Samenvatting
Summary Methdology in Marketing and Strategy Research
Summary of all the lectures, video clips, relevant chapters of the book, and the four assignments.
[Meer zien]Voorbeeld 4 van de 58 pagina's
Voorbeeld 4 van de 58 pagina's
In winkelwagengesponsord bericht van onze partner
1 beoordeling
Door: gielsimons • 11 maanden geleden
Voorbeeld van de inhoud
Methodology in Marketing andStrategy Research Lectures
Lecture 1
Introduction lecture of the course
Assignments will in total yield 0.2 bonus points if done above average
Second part of the lecture
Construct
- Definition: a conceptual term used to describe a phenomenon of theoretical interest. All
constructs should have a clear definition
- A construct is quantifiable and directly/indirectly observable.
- An indirectly observable construct is called latent
- Examples from strategy research: firm success, competitive advantage, innovation
performance
Construct must be defined in terms of
- Object – something that is looked at
- Attribute – it needs to describe something
- Rater entity – the level on which you look at the construct – e.g. in the example of team
diversity, the rater entity is team.
Overview of relationships
- Direct causal relationships
- (fully or partially) mediated relationships (indirect) causal relationships
- Spurious relationships
- Bidirectional (cyclic) causal relationships
Direct causal relationships
- A leads to B. In social sciences, this usually implies a linear effect.
Mediated causal relationship
- A appears to have a direct effect on B. But they are not directly related but through a
mediator Z. A influences Z, Z influences B.
- Full mediation: effect of A on B is completely mediated by Z
- Partial mediation: A influences Z, Z influences B, but A still influences B directly (for a smaller
part)
Exogenous variable: a variable whose value is determined outside the model and is imposed on the
model.
,Endogenous variable: a variable whose value is determined by the model. The moment there is an
arrow on a variable, it is an endogenous variable.
Spurious relationship
- A third variable influences A as well as B
- Example: there is a relationship between drowning in pools and ice cream consumption.
Does ice cream then lead to drowning in pools? No, both are influenced by warm weather.
Bidirectional causal relationship
- A leads to B, and B leads to A. There is a feedback loop.
- But, this does not necessarily happen at the same time.
- Not so common in social science research because it’s hard to prove and difficult to entangle.
- Chicken and egg problem
Unanalysed relationship
- There is a correlation between A and B which is not analysed further.
Moderated causal relationship
- The strength and/or direction of the effect of A on B differs depending on the level of M.
- Variable M is a moderator
Theory of reasoned action
- Explains why we do something
- Norms and attitude lead to a certain intention, which in turn can lead to an action.
Greek letters: theory level
X and Y: observational level
Measurement model: first step, measuring all the constructs. Corresponding to the observation
Structural model: corresponding to the theoretical model, measuring the relationships between the
constructs.
Multi-item measurement
- In the example the different constructs have several items
- Using several items increases reliability and validiy of measures.
- It also allows for measurement assessment: measurement error, reliability, validity.
- Two forms of measurement models:
Formative (emerging)
Reflective (latent)
,Reflective measurement models:
- Direction of causality is from the construct to the measure
- The observation drives the measurement
- The indicators are expected to be correlated
- Dropping an indicator from the measurement model does not alter the meaning of the
construct
- Similar to factor analysis
Formative measurement models:
- Direction of causality is from measure to construct
- No reason to expect indicators to be correlated
- Dropping an indicator from the measurement model may alter the meaning of the construct
- Based on multiple regression
- Important to have all possibilities in your measurement, e.g. in indexes. Think about the
drunkenness example: if you only measure wine & liquor consumption and someone only
drank beer, you may think someone is not drunk.
Technique Consultation Factor Analysis
, Principal components analysis
- Takes into account the total variance
- Example, you have 5 variables. Then PCA looks into the total variance of these 5 variables.
- If your goal is to explain as much variance as possible, you would use PCA. It is more
exploratory way.
- Primary concern: minimum number of factors that account for the maximum of variance.
- Factors are called principal components.
- Mathematically, each variable is expressed as a linear combinations of the components. The
covariation among the variables is described in terms of a small number of principal
components.
- PCA standardizes the variables – standardized regression coefficient.
Common factor analysis
- Factors are estimated based only on the common variance. So not the total amount of
variance is taken into account. We speak of communalities.
- Common item variance is the variance one item shares with all other items included.
- Primary concern: identify underlying dimensions and their common variance.
- Mostly used in research in which there is also prior knowledge, so that could be in a thesis.
- In SPSS this is called principal axis factoring.
SPSS Example – Principal components analysis
- All variance has been extracted. You see communalities all of 1.
SPSS Example – Common factor analysis
- Extraction is the variance shared by variables. You see these are not only 1.
Initial communalities – based on maximum number of factors possible, extracted communalities are
based on the numbers of factors that can be extracted (e.g. factors with an eigenvalue of > 1)
Factor loading: the correlation between factor and item.
Main differences between PCA and CFA
- Amount of variance they take along
- Different purposes. PCA is exploratory, CFA is more confirmatory in that there is prior
knowledge.
Factor rotation
- Why do we rotate at all?
We move the factors closer to the real observations, in order for the results to be more
interpretable.
- Orthogonal rotation is the default rotation.
Voordelen van het kopen van samenvattingen bij Stuvia op een rij:
Verzekerd van kwaliteit door reviews
Stuvia-klanten hebben meer dan 700.000 samenvattingen beoordeeld. Zo weet je zeker dat je de beste documenten koopt!
Snel en makkelijk kopen
Je betaalt supersnel en eenmalig met iDeal, creditcard of Stuvia-tegoed voor de samenvatting. Zonder lidmaatschap.
Focus op de essentie
Samenvattingen worden geschreven voor en door anderen. Daarom zijn de samenvattingen altijd betrouwbaar en actueel. Zo kom je snel tot de kern!
Veelgestelde vragen
Wat krijg ik als ik dit document koop?
Je krijgt een PDF, die direct beschikbaar is na je aankoop. Het gekochte document is altijd, overal en oneindig toegankelijk via je profiel.
Tevredenheidsgarantie: hoe werkt dat?
Onze tevredenheidsgarantie zorgt ervoor dat je altijd een studiedocument vindt dat goed bij je past. Je vult een formulier in en onze klantenservice regelt de rest.
Van wie koop ik deze samenvatting?
Stuvia is een marktplaats, je koop dit document dus niet van ons, maar van verkoper StudentRadboud34512. Stuvia faciliteert de betaling aan de verkoper.
Zit ik meteen vast aan een abonnement?
Nee, je koopt alleen deze samenvatting voor €3,48. Je zit daarna nergens aan vast.
Is Stuvia te vertrouwen?
4,6 sterren op Google & Trustpilot (+1000 reviews)
Afgelopen 30 dagen zijn er 71184 samenvattingen verkocht
Opgericht in 2010, al 14 jaar dé plek om samenvattingen te kopen