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study_analysis_proposal__03_20_2016.docx

What are the associations among return on equity, return on investment, total annual revenues, and CEOs stock options awards?

Hypotheses

Research Question 1: Is there a statistically significant relationship between return on equity and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

H10: There is no statistically significant relationship between return on equity and CEOs’ stock options awardswhile controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

H1A: There is a statistically significant relationship between return on equity and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

Research Question 2: Is there a statistically significant relationship between return on investment and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

H10: There is no statistically significant relationship between return on investment and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

H1A: There is a statistically significant relationship between return on investment and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

Research Question 3: Is there a statistically significant relationship between annual revenues and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

H10: There is no statistically significant relationship between annual revenues and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

H1A: There is a statistically significant relationship between annual revenues and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

Research Method

In this study, I will adopt a quantitative correlational approach to collect and analyze data. Researchers engaged in quantitative research employ large and random samples, reduce complex phenomena to a few variables, test hypotheses, and thus deduce inferences (Bergman, 2011).The research method will entail a review of pharmaceutical companies financial statements filed with SEC (SEC, 2014). The objective of the proposed research is to determine whether and to what extent a relationship exists between firm performance, measured by ROE, ROI, and annual revenues to stock options awarded to CEO in pharmaceutical companies. In developing the research design for this study, I reviewed Ayiro’s (2012) work on educational research methods and statistics, including theoretical fit (reliability and validity), describing data, and testing hypotheses.

The pros and cons of research methods should be argued in relation to their specific context, including research question posed and resources available for research (Allwood, 2011). Academicians can choose among three methods when conducting research: (a) quantitative, (b) qualitative, or (c) mixed methods (Frels & Onwuegbuzie, 2013). Based upon the purpose of this study to examine the relationship between firm performance and CEO stock options, I selected a quantitative research method (Allwood, 2011). A quantitative study involves researchers asking precoded questions with numeric value response options to examine the relationship between variables (Curtis & Drennan, 2013). Teo (2013) asserted that quantitative approaches best addresses problems in situations in which researchers want to understand which variables or factors influence outcomes.

Other research methods were available to conduct this study. I did not select either a qualitative or a mixed method for a number of reasons. A qualitative method is not appropriate choice for this study because qualitative study’s inductive nature precludes defining variables and hypotheses before conducting the research (Ogussakin, 2015). Qualitative researchers explores questions such as what, why, how, rather than how many or how much;focusing on meaning rather than measuring (Cooper & Schindler, 2013). According to Cooper and Schindler, understanding why individuals and groups think and behave as they do lies at the heart of qualitative research. Findings in qualitative analysis are context-specific, unlike in quantitative analysis,where findings could be generalizable to a large population (Poore, 2014).

A mixed method is not appropriate for this study. Mixed method would involve collecting both quantitative and qualitative data for the study (Frels & Onwuegbuzie, 2013). However, for this study, there is no coherent method for combining qualitative results with quantitative data to achieve the study goals.The choice of a research method can impact greatly the data collections and analysis of the research study (Converse, 2012). The application of triangulation in research yields to complementary of the mixed methods asresearchers use quantitative techniquesto further develop findings derived from qualitative techniques and vice versa (Copper, 2012). Constraints such as time and resources can render using a mixed method approach impractical (Ridder, 2012). In addition, studies on the relationship between CEOs stock options and firm performance tend to favor the use of quantitative approach; remaining consistent will allow building upon the work of previous scholars (Essaid, 2013; Moore, 2014), allowing easy comparison of information.

A quantitative approach is more appropriate than the qualitative approach to determine the associations among ROI, ROE, annual revenues, and CEOs’ stock options compensation. I will seek to infer the relationship between CEO stock options and firm performance within the pharmaceutical industry. Big data are not only about the data, but also about analyzing those data and the resulting theoretical and empirical understanding of how individuals, groups, and societies think and behave. Therefore, the quantitative research method was the most appropriate method for use in this study.

Research Design

Quantitative techniques are appropriate for identifying the relationship between variables (Joanne, 2012). I considered three quantitative research designs, including (a) experimental, (b) quasi-experimental, and (c) correlational design. The experimental design involves the random assignment of variables to test the effectiveness of interventions between two groups (Joanne, 2012). For the purpose of this study, no test of interventions between groups is necessary. Quasi-experimental is designed to investigate the effect of one variable on other variables but lacks the element of random assignment of variables (Aussems, Boomsma, & Snijders, 2009). In this study, no manipulation of variables to measure its effect on other variables is required. Therefore, the appropriate study design is hierarchal and non-experimental (Martinez, 2014).

I will use hierarchical regression analysis to examine the relationship between independent variables (ROE, ROI, and annual revenue) and the dependent variable (CEO stock options) while moderating for firm size, the age of the CEO, and tenure of the CEO. Hierarchical regression is a conservative method of testing the hypothesis; entering control variables into the regression model before the variables of theoretical interest are analyzed (McClelland et al., 2012). Signs of the regression coefficients are used to indicate the relationship between variables and may range from -1 (a perfect negative relationship) to 0 (no relationship) to a +1 (a perfect positive relationship).

I adapted my study from previous research on health care industry by Sigler (2003). Sigler tested the relationship between cash compensation of healthcare CEOs and organization financial performance. This proposed study will include an analysis of the relationship between stock options awarded to CEOs in pharmaceutical companies and organizational financial performance. Stock options constituted only a trivial percentage of CEO pay in the 1970s but grew to a dominant form of pay by the late 1990s (Murphy & Trefftzs, 2012).

While firm performance is measured by many variables (e.g., return on assets, assets ratio, equity ratio, net profit margin), for the purpose of this study, I will use ROE, ROI, and annual revenues. Fathi et al. (2012) stated that ROE measures the impact of management on shareholders’ wealth, and Bihari (2014) stated that ROE is a key indicator of stock price. Pandher and Currie (2013) found a positive relationship between firm revenues and firm performance. Pandher and Currie stated that CEOs use resource- based advantage (the difference between revenues and expenses) to bargain for higher compensation.

Sigler (2003) asserted that there is a positive and significant link between annual revenues and CEO compensation. Moderated variables for this study will be the size of the firm, CEO tenure, and CEO age. Ozkan (2011) found CEO tenure and age of CEO might be related to the entrenchment of the CEO, leading to compensation not tied to the performance of the firm. Lin and Lin (2014) found a significant positive relationship between CEO compensation and firm size. Larger firms are typically more complex, and CEOs are therefore highly compensated (Lin & Lin, 2014). I selected ROE because this measure reflects how well a firm performs from the shareholders’ point of view (Lin & Lin, 2014) and annual revenues, which is an indicator of core earnings of the firm (Ettredge, Schliz, Smith, & Sun, 2010).

Data Analysis

What are the associations among return on equity, turn on investment, total annual revenues, and CEOs stock options awards?

H10: There is no statistically significant relationship between return on equity and CEOs’ stock options awards while controlling for size of firm, age of CEO, and CEO tenure within the U.S pharmaceutical industry.

I will examine the extent and nature of the overall relationship of firm performance, measured by ROE, ROI, and annual revenues, to stock options awarded to CEOs using a quantitative, correlational design. I will use a hierarchical regression model to test the independent variables of ROE, ROI, and annual revenues of firms to dependent variable of CEOs’ stock options, while controllingfor the size of the firm, the age of the CEO, and tenure of the CEO (Tabachnick & Fidell, 2012).

Management scholars have used multiple regression models to examine the relationship between CEO compensation to firm performance. For example, Moore (2014) used a hierarchical regression to examine the relation between CEO compensation to firm performance. Darweesh (2015) used a multiple regression model to specify the relationship between corporate governance, financial performance, and market value. Siger (2003) used a regression model to examine the relationship between CEO salaries to firm annual revenues. In addition, Paz (2012) used a regression model to examine the impact of stock option expensing as part of CEO compensation and earnings quality.

A hierarchical regression analysis is a type of linear regression model in which observations fall into hierarchical levels (Moore, 2014). In this study it will be important that I control how I input variables into the models. Using hierarchical regression will allow in specifying a fixed order of entry for predictor variables (Cooper, 2012).In Hierarchical regression, the researcher, not the computer determines the order of entry of the variables (Moore, 2014).The dependent variable (stock options), followed by the control variables (age of CEO, tenure of CEO and size of the firm) are put into hierarchical model first. This order ensures that the control variables get credit for any variability they may have with stock options (Joanne, 2012). After controlling for the effect of controlled variables, then financial performance variables (ROE, ROI, and annual revenues) will be input into the model to evaluate how much predictive power they have on stock options awards (Regression with SPSS, 2014).

I also chose hierarchical design mainly based on the purpose of the study and nature of the independent, dependent, and control variables. A hierarchical regression is a model comparison approach in which richer models are compared to simple models to infer if additional regressors account for a statistically significant amount of variance (Damien, 2013). I considered other statistical analyses such as ANOVA and logistic regression. ANOVA is used to measure variability between and within groups (Klimberg & McCullough, 2013). The objective of this study is to explore the relationship between firm performance, measured by annual revenues and ROE, to stock options awarded to CEO, and not the analysis of variance (Davis, 2013). Logistic regression is designed for use in studies in which the response variable is a categorical variable with two possible values (Glynn & Robinson, 2014). Logistic regression is distinguishable from multiple linear regression analysis in that the dependent variable is categorical in nature and assumes a nonlinear relationship between the explanatory variables (Teo, 2013). For this study, none of the variables are categorical variables; thus, logistic regression is not appropriate. A hierarchical regression design is used to understand the cause-and-effect relationship between one dependent variable and one or more independent variables (Klimberg & McCullough, 2013). For this correlation design, I will use SPSS Version 21 software to determine the direction of the relationship between the independent and dependent variables.

Data cleaning and screening involves the detection, removal of errors, and inconsistencies in data set (Pham, 2015). Leo (2013) recommends that researchers should look for the following in data screening and cleaning: (a) look for coding errors, (b) look for outliers, (c) check for logical consistency of answers, and (d) decide how to deal with incorrect or missing values. To clean and address missing data, I will use a bar graph to look for outliers (Moore, 2014). I will also use cross-tabulating pairs of variables to root out for data inconsistencies (Regression with SPSS, 2014). In addition, I will also go back to data source to fill in the missing data or remove missing and incorrect values (Miranda, 2015).

In conducting inferential statistics, I will check the data for outliers. To check for the normality of variable, I will use descriptive data such as mean, mode, median, standard deviations, minimum, maximum, and bar graphs. The effect size of the sample will be calculated from AVOVA table while confidence interval will be checked using a t-test. After all assumptions are met, regression outputs, including correlation coefficient, F-ratio, beta, R-square, adjusted R-squared, R-square change values will be evaluated. The F-ratio of ANOVA is reported to indicate the overall regression used for statistical analysis of data and whether the independent variables statistically predict the dependent variables (Pham, 2015). The R-value provides the indication of the quality ty of the prediction of the prediction variable. The R-squared suggest the proportion of variance that can be explained by the independent variables while adjusted R-square also considers the sample size. The R-square change will indicate the change in R-square, indicating the predictive capacity of the dependent variable in the regression model.

Most researchers using statistical tests rely upon certain assumptions about variables used in the analysis (Regression with SPSS, 2014). In this study, I will assume that certain assumptions are not violated. Specifically, the assumptions will be: (a) outliers, (b) linearity, (c) multicollinearity, (d) normality, (e) homoscedasticity, and (e) independence of residuals (Leo, 2013). Violations of these assumptions will require data transformations as a minimum (Miranda, 2015).

In linear regression, an outlier is an observation in which the value of the dependent variable is unusual and contains high residuals (Miranda, 2015). According to Leo (2013), the best way to address outliers is to examine a scatter diagrams and residuals of each variable. Accordingly, I will use a scatter plot of all my study variables (Regression with SPSS, 2014).

I will also perform the assumption test for multicollinearity. Multicollinearity is an adverse situation whether the correlations between the independent variables are very strong (Regression with SPSS, 2014). If a strong correlation exists between stock options and firm performance, these variables would convey the same information and regression results would have been paradoxical (Miranda, 2015). I will use the Variance Inflation Factors (VIF) test to flag for multicollinearity (Leo, 2013). A VIF result score of 1, means no strong correlation between the independent variables. If the VIF score is 10 or above, I would need to remove one of the study variables (Pham, 2015).

In linear regression, an assessment of the normality of the data is essential because of the underlying assumptions that the data is normally distributed (Regression with SPSS, 2014).I will use Shapiro-Wilk tests in SPSS to determine the normality of the data (Miranda, 2015). If sig. value of the test greater than 0.05, the data would be considered normal (Klimberg & McCullough, 2013). However, a sig. value is below 0.05, a significant deviation possibly exist from normal deviation. I will also use boxplots and scatter plots before making a final determination (Leo, 2013).

Homoscedasticity, also known as homogeneity of variance, assumes that the dependent variable exhibits similar amount of variance across the range of the independent variables (Miranda, 2015). In homoscedasticity, the error variance would be constant between the variables (Regression with SPSS, 2014). To test for homoscedasticity, I will conduct a scatterplot graph (Leo, 2013). Ideally, residuals randomly scattered around the horizontal line, means a relatively even distribution (Kiecolt & Nathan, 2015).

Issues of independence of residuals can be very serious (Regression with SPSS, 2014). Independence of residuals is when errors of one observation are not in correlation with errors of other observations (Miranda, 2015). Independence of residuals is a problem for time-series data (Leo, 2013). I will use the Durbin-Watson test in SPSS to look for serial correlation (Regression with SPSS, 2014). The Durbin-Watson test ranges from 0 to 4. The residuals are uncorrelated when the Durbin-Watson test is approximately 2 (Miranda, 2015). A value close to 0 indicates strong positive correlation, while a value of 4 indicates strong negative correlation (Regression with SPSS, 2014).

The hierarchical regression will include stock options awarded to CEOs as the dependent variable to predict the performance of firms after controlling for the size of the firm, the age of the CEO, and tenure of the CEO. Control variables will be entered into SPSS before firm performance variables to ensure that the controls do not explain away the entire association between firm performance and CEO stock options awarded. I will exclude cases pairwise to detect missing data, and listwise will delete any entries with missing data.

Researchers encounter missing information that may occur for reasons not anticipated (Pham, 2015). For this study, I will only analyze companies with complete financial data. The most common method and the easiest to apply is the use of only those cases with complete information (Leo, 2013). Leo stated that using only cases with available data has simplicity and comparable across analysis. However, according to Leo this reduces statistical power because it lowers Nand does not use all information. Nevertheless, I have a large sample to select from to mitigate loss of statistical power from a lower N.

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Appendix A: Summary Compensation Table

Appendix B: Summary Compensation Table for Pfizer

Position	Year	Salary	Bonus	Stock Awards	Option Awards	Non-Equity	Deferred	All Other	Total
CEO	2014	1,815	-	6,447	6,361	3,000	5,266	391	23,023
CEO	2013	1,776	-	6,016	6,066	3,400	1,212	476	18,948
CEO	2012	1,737	-	6,441	6,497	3,400	7,147	409	25,634

Note. Pfizer CEO Compensation received from 2012 to 2014 in thousands.

Appendix C: G*Power for a Priori Analysis for a Pearson Correlation Model

Appendix D: G*Power for a Priori Analysis for a Pearson Correlation Model

Appendix E: Sample of Standard and Poor’s Capital IQ

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