Explain the concepts related to business research methods and statistical courses, and contact the two articles given.

Business Finance

Business Research Methods and Statistics

Brock University

Question Description

Q1:Read the “JSBM” article. Explain how four concepts/topics (e.g., two-tailed hypothesis, moderating effect, etc. – these two examples are not necessarily relevant, they are just examples that I give here) that we covered in the course were applied in this article. [For example, if you selected ‘moderating effect’, then you would first explain what the meaning of a moderating effect is in general, and then explain (in your own words) the specific nature of a moderating effect in the context of the article’s theoretical framework.]

Q2:Read the “JBR”. Explain how four different concepts/topics (not discussed in Question 1) that we covered in the course were applied in this article.

I have organized the concepts in class and provided some explanations to help you understand them. You can choose any eight to answer.(total 4 page) Use the complete 1/2 page per concept, single space, front size 12

If you need any notes about the class, please contact me.

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Predictor variable. A variable (IV) from which a value is used to estimate a value on another variable (DV) Criterion variable. A variable (DV) a value of which is estimated from a value of the predictor variable (IV) Line of Best Fit Least Squares Solution This is the model that minimizes the sum of the squared deviations from each point to the regression line. The regression line defined by the least squares model is the line of best fit. Simple Linear Regression The Pearson Product-Moment Correlation Coefficient represented by the letter r tells us how widely scores in the scattergraph are distributed around the regression line (line of best fit). The larger the absolute value of r, the closer the fit between the scores and the regression line and the better prediction we get of Y. Coefficient of Determination Multiple Regression A technique for estimating the value of the criterion variable (Y) from values on two or more other predictor variables (X’s) Multiple Correlation (R) a measure of the correlation of one dependent variable with a combination of two or more predictor variables. Coefficient of Multiple Determination is R2 Assumptions of Multiple Regression There are three types of Multiple Regression Standard Multiple regression Hierarchical Multiple Regression Stepwise Multiple Regression Correlation Correlation examines the strength of a connection between two characteristics belonging to the same individual, or event or equipment The concept of correlation does not include the proposition that one thing is the cause and the other the effect We merely say that two things are systematically connected Relationships Two variables can be positively correlated - an increase in one variable coincides with an increase in another variable, e.g. the more electricity used the higher the power bill. A negative correlation - when one variable increases as the other decreases, e.g. as price increases, demand decreases. A zero or random correlation - when variations in two variables occur randomly, e.g. number of accountants graduating per year with total annual attendance at national football league matches Correlation coefficient (R) The Correlation is measured by the correlation coefficient which is usually designated as ‘r’. Correlations (r) range from +1.00 perfect positive to -1.00 perfect inverse with a midpoint 0.00 indicating absolute randomness Coefficient of Determination The Coefficient of Determination (r2) represents the proportion of variation in one variable which is explained by the other. PARTIAL CORRELATION Partialling out a variable is used when you wish to eliminate the influence of a third variable on the correlation between two other variables. It simply means controlling the influence of that variable. Other terms for partialling out are ‘holding constant’, and ‘controlling for’. The partial correlation coefficient, which, like other correlations takes values between -1 and +1, is essentially an ordinary bivariate correlation, except that some third variable is being controlled for. Chi Square Chi Square is the most common and simple non-parametric test of significance investigating associations between categories of nominal variables where observations can be classified into discrete categories and treated as frequencies. Goodness-of-Fit Chi Square A goodness-of-fit test - how well does an observed distribution fit a hypothesized or theoretical distribution Cross-tabulations (contingency tables) CONTINGENCY AND CROSS-TABULATION TABLES ANALYSIS OF VARIANCE TECHNIQUES (ANOVA) ANOVA is used when we want to know if two or more conditions or levels of the independent variable create significant mean differences on the dependent variable. F ratio ASSUMPTIONS of ANOVA Normality - ANOVA is fairly robust for departures from normality as long as they are not extreme Homogeneity of variance - This similarity of variance in each group is needed in order to ‘pool’ the variances into one Within Group source of variance Independence of errors - Error here refers to the difference between each observation or score from its own group mean. In other words each score should be independent of any other score INDEPENDENT GROUPS ANOVA Post Hoc Test A Post Hoc Test is conducted after a significant F test in order to identify where significant differences lie among three or more treatments Repeated measures analysis of variance Analysis of Variance in which each individual is measured more than once so that the levels of the independent variable are the different times or types of observations for the same people TWO FACTOR ANOVA or TWO WAY ANOVA or FACTORIAL ANOVA When two factors of interest are to be examined at the same time Tests a null hypothesis for each of the independent variables and also one for their interaction, the interaction effect Interactions occur when the effect of 1 IV on the DV is not the same under all the conditions of the other IV. FACTORIAL ANOVA ANALYSIS OF COVARIANCE (ANCOVA) a form of analysis that is based on a combination of regression and ANOVA Validity Validity of a measure: The degree to which a measure actually measures what we think it measures. External validity - capacity to generaliz from sample result to population Internal validity - can the research design actually provide the answer to the question asked Reliability Reliability of a measure: The consistency of a measure over time situations. or under similar In social, or business research we cannot expect any measure to be perfectly valid or perfectly reliable. The best we can do is to design our measures to be as valid and reliable as they can be and where feasible run some statistical tests to evaluate their levels of reliability and validity. Test-retest reliability The degree to which the measure returns the same value from the same respondents on a second occasion. Temporal reliability or stability over time Parallel-form reliability When two equivalent (but different) forms of a measure are developed (e.g. two equivalent forms of a single personality scale are developed), it is the degree to which the same value is returned from the same respondents for the two different equivalent forms. Split-Half or Inter-Item Method A measure of reliability reflecting the degree to which one half of the items is the same as that for the other half of the items (like a test-retest, but can be performed on a single occasion). Problem – how do we determine which items should be in each half? Often worked on the odd-even items on a scale/test. Internal Consistency Method (Cronbach’s Alpha) A measure of reliability that is equivalent to the mathematical average of all possible split half combinations. Descriptive statistics - summary data describing what is rather than providing an explanation e.g. average weekly wage; number of unemployed persons per month in 2007. Often conducted on a regular basis to monitor social and economic change. E.g. census, government statistics on unemployed persons or import-export data. Inferential statistics – used when we try to extrapolate our findings from a sample (shown by descriptive statistics) to a population. Essentially they provide an estimate of the probability that a pattern found in the sample will also be found in the larger population. for example, they allow the manager to draw better inferences as to whether a phenomenon such as work satisfaction, or relative demand among competing brands, measured in a sample, can be legitimately generalized to a population. Applied research – carried out with the intention of finding and applying solutions to specific problems in the business organization. Basic research – carried out to enhance the understanding of problems or develop theory that commonly apply across a range of organizations. Often new and original findings. Positivist paradigm Objective world with universal laws and causality. Value free. Uses precise, objective measures and usually associated with quantitative data. Researcher remains separate from the subjects. Research is rigorous, linear and rigid based on hypothesis testing Methods include experimental studies, re-analysis of secondary data, questionnaires, structured interviews Mostly uses deductive reasoning Generally a ‘scientific’ method implied Interpretivist paradigm People experience physical and social reality in different ways Reality is therefore socially constructed Focus on meaning of experience for individual Researcher becomes fully involved with individual subjects Subjective with values made explicit Research process flexible and flows from the material provided by participants Methods include ethnography, participant observation, focus groups, depth interviews - generally inductive Induction Develops theory from initial data – a bottom-up approach. This is open-ended and exploratory, major characteristics of the qualitative interpretive approach. Deduction Starts with a theory or hypothesis from which certain other things should logically follow. These implications can be tested and on the basis of the results the initial theory/hypothesis can be supported or rejected. This process is the deductive process – a top-down strategy, working from the general to the specific. Moderating variable affects the nature of the relationship between the independent and dependent variables. Intervening variable (IVV) This class of variable intervenes between IV and DV. It is affected by the IV and therefore impacts on the DV. Measures of Central Tendency Nominal scale: the mode is the only legitimate statistic to use. Ordinal scale: median preferred over the mean which could be distorted by an extreme score interval and ratio scales (grouped together as Scale level in SPSS): the mean is the recommended measure of central tendency, median & mode may also be reported for these types of scales Distributions 1. Skewed distributions negatively skewed curve positively skewed curve bi-modal distribution 2. Normal distribution or Gaussian curve Z Distribution (or standard normal distribution) is a normal distribution which has a mean of zero and a SD of one. The Z’s along the baseline are the SD’s. level of significance Statistical Significance Hypothesis A proposition, tentative assumption, or educated conjecture about some aspect of the world around us that is testable. Usually derived from theoretical framework in quantitative approach Hypothesis Testing - An inferential procedure that uses sample data to evaluate the validity of a hypothesis about a population. That is: hypotheses relate to populations but we usually test them with samples. Null Hypothesis – symbolized as Ho Ho: The finding was simply a chance (random) occurrence – nothing really occurred Alternate Hypothesis – symbolized as H1 H1: The finding did not occur by chance but is real (alternative version) – something did occur The null hypothesis is assumed to be true unless we find evidence to the contrary which then allows us to assume the alternate hypothesis is more likely correct. Stating the hypothesis Hypotheses may be stated in the form of proposed relationships (associations) or in terms of differences (comparisons). A relationship hypothesis would exist if we propose ‘that changes in demand for a specified good are related to changes in price of the same good’. A difference hypothesis is ‘that female employees take more sick days than male employees’. Statistically significant difference Significance level is often termed the alpha level. The significance level offers a probability level for our evidence to be unreasonable as a chance result. A minimum decision criterion of p < 0.05 (5% level for two-tailed tests) is recommended. BUT a chance result at this level will occur 5% of the times. Assumptions Selecting an appropriate test for analysing hypotheses of difference depends on a number of important assumptions particularly relating to parametric and non-parametric assumptions Parametric tests Tests based on assumptions about population distributions and parameters The assumptions for parametric tests interval or ratio level data normal distribution or closely so homogeneity of variance - the variance (standard deviation squared) should be similar in each group samples randomly drawn from the population Non-Parametric Tests Tests that make no assumptions about population parameters or distributions T - Test t Tests are parametric tests and assume normal distribution or approximately so, random selection of sample elements, homogeneity of variance or approximately so and scale data measurement. Three main types of t test exist: 1. The one sample t test 2. The independent group t test between two separate random samples 3. The repeated (or paired) measures t test between two testings of the same sample or between two paired samples Testing hypotheses for single samples tests the null hypothesis that the mean of a particular sample differs from the mean of the population only by chance One sample t test and Z test Confidence intervals for a nominated level of probability provide a range of values within which the population mean is likely to lie. Using the same sort of logic, a different approach is a one sample test. In a one sample test, you nominate a known or possible population mean and you conduct the test to determine whether it is likely (with a nominated level of probability) that the mean value you have obtained from your sample could have come from the population with the nominated population mean. The Standard Error of the Difference The standard error of the difference is the standard deviation of the distribution of differences between every possible pairings of sample means when each pair is formed from one sample mean taken from each population DEGREES OF FREEDOM Abbreviated to df The number of values free to vary in a set of values Used to evaluate the obtained statistical value rather then N df usually = N – 1 per sample (group) Assumptions of the Test measurements are on interval scale subjects are randomly selected from a defined population the variances of the scores for the two samples or occasions should be approximately equal the population from which the samples have been drawn is normally distributed. Independent Samples Tests (Between Groups Design) Related Samples tests (Repeated and Paired Measures Design) Prediction Regression Journal of Business Research 89 (2018) 27–36 Contents lists available at ScienceDirect Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres Family incivility, emotional exhaustion at work, and being a good soldier: The buffering roles of waypower and willpower T ⁎ Dirk De Clercqa, , Inam Ul Haqb, Muhammad Umer Azeemc, Usman Rajaa a Goodman School of Business, Brock University, St. Catharines, Ontario L2S 3A1, Canada Lahore Business School, The University of Lahore, Lahore, Pakistan c School of Business and Economics, University of Management and Technology, Lahore, Pakistan b A R T I C LE I N FO A B S T R A C T Keywords: Organizational citizenship behavior Family incivility Emotional exhaustion Waypower Willpower Conservation of resources theory This study unpacks the relationship between family incivility and organizational citizenship behavior (OCB), suggesting a mediating role of emotional exhaustion and moderating roles of waypower and willpower, two critical dimensions of hope. Three-wave data from employees and their peers in Pakistani organizations show that an important reason that family incivility diminishes OCB is that employees become emotionally overextended by their work. Employees' waypower and willpower buffer this harmful effect of family incivility on emotional exhaustion though, such that this effect is mitigated when the two personal resources are high. The study also reveals the presence of moderated mediation, such that the indirect effect of family incivility on OCB through emotional exhaustion is weaker for employees high in waypower and willpower. For organizations, this study accordingly identifies a key mechanism by which family adversity can undermine voluntary behaviors; this mechanism is less forceful among employees who are more hopeful though. 1. Introduction Previous studies emphasize the need to examine ways to stimulate employees' propensity to undertake organizational citizenship behaviors (OCB), positive work behaviors that are not required by formal job descriptions, often referred to as being a “good soldier” (Organ, 1988; Podsakoff, MacKenzie, Paine, & Bachrach, 2000). Such behaviors benefit both organizations and employees, because when employees engage in voluntary work efforts, they improve their organizations' wellbeing and competitive advantage (Podsakoff, Whiting, Podsakoff, & Blume, 2009) and also improve their own position, in that their performance appraisals often are based on such efforts (Lievens, De Corte, & Schollaert, 2008). Although OCB sometimes entails helping behaviors targeted at individual members, which can contribute to organizational well-being indirectly (Deckop, Cirka, & Andersson, 2003), the focus of the current study is on voluntary work behaviors that contribute to the organization directly, such as work attendance above the norm, voluntary adherence to informal rules that increase organizational effectiveness, and a strict focus on job-related issues instead of personal matters during work hours (Spitzmuller, Van Dyne, & Ilies, 2008; Williams & Anderson, 1991). In light of the positive outcomes of OCB, previous studies examine a plethora of enabling factors, such as transformational leadership (López-Domínguez, Enache, Sallan, & Simo, ⁎ 2013), perceived organizational justice (Cohen-Charash & Spector, 2001), constructive feedback (Sommer & Kulkarni, 2012), and positive job attitudes (Bowling, Wang, & Li, 2012). Despite the many positive consequences of OCB, such behavior does not emerge automatically but instead requires significant personal investments of time and energy (Quinn, Spreitzer, & Lam, 2012; Trougakos, Beal, Cheng, Hideg, & Zweig, 2015). Notable in this regard is that employees' exposure to stressful situations may deplete their energy resources that otherwise would be available for OCB (Hobfoll, 1989). Accordingly, previous studies show how negative work conditions, such as role stress (Eatough, Chang, Miloslavic, & Johnson, 2011; Rodell & Judge, 2009), work overload and interpersonal conflict (Pooja, De Clercq, & Belausteguigoitia, 2016), despotic leadership (Naseer, Raja, Syed, Donia, & Darr, 2016), and psychological contract violations (Priesemuth & Taylor, 2016), might steer employees away from OCB. Relatively less research investigates how OCB can be inhibited by stressful situations outside the workplace though, with the exception of research on the harmful effect of family-to-work conflict (Amstad, Meier, Fasel, Elfering, & Semmer, 2011). This oversight is important; to the extent that hardships experienced at home negatively interfere with organizational functioning, employees' propensity to allocate resources to voluntary activities may be thwarted (Leiter & Durup, 1996). Discretionary work efforts that are not formally required usurp significant Corresponding author. E-mail addresses: ddeclercq@brocku.ca (D. De Clercq), umer.azeem@umt.edu.pk (M.U. Azeem), uraja@brocku.ca (U. Raja). https://doi.org/10.1016/j.jbusres.2018.04.002 Received 10 August 2017; Received in revised form 2 April 2018; Accepted 4 April 2018 0148-2963/ © 2018 Elsevier Inc. All rights reserved. Journal of Business Research 89 (2018) 27–36 D. De Clercq et al. draining, negative factors may prevent employees' OCB. In the few studies that include negative factors, the focus is mostly on the workplace instead of the family sphere (e.g., Eatough et al., 2011; Pooja et al., 2016). In contrast, ...
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