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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, ...