives way to statistical test of more than two means: analysis of variation, also called the ANOVA. The associated statistic of the ANOVA is the F-test. There are two versions of the ANOVA and its associated statistic. The first version of ANOVA is known as the one-way ANOVA, in which there is only one independent variable, but this independent variable may have many different levels as when you are comparing various dosages of a drug or varying amounts of reinforcement (Levin, 1999). In this case, the null hypothesis is H0: μ1 = μ2 = . . . = μk, where μi is the population mean for level i. Here, the alternative hypothesis would state that the population means are not all equal. The ANOVA is similar to an extension of the t-test, but the t-test is only able to test the means of two variables, each containing one level. If, for example, there were eight groups to be compared, and one used a t-test to compare every possible pair of groups, then one would have to conduct 28 different t-tests. If each individual t-test was conducted with
α = 0.05, then you could easily see that 28 t-tests could easily lead to one or more Type I errors because the expected number of errors would be 28 X 0.05 = 1.40. Thus, instead, a single analysis of variation is preferred because it would not be as likely to lead to a Type I error.

The F-test, in ANOVA, is defined as the ratio of two sample variances, that is to say,
F = s12 / s22; hence the term, analysis of variance. In the case of a one-way ANOVA, the variance term in the numerator is referred to as the between-groups variance because it is a measure of how much the k different group means vary from one another. The variance term in the denominator is called the within-groups variance because it is a measure of the average variance of scores within each experimental condition. Thus, the denominator is a measure of sampling error, while the numerator is a measure of sampling error plus any differences between experimental conditions that go beyond sampling error (Levin, 1999).

Reference:

Levin, I. P. (1999). Relating Statistics and Experimental Design: An Introduction (p. 90). Thousand Oaks, CA: Sage Publications, Inc.

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