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Environmental effects of violent and aggressive behaviors in malesEnvironmental effects of violent and aggressive behaviors in males
Paper details 
Assignment highlighted in red
The paper must be in times new Roman. 12pts
Proposal (1,000-1,500 words) on a topic relevant to the course. To complete the Research Proposal, do the following:
Review the Topic 7 lecture section on The Results and Discussion Sections in the Research Proposal for a brief overview pertaining to “how to” complete the assignment.
Find a research article, critique it, include at least two charts or tables in results/appendices sections
1. Introductory section: Include hypothesis and a review of the literature.
2. Methodology section: Include subsections on Participants, Apparatus/Materials/Instruments, Procedure, and Design.
3. Results section: Include statistic, critical values, degrees of freedom, and .05 alpha level. (Include tables and charts). Include which correlated t test/dependent t test was used, include calculations.
4. Discussion section: Include interpretation of results, ethical concerns, limitations of study, and suggestions for future research.
5. Appendices section: Include a minimum of two appendices (either two figures, two tables, or a figure and a table).
6. Include at least six to eight scholarly references
Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required. Please read lecture 7 below:
Lecture 7 How to complete assignment above:
Introduction
In this lecture, tips are given on how to choose the correct statistic for a particular experimental design. Also discussed are how to calculate results with chi-square tests, the t-test for correlated groups, and the analysis of variance formula.
The Correlated-Groups t-test (or the t-test for Two Related Samples)
As seen in Module 6, the t-test examines differences in designs that have one independent variable, two treatment groups, and a dependent variable that is measured by an interval or ratio scale (Bluman, 1998). Questions to ask are: 1) are we using a directional or nondirectional hypothesis (, thus, a one-tailed or a two-tailed test), 2) what is our significance level, and 3) how many degrees of freedom do we have? In within-subjects designs or repeated-measures designs, subjects serve as their own controls. Thus, this design lowers the need for subjects and reduces variance and error by removing individual differences. This design is also effective when demonstrating changes over time, such as learning or development (Gravetter & Wallnau, 2008). This t statistic is also used for between-subjects designs that use matched groups. The critical values of t are listed in Table B-2 (Jackson, 2011, p. 337).
The t-test for related samples involves calculating difference scores (D)between the subjects’ scores in treatments 1 and 2 (in a within-subjects design) or between each pair of subjects’ scores (in a matched-subjects design).
The formula is:

Once the mean for difference scores is calculated, the t statistic can be calculated:

Hypothesis Testing, Confidence Intervals, and Effect Size for the Correlated-Groups t-test and the Matched Design
The four steps for hypothesis testing are the same as those seen previously. In step 3, sample variance must be calculated since population variance is unknown:

Cohen’s d for repeated-measures designs is:

Variance (r²) is calculated the same way as shown previously.
The Analysis of Variance Test
The analysis of variance (ANOVA) test examines mean differences among two or more treatment groups (Bluman, 1998). The one-way ANOVA is used in two cases: 1) a one-way between-subjects analysis of variance is used when there is one independent variable with multiple independent groups, and 2) a one-way repeated measures analysis of variance is used when there is one independent variable with multiple matched groups (or a within-subjects design). The two-way ANOVA is used for factorial designs (two or more independent variables), whether the design uses independent groups, matched groups, or a combination of the two (a mixed design). In addition, the data should be measured by interval or ratio scales. Additional criteria are that the variances of the groups’ populations are roughly equal (or homogeneous) and that the populations are normally distributed. However, the test is fairly robust,meaning that meaningful results may be obtained even if some of the criteria are unmet (Myers & Hansen, 2006).
Instead of using ANOVA, one could simply calculate a series of t-tests, but that is more cumbersome and, more importantly, increases the chance of committing a type I error. ANOVA examines mean differences indirectly by calculating differences in variance, a statistic called the F ratio (Bluman, 1998). The numerator of the F ratio calculates between-groups variance, actual differences caused by the independent variable plus random chance fluctuations caused by error. The denominator of the F ratio calculates within-groups variance, which is error caused by random chance fluctuations among the subjects. Thus, the F ratio is an attempt to factor out random chance error; what is left over should reflect the impact of the independent variable on subjects’ behavior.
Following is an example of working through the ANOVA formulas. The student is encouraged to follow each step outlined in Jackson’s text. One-way, between-subjects ANOVA is calculated as follows: Examine Table 20.1 (p. 299) to see the hypothetical raw data, means, and the grand mean for this particular study involving one independent variable and three treatment groups. Next, SSTotal is calculated in Table 20.2 (p. 302), and the denominator of the F ratio, within-groups variability, is calculated in Table 20.3 (p. 303). The denominator, MSW, is the meansquarewithingroups. It is calculated by dividing SSW, or sum of squares for within-groups variability, by dfW, or degrees of freedom for within-groups variability.
Once the denominator is calculated, then the numerator can be calculated for between-groupsvariability. The grand mean is calculated first, followed by calculating deviations of group means from the grand mean, and then calculating the squareddeviations (SSB). Finally, dfB is calculated so that MSB (the meansquarebetweengroups) can be calculated (SSB divided by dfB). The F ratio can now be computed (it is MSB divided by MSW). Once the ratio is obtained, its significance level can be determined (Table B-3, pp. 379-381). There are two df numbers to use. Use dfBfor the column, and dfW for the row. For each pair of df, find the critical F value to beat; the number on top is the .05 value, and below it is the .01 value. Examine Tables 20-5 and 20.6 for tips on how to set up an ANOVA summary table (p. 306).
Because the F test gives results only for the overall pattern of treatment means, sometimes more information is desired (e.g., differences between two of the cells). In this case, post hoc tests may be used such as Tukey’s post hoc test; they are like t-tests, except they are more conservative to minimize chances of a type I error (however, because of that stringency, they increase the chance of making a type II error) (Bluman, 1998). If there is good reason before the experiment is run to expect that a t-test will need to be calculated between particular cells, then an a priori comparisoncan be planned; however, it is not as stringent as a post hoc test in determining statistical significance.
With computers being used more and more to calculate results (e.g., Statistical Packages for the Social Sciences); it is very rare to see anyone calculate by hand a two-way ANOVA anymore. It is important to note that, with a factorial design, not only do independent variables (or main effects) need to be checked for possible statistical significance, but also possible interaction effects need to be checked as well (Martin, 1977). For example, for a design with two independent variables, three F ratios would be calculated (two main effects and one interaction). For a design with three independent variables, six F ratios would be calculated. Since interaction effects are often tricky to decipher, this is yet another reason for keeping the design simple.
The Results and Discussion Sections in the Research Proposal
Since you will not typically be carrying out your research proposal, you may be wondering how you will fill out your results section. There are four items that can be included. 1) Since you created the design for the Method section, you can select the appropriate statistic. 2) You can include the alpha level (of .05). 3) You know how many subjects you need, so df can be calculated. 4) Thus, you can also calculate the critical value needed to reject the null hypothesis.
The Discussion section should explain three aspects: 1) what the results mean, in “everyday” language (practical versus research significance), 2) the strengths and the limitations of the study, and 3) future possibilities for research (Martin, 1977). In addition, for this class’ project, a discussion of ethics is needed (e.g., how ethical issues were dealt with).
Following the body of the paper is the References page; see the American Psychological Association (APA) Manual for the correct format (pp. 49-51). The last two items are Tablesand Figures. The research project must contain one of each (or two tables or two figures). Tables are columns of data (if you do not carry out the project, do not make up data; the table format can still be set up). Figures include pictures, graphs, or drawings.
Conclusion
This lecture discussed how to use inferential statistics. Two inferential tests were examined in detail: the t-test for correlated groups and the one-way analysis of variance test. In the last lecture, tips for drawing appropriate inferences and writing in APA format will be explored

Environmental effects of violent and aggressive behaviors in males

Environmental effects of violent and aggressive behaviors in males

Paper details 
Assignment highlighted in red
The paper must be in times new Roman. 12pts
Proposal (1,000-1,500 words) on a topic relevant to the course. To complete the Research Proposal, do the following:
Review the Topic 7 lecture section on The Results and Discussion Sections in the Research Proposal for a brief overview pertaining to “how to” complete the assignment.
Find a research article, critique it, include at least two charts or tables in results/appendices sections
1. Introductory section: Include hypothesis and a review of the literature.
2. Methodology section: Include subsections on Participants, Apparatus/Materials/Instruments, Procedure, and Design.
3. Results section: Include statistic, critical values, degrees of freedom, and .05 alpha level. (Include tables and charts). Include which correlated t test/dependent t test was used, include calculations.
4. Discussion section: Include interpretation of results, ethical concerns, limitations of study, and suggestions for future research.
5. Appendices section: Include a minimum of two appendices (either two figures, two tables, or a figure and a table).
6. Include at least six to eight scholarly references
Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required. Please read lecture 7 below:
Lecture 7 How to complete assignment above:
Introduction
In this lecture, tips are given on how to choose the correct statistic for a particular experimental design. Also discussed are how to calculate results with chi-square tests, the t-test for correlated groups, and the analysis of variance formula.
The Correlated-Groups t-test (or the t-test for Two Related Samples)
As seen in Module 6, the t-test examines differences in designs that have one independent variable, two treatment groups, and a dependent variable that is measured by an interval or ratio scale (Bluman, 1998). Questions to ask are: 1) are we using a directional or nondirectional hypothesis (, thus, a one-tailed or a two-tailed test), 2) what is our significance level, and 3) how many degrees of freedom do we have? In within-subjects designs or repeated-measures designs, subjects serve as their own controls. Thus, this design lowers the need for subjects and reduces variance and error by removing individual differences. This design is also effective when demonstrating changes over time, such as learning or development (Gravetter & Wallnau, 2008). This t statistic is also used for between-subjects designs that use matched groups. The critical values of t are listed in Table B-2 (Jackson, 2011, p. 337).
The t-test for related samples involves calculating difference scores (D)between the subjects’ scores in treatments 1 and 2 (in a within-subjects design) or between each pair of subjects’ scores (in a matched-subjects design).
The formula is:

Once the mean for difference scores is calculated, the t statistic can be calculated:

Hypothesis Testing, Confidence Intervals, and Effect Size for the Correlated-Groups t-test and the Matched Design
The four steps for hypothesis testing are the same as those seen previously. In step 3, sample variance must be calculated since population variance is unknown:

Cohen’s d for repeated-measures designs is:

Variance (r²) is calculated the same way as shown previously.
The Analysis of Variance Test
The analysis of variance (ANOVA) test examines mean differences among two or more treatment groups (Bluman, 1998). The one-way ANOVA is used in two cases: 1) a one-way between-subjects analysis of variance is used when there is one independent variable with multiple independent groups, and 2) a one-way repeated measures analysis of variance is used when there is one independent variable with multiple matched groups (or a within-subjects design). The two-way ANOVA is used for factorial designs (two or more independent variables), whether the design uses independent groups, matched groups, or a combination of the two (a mixed design). In addition, the data should be measured by interval or ratio scales. Additional criteria are that the variances of the groups’ populations are roughly equal (or homogeneous) and that the populations are normally distributed. However, the test is fairly robust,meaning that meaningful results may be obtained even if some of the criteria are unmet (Myers & Hansen, 2006).
Instead of using ANOVA, one could simply calculate a series of t-tests, but that is more cumbersome and, more importantly, increases the chance of committing a type I error. ANOVA examines mean differences indirectly by calculating differences in variance, a statistic called the F ratio (Bluman, 1998). The numerator of the F ratio calculates between-groups variance, actual differences caused by the independent variable plus random chance fluctuations caused by error. The denominator of the F ratio calculates within-groups variance, which is error caused by random chance fluctuations among the subjects. Thus, the F ratio is an attempt to factor out random chance error; what is left over should reflect the impact of the independent variable on subjects’ behavior.
Following is an example of working through the ANOVA formulas. The student is encouraged to follow each step outlined in Jackson’s text. One-way, between-subjects ANOVA is calculated as follows: Examine Table 20.1 (p. 299) to see the hypothetical raw data, means, and the grand mean for this particular study involving one independent variable and three treatment groups. Next, SSTotal is calculated in Table 20.2 (p. 302), and the denominator of the F ratio, within-groups variability, is calculated in Table 20.3 (p. 303). The denominator, MSW, is the meansquarewithingroups. It is calculated by dividing SSW, or sum of squares for within-groups variability, by dfW, or degrees of freedom for within-groups variability.

Once the denominator is calculated, then the numerator can be calculated for between-groupsvariability. The grand mean is calculated first, followed by calculating deviations of group means from the grand mean, and then calculating the squareddeviations (SSB). Finally, dfB is calculated so that MSB (the meansquarebetweengroups) can be calculated (SSB divided by dfB). The F ratio can now be computed (it is MSB divided by MSW). Once the ratio is obtained, its significance level can be determined (Table B-3, pp. 379-381). There are two df numbers to use. Use dfBfor the column, and dfW for the row. For each pair of df, find the critical F value to beat; the number on top is the .05 value, and below it is the .01 value. Examine Tables 20-5 and 20.6 for tips on how to set up an ANOVA summary table (p. 306).
Because the F test gives results only for the overall pattern of treatment means, sometimes more information is desired (e.g., differences between two of the cells). In this case, post hoc tests may be used such as Tukey’s post hoc test; they are like t-tests, except they are more conservative to minimize chances of a type I error (however, because of that stringency, they increase the chance of making a type II error) (Bluman, 1998). If there is good reason before the experiment is run to expect that a t-test will need to be calculated between particular cells, then an a priori comparisoncan be planned; however, it is not as stringent as a post hoc test in determining statistical significance.
With computers being used more and more to calculate results (e.g., Statistical Packages for the Social Sciences); it is very rare to see anyone calculate by hand a two-way ANOVA anymore. It is important to note that, with a factorial design, not only do independent variables (or main effects) need to be checked for possible statistical significance, but also possible interaction effects need to be checked as well (Martin, 1977). For example, for a design with two independent variables, three F ratios would be calculated (two main effects and one interaction). For a design with three independent variables, six F ratios would be calculated. Since interaction effects are often tricky to decipher, this is yet another reason for keeping the design simple.
The Results and Discussion Sections in the Research Proposal
Since you will not typically be carrying out your research proposal, you may be wondering how you will fill out your results section. There are four items that can be included. 1) Since you created the design for the Method section, you can select the appropriate statistic. 2) You can include the alpha level (of .05). 3) You know how many subjects you need, so df can be calculated. 4) Thus, you can also calculate the critical value needed to reject the null hypothesis.
The Discussion section should explain three aspects: 1) what the results mean, in “everyday” language (practical versus research significance), 2) the strengths and the limitations of the study, and 3) future possibilities for research (Martin, 1977). In addition, for this class’ project, a discussion of ethics is needed (e.g., how ethical issues were dealt with).
Following the body of the paper is the References page; see the American Psychological Association (APA) Manual for the correct format (pp. 49-51). The last two items are Tablesand Figures. The research project must contain one of each (or two tables or two figures). Tables are columns of data (if you do not carry out the project, do not make up data; the table format can still be set up). Figures include pictures, graphs, or drawings.
Conclusion
This lecture discussed how to use inferential statistics. Two inferential tests were examined in detail: the t-test for correlated groups and the one-way analysis of variance test. In the last lecture, tips for drawing appropriate inferences and writing in APA format will be explored

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