Please use the dataset provided to calculate all results and do a simple write-up of the results. To...

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Please use the dataset provided to calculate all results and do a simple write-up of the results. To calculate the results, you need to use the PSPP software. I have provided the dataset and an example of how the write needs to be. In order to do the write up, I have also provide the paper with the Hypothesis and the research questions. This needs to be done in APA style and must include in-text citations. I would also require a bibliography for the references. Can you provide a link or the references?Microsoft Word - APA Results Write-up Sample 1 1    N. WILLS  PSYC 499  Chapter 4: Results  Insert chapter introduction here…  Screening   Descriptive statistical analyses where conducted to screen all data entry errors and  missing data. There were no errors, but there were missing data.  Demographic   The sample consisted of 439 participants, there were some missing data for the primary  variables; however, I opted to use pairwise deletion during analysis instead of removing the  participants. There was a total of  42.1% male (n = 185) and 57.9% females (n = 254). The ages  of the participants ranged from 18 years to 82 years old with an average age of 37 years (M =  37.44, SD = 13.20).  Preliminary Analysis  Reliability   All scales used in this study showed good internal consistency: PCOISS (α = .90, n =  432), Optimism Scale (α = .80, n = 435), and PSS-10 (α = .85, n = 433). All items were  positively correlated in the Corrected Item-Total Correlation column for all three scales. For this  reason, no items were removed in the calculation of the total scores for all scales (see Pallant,  2016).   Commented [NW1]: Introduces briefly what is addressed in the  chapter and the software (e.g. PSPP) you used to analyze your data.  Commented [NW2]: Give step by step account of how you  checked the data for errors and fixing or removing these errors.  Commented [NW3]: Provide the means, standard deviations,  frequencies, etc. for your sample characteristics or demographics  in the study.   Explain how many persons were included in your sample and  why.  Support all rational with citations for your decisions.   This is where you also describe how you cleaned data (remove  persons) and why.  Commented [NW4]: Note: This is left generally as participants  for this sample paper.  For your thesis - Remember to use the exact sample you are  investigating – e.g. students, employees, etc.  Commented [NW5]: If you are removing all participants with  missing data for the primary variables (IVs and DV), this must be  communicated at this point.  Commented [NW6]: Always italicized letter in the statistical  presentation.    Ensure to use small n as you are not looking at the entire targeted  population but a sample.  Commented [NW7]: Always present the standard deviation (SD)  along with the mean (M)  Commented [NW8]: Discuss all scales scoring and  reliabilities.   If any item need to be removed, please provide information about  what item was removed and why.  Please support all decisions with citations.  Commented [NW9]: Notice the differing n value, because not all  participants provide scores for the PCOISS.     However, this is not an issues if you decided to remove all  participants with missing data for your primary variables (i.e. you IVs  and DV) – the sample size will be the same as the overall sample size  and you do not need to repeat n.  2    N. WILLS  PSYC 499  Test of Assumptions  Sample Size.   The sample size for the standard multiple linear regression analysis was 424 participants.  Using Steven (1996) suggestion of 15 persons per predictor the sample size exceeds the  minimum requirement based on two predictors for standard multiple regression analysis to  reduce the chances of a type I error.  Number of Variables and Level of Data   A standard multiple regression analysis requires two or more independent variables and  one dependent variable (Pallant, 2010). In this study there are two predictors (PCOISS and  optimism) and one dependent variable (perceived stress); thus, meeting the minimum  requirements for this type of analysis. Pallant (2016), also stipulated that a standard multiple  regression analysis requires that the predictors are continuous or dichotomous discrete variables  and the dependent variable is continuous. All three variables in the study were measured at the  interval level because they were measured using Likert scales.  Independence of Observation and Related Pairs   It is assumed that each participant completed the self-report questionnaire on their own  without the influence of others. Furthermore, it is assumed that responses on the scales measuring  the two independent variables (PCOISS and optimism) did not have an effect on each other’s  responses (see Pallant, 2016). Hence, I assumed that the independence of observation is met for  this analysis. As stated previous, pairwise deletion was chosen to address missing data ensuring  that each participant provided a score for each of the primary variables; thus, meeting the  assumption for related pairs (see Pallant, 2016).  Commented [NW10]: Discuss all preliminary analysis (e.g.  normality, outliers, linearity, etc.) for all statistical test (e.g.  standard multiple regression) you applied for measure your  research questions and hypotheses.  Explain all decisions and support with cited information.  Commented [NW11]: Given as part of the regression analysis  output. However, if participants responded on all your scales, then  this will be the same as your total sample size.  3    N. WILLS  PSYC 499  Outliers   The effects of outliers on the distribution of scores for the three variables were investigated  at the univariate level of analysis, given that multiple regression analysis is very sensitive to  outliers (see Pallant, 2016). An investigation of the mean and 5% trimmed mean showed that any  outliers present were not affecting the mean, given that these two values were similar for each  variable: PCOISS (M = 60.63, SD = 11.99, 5%TM = 60.92), optimism (M = 22.12, SD = 4.43,  5%TM = 22.28), and perceived stress (M = 26.73, SD = 5.85, 5%TM = 26.64).  Normality   The distribution for PCOISS was weakly, negatively skewed (S = -.40) with the majority  of scores falling above the mean; it was also slightly leptokurtic (K = .26, see Figure 2). Similarly,  the distribution for optimism was also weakly, negatively skewed (S = -.49) and leptokurtic (K =  .21, see Figure 2). Contrastingly, perceived stress was weakly, positively skewed (S = .25) with  the majority of scores falling below the mean; but it was also leptokurtic (K = .18, see Figure 2).  All three distributions appeared to be not normal. Nevertheless, the sample size for this analysis is  large; therefore, a non-normal distribution does not pose a problem reaching significant power  during parametric testing (see Pallant, 2016).  Commented [NW12]: No need to reinstate the n values here  given that they would be same as the n values for the reliability  analysis.  4    N. WILLS  PSYC 499  Figure 2   Histograms of the Distributions for PCOISS, Optimism, and Perceived Stress Linearity and Homoscedasticity   The scatterplot investigating the relationship between PCOISS and perceived stress  showed a downward linear relationship, suggesting that the relationship between the two  variables is negative (see Figure 3). Similarly, the scatterplot investigating the relationship  between optimism and perceived was a downward liner relationship (see Figure 3). Both  scatterplots showed a relatively cigar-shaped with the clusters predominantly packed closely  Commented [NW13]: This is another option for presenting your  figures to save on space. Once the figure is clear to understanding,  you can combine them into one figure.    If it becomes unclear the presenting them as separate figures.  5    N. WILLS  PSYC 499  together with some dispersion, suggesting a moderate to strong effect size (see Pallant, 2016).  Thus, the assumption of linearity and homoscedasticity was met for this analysis.  Figure 3  A Scatterplot Showing the Relationship between Perfectionism and Psychological Wellbeing Multicollinearity and Singularity   The occurrence of multicollinearity and singularity is a major problem in a standard  multiple regression analysis (Pallant, 2016). It is assumed that singularity is not occurring  between the predictors, given that they measure separate constructs and are not subscales of each  other. The correlation matrix (Table 1) showed that the relationship between PCOISS and  optimism (r = .51, p

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