Module 3 Flashcards
(36 cards)
Whats Providing a literature review
a literature review summarises the existing research on a topic,
The purpose of a literature review is to provide a condensed overview of the key studies on a particular topic.
The idea is similar to to the phrase standing on the shoulders of giants,” coined by Isaac Newton.
an example:
For example, ‘’Results of prior work have been conflicting. While A (2017) concluded that there is a large positive effect on …, study B (2018) found no effect. Study C (2018) and D (2012) concluded that the effect is negative. These differences might be due to differences in the type of firms that were investigated. Specifically, ….’’
what is A conceptual model
Is a representation of the research problem that shows the variables and relationships
Relationships
visualised by arrows ,
can pertain to the moderating effect or the main effect.
Dependent variable:
is what we try to explain or predict
(Typically denoted by a Y , also known as DV or criterion)
Independent variable:
Is a variable that effects the dependent variable positively or negatively. AKA predictor variable or IV
Mediating variable:
Is a variable that explains the process that underlies the relationship between X and Y variable (other names: mediators or interviewing variables)
- Basically provides an answer to the question why or how does the X (independent variable) effect the Y (dependent variable)
Moderator Variable:
A variable that changes the strength and sometimes even direction of the relationship between X ( independent variable) and Y (dependent variable)
(AKA moderator, interaction variable)
how can The unit of analysis of a study can be deduced
from the dependent variable variable.
Two literature review writing tips:
Never describe studies chronologically: The key to writing an interesting literature review is structure. Describing studies chronologically is NOT a good way, as it tends to be enumerative (and boring to read …). It is more meaningful to structure a literature review around relevant themes about your subject that help to highlight relationships between studies, as well as controversies and/or gaps.
Never argue that a literature review is not needed because the topic has never been researched before: If the topic you want to study has never before been examined in prior research, you will want to find inspiration in studies on similar topics. Then, draw on your reasoning powers to identify the most important common elements.
When writing a Conceptual model
Before you visually show the conceptual model, briefly discuss the general thrust. In doing so, ensure you include a formal definition of each variable. As you should always try to build on prior research, a variable definition should be based on the literature.
Sometimes, you may come across different definitions in the literature. In such instances, we advise you to first acknowledge the major differences between the various definitions. Subsequently, you can either focus on the shared meaning across definitions.
Alternatively, you can pick one definition – provided it comes from a reputable source – and justify why you will use this definition (and not the others) in your research.
It is always recommended to include a figure that represents all the variables and relationships that are the subject of your study.
Five tips for a conceptual model:
- Never define your variables one after the other: Instead, integrate the definitions in the text
that briefly describes your conceptual model. - Never use synonyms for your variable names: You are not writing a novel but a scientific
report. Using exactly the same variable names throughout your report provides clarity. - Never define a variable by copy-pasting the first definition you encounter in the literature:
Define your variables carefully. Make an overview of the various definitions that are offered
in the literature and proceed with the shared meaning. Alternatively, justify why a specific
definition fits your study best. - A variable cannot be defined by using examples: You first need to provide a formal definition,
after which you can supplement that definition with examples. Examples can never replace a
reference. - Never strive for complexity: A conceptual model must be parsimonious. It must be simple
enough to be readily applied. If it is very complex, it becomes difficult to derive explicit
predictions about real-world events from it.
Whats a Research Hypothesis:
A research hypothesis is “a tentative statement about the coherence between two or more variables.”
- Let’s review the various elements of this definition.
A research hypothesis is a tentative statement. This means that a research study will test, using data, whether this statement is sound.
A research hypothesis is about the coherence, or the relationship, between variables.
A research hypothesis pertains to two or more variables. A main-effect hypothesis is about the relationship between two variables. A mediator and a moderator hypothesis are about the relationship between three variables.
Directional hypotheses:
indicate the expected direction of the relationship; is the expected association positive or negative?
For example:
“Higher workloads are associated with lower employee morale”
is a directional hypothesis. The hypothesised main effect is negative.
Non-directional hypotheses:
expect a relationship, but they do not indicate the direction.
For example:
“Workloads are associated with employee morale.”
is an non-directional hypothesis. The hypothesis does not indicate whether the main effect is likely to be positive or negative.
Why start with research hypotheses?
Whether a study starts with research hypotheses or not, it will of course produce the same empirical findings. However, without research hypotheses, these empirical findings could be a mere coincidence.
What makes a good research hypothesis?
First, a research hypothesis must be testable. This means it should be phrased in terms of (measurable) variables.
Second, a research hypothesis should not be based on your gut feeling but must be justified using logical arguments based on prior (high-quality) research studies.
Are hypothesis proven or used for
Finding support for a hypothesis
Hypotheses are neither true nor false in an absolute sense. You can never claim that you have proven a hypothesis. The word prove is not used in business science. A single confirming finding can never prove a hypothesis.
Instead of saying “proven,” we say that a hypothesis is supported by (or consistent with) or not supported by (or not consistent with) the data. A hypothesis is not right; it is simply not proven wrong.
- Choosing variable names
Before formulating your research hypotheses, you must decide on the names you will use to refer to your variables. Variable names should
(i) not overpromise,
(ii) leave no room for ambiguity,
(iii) be short.
i) Variable names should not overpromise
In choosing a variable name, you should make sure it is to the point and does not overpromise.
Example: When you study the effect of a green vs. blue package colour on the sales of a sustainable product, the variable name “package colour” overpromises: it insinuates that you study many more colours than just green and blue. A name that is more to the point is: “green vs. blue package colour.”
ii) Variable names should be unambiguous
In choosing a variable name, you should make sure it is unambiguous. Avoid variable names that can be interpreted in multiple ways.
Example: The variable name “preference” is ambiguous. Preference related to what? The variable name should also indicate what the term preference refers to (e.g., product preference).
iii) Variable names should be short
At the same time, you should try to make your variable names as short as possible, in the interest of readability. As you will be referring to your variables repeatedly in your research report, very long names will become a nuisance.
Example: Instead of the variable names “size of a firm” and “attitude toward a brand,” more suitable variable names are the crisper “firm size” and “brand attitude.”
- Guidelines to formulate research hypotheses
A research hypothesis proposes a relationship between two (or more) variables. The correct formulation of a hypothesis differs depending on the type of variables involved. An important distinction is whether a variable is metric or categorical.
A metric variable: captures a quantity. For example, household size (the number of persons in a household) or product sales are metric variables.
A categorical variable: has different “levels” or “categories” that are not ordered along an underlying dimension. For example, color is a categorical variable with the levels “blue”, “red”, etc.
A main-effect hypothesis:
when both the DV and IV are metric
When one of the variables is categorical rather than metric,
hypotheses need to be worded slightly differently. A categorical variable has different “levels” or “categories” that are not ordered along an underlying dimension. For example, color is a categorical variable with the levels “blue”, “red”, etc. (there is no order along an underlying dimension as red is not “better” or “more” than blue).
When Main-effect hypotheses when the IV is categorical and the DV is metric
Example: A research study investigates whether there is a difference in earnings between men and women. Gender is a categorical variable with two levels (men vs. women), while earnings is a metric variable (it is expressed in Euros).
A directional hypothesis about the relationship between gender and earnings could read:
H: Men earn more than women.
When Main-effect hypotheses when the DV is categorical and the IV is metric
Example: A researcher is interested in the effect of downsizing on bankruptcy. Bankruptcy is a categorical variable with two levels: a firm is either bankrupt or it is not. Downsizing is a quantitative variable (note: downsizing is defined as making a company smaller by firing employees and can therefore be measured as the percentage of employees being fired).
A directional hypothesis can, e.g., be expressed as:
H: When downsizing increases, the likelihood of bankruptcy increases.
H: Downsizing is positively related to the likelihood of bankruptcy.
H: Downsizing is positively associated with the likelihood of bankruptcy.
- How to word a mediator hypothesis
Expectations about a mediator effect can be formulated using two main-effect hypotheses
The first hypothesis pertains to the relationship between the independent variable X and the mediator MED
The second hypothesis concerns the relationship between the mediator MED and the dependent variable Y.
For three metric variables, the hypotheses could be formulated as follows:
H1a: When X increases, MED increases/decreases. H1b: When MED increases, Y increases/decreases.
