quantitative research approach professor yvonne brunetto, professor stephen teo, professor jarrod...
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Quantitative research approach
Professor Yvonne Brunetto,
Professor Stephen Teo,
Professor Jarrod Haar
Three basic criteria determines which method to use
1. The type of research question. E.g “Why” and “How” questions are ideally suited to a case study approach.
2. The extent to which the researcher has control over the subject and context of the subject e.g., The experimental approach is more suitable for controlled
environments, whereas the case study approach is better if you can’t control subjects or the context
3. The period of history under examination influences the choice of methodology. E.G if you want to study an historical phenomena ,then you have to use archives information
The Need for a Framework
The conceptual/theoretical framework is a logically developed, described and elaborated network of associations among concepts or variables deemed relevant to the problem situation.
The basis of quantitative research: Central Tendency
Across any one SAMPLE there will typically be a standard distribution of a particular property, for example it is said that intelligence across a country’s population will adhere to a standard distribution.
Populations and samples that adhere to a standard distribution will have a majority of participations existing in the centre of any distribution – and this frequency will decrease as the deviations get closer to the extremes at any end of the distribution
Frequency
Theoretical Framework 1. Types of variables
• Dependent• Independent• Moderating• Intervening
2. Components of Theoretical Framework
Concepts and VariablesA Concept
• an idea expressed as a symbol or in words
• Conceptual framework
A Variable
• Can be observed and measured• Theoretical framework
Dependent variable
• A dependent variable is a measurable outcome of an experiment.
– For example communication satisfaction, productivity, number of sick days, employee morale, or organisational commitment could be dependent variables. There is a clear advantage if the dependent variable is easily measured; sick days are much easier to measure than employee morale.
Independent variable(s)• An independent variable or treatment variable
represents a quality or characteristic that is varied or manipulated during the experiment. – examples include quality of feedback, training
methods, remuneration, or work hours. The independent or treatment variable is manipulated to determine the effect on the dependent variable. This process is the treatment received by the participants. The group receiving the treatment is referred to as the treatment or experimental group.
Attitude**
Subjective Norm**
Perceived Behavioral Control**
Trust in Social Network
Behavioral Intention
EC Adoption**
TAM Beliefs
Dr Apivut Chakuthip thesis
Theoretical Framework
Social Exchange
Theory
Social Exchange
Theory• Procedural
justice• Interactional
justice• LMX• Trust• Organisational
culture• Tie Strength
• Procedural justice
• Interactional justice
• LMX• Trust• Organisational
culture• Tie Strength
Affective Commitment
Affective Commitment
Intention to turnover of nursing professionals
Intention to turnover of nursing professionals
Innovative Behaviour
Innovative Behaviour
• POS (Perceived organisational support)
• POS (Perceived organisational support)
OCBOCB
Dr Matt Xerri ‘s Theoretical framework
Independent variables Dependent Variables
Training and Development
CommunicationProcesses:
• Frequency• Informal• Indirect• Two-way
Professionalism Dimensions:
• Referent• Self-Regulation• Autonomy
Job Satisfaction
Employee Performance
Affective Commitment
Organisation
Occupation
Customers
AmbiguityRegarding Customers
AmbiguityRegarding Customers
IndependentVariables
Moderator DependentVariables
An example of a conceptual framework
By Dr Natasha Currant
Using SEM for your research
Professor Stephen TeoManagement Department
AUT Business School
What is SEM [structural equations modelling]? A technique for testing theoretical models Researcher specifies their model and how the various
constructs should influence each other Hoyle’s (1994) review tells us that SEM can address:
Questions about causal process Basic questions of measurement Questions about causal process when variables are not well
measured SEM methods share most of the strengths of OLS
multiple regression SEM tests this model statistically Incorporates the features of factor analysis and regression
analysis
Strengths Proposed causal explanations are made explicit Tests of fit allow implausible models to be rejected Competing models can often be compared, and one
may emerge as more plausible given the data.Limitations Models are often mis-specified (needs theorizing):
Linearity assumption is often made uncritically Measurement error distorts analysis Important variables may be missing
Communicating results is challenging Novices may overstate claims or make errors in
complex analyses that are difficult to detect
Covariance-based SEM
Model Data
Question to ask during the modelling process Could this model have led to the data that I have? That’s why in AMOS, Mplus and even PLS, we
refer to goodness of fit indices to show model fit (eg. χ2/df=2.076, CFI=.95, TLI=.94, RMSEA=.04, SRMR=.07
JobSatisfaction
WLB
ACR2=41.3%
.23***
(.12*)
Figure 2a. Results of Analysis using Mplus (Female)
InfoProvision
Influence
Involvement
.31***
.29***
(.06*)
.09**
.55***
Goodness of fit: 2 /df=1.77, RMSEA=0.032, CFI=0.991, TLI=.988, SRMR=0.021
Source: Ravenswood and Teo (2014) in AIRAANZ Conference
• As Yvonne mentioned, “Theory” is important and SEM, is a theory driven process– Theory is specified as a model
• Alternative theories can be tested– Specified as models (Q: simple or complex?)
Data
Theory A Theory B
Org Sys
HRMSystem
Ambidex
WorkAttitudes
OrgPerf
Source: Plimmer, Teo and Bryson (2014): using PLS
Strg Mgt
Strg HRM
Partial Least Squares (PLS) Modelling
PLS is a latent path model, a well-established technique for estimating path coefficients in causal modelling Statistical basis initially formed in the late 60s
through the 70s by econometricians in EuropeThe conceptual core of PLS is an iterative combination
of principal components analysis relating measures (each questionnaire item) to constructs (latent variables or factors), and path analysis permitting the construction of a system of constructs
Allows for the simultaneous testing of hypotheses, unlike multiple regression, within the same statistical analysis
Measurement (items within each construct) model (blue)
Structural (path) model (lines and circles)
2nd order
LV
1st order LV
Positives of PLS Modelling
Does not require normal dataAccept smaller sample sizes because “each causal
subsystem sequence of paths is estimated separately. … and is particularly suitable for studies in the early stages of theory development and testing…” (Johansson & Yip, 1994, 587)Min sample size 30 to 100 (Chin and Newstead 1999), but
Green (1991) has a set of ‘rule of thumb’ based on the number of ‘independent variables’ (predictors) in the model
Combined regression and factor analysis within the model (measurement model) in each “run”
Suitable for developing constructs and models for further testing
Negatives of PLS ModellingDoes not allow conventional test for goodness of fit as
per AMOSUnable to test for co-variance relationships between
variables (constructs)Not suitable for testing theory (and model)
Irrespective of which technique (AMOS or PLS), researchers must consider the threat of Common method bias (Podsakoff et al., 2003) post-hoc (Harman’s one factor test) or using method factor (see Rafferty & Griffin, 2004) or using longitudinal data (see example in Teo et al., 2013)
T2 Nursing Stress
T1 Change Information
T1 Admin Stressors
T1 Participation in change
T2 Job Satisfaction
T1 Role StressT2 Effective
Coping Strategies
H1
H3
H4
MediationH10
H7
Source: Teo et al. (2013) Journal of Nursing Management
Time 1 Time 2
Multi-Level Analysis
Professor Jarrod Haar
School of Management
Massey University (Albany)
What is Multi-Level?
• Multi-level represents a different level of analysis
• Single source data is typically analysed with more ‘simply analysis’ ie regression in SPSS, to SEM in AMOS or Mplus
• Fundamentally, the relations are on the same level ie an employee with more work-life balance has more job satisfaction. Thus,
Simple Linear Regression
Why Multi-Level?
• For when we explore relationships that are NOT on the same level
• This is because data is ‘nested’ e.g., it might be nested in teams, or follower data is nested under a specific leader
• This ‘nesting’ effect means ‘Simple Linear Regression’ is not sufficient – as it does not pick up and account for these ‘nested’ effects… thus, multi-level analysis might look like…
Multi-Level Linear Regression
Multi-Level?
• With the advances in technology (statistical programs) we can now readily conduct multi-level studies where previously these have been particularly difficult.
• Programs such as MlwiN, Mplus, and HLM• These approaches are advantageous (from a
theoretical contribution, empirical contribution) and thus are more readily received in journal publishing. But, the data is typically harder to get!
Multi-Level Examples• So, what would these relationships look like?• Well, in the Pure Sciences it might go from: cell to
organ to person to a family [thus: cell=neurochemistry; organ=ability to metabolize ethanol; person=genetic susceptibility to addiction; family=alcohol abuse in the home]
• In the Social Sciences (Management) we might explore: employees – teams – departments – divisions – organisational sites – firms in industries – etc etc…
Multi-Level Examples• Specific management examples:• Teams (collective) IVs (e.g., safety climate) to
individual outcomes (e.g., OCBs, Job Sat, Turnover Intentions)
• Individuals (e.g., personality styles) to Team Outcomes (e.g., team performance, team wellbeing)
• Leaders influence (e.g., transformational leadership style) to Team Outcomes (individual or collective/team)
Multi-Level Summary• Fundamentally, the issues and rules behind good
quantitative research simply apply to multi-level research. But good research needs context specific analysis – team data in SEM is not right!
• The data collection might be more onerous (e.g., team data is hard – mean majority typically!); and time demands (e.g., takes longer); issues of trust (e.g., leaders and their followers)…
• The trade off? You can get away with smaller sample sizes e.g. Spell et al. (2011) SGR (ABDC=A) had 42 teams (n=174 employees)