module 3 Flashcards

1
Q

providing a literature review

A

Each study builds on previous work. The purpose of a literature review is to provide a condensed overview of the key studies on a particular topic.

Are you familiar with the phrase “standing on the shoulders of giants”

The “standing on the shoulder of giants” metaphor is often used to acknowledge the work of others when undertaking research and, in particular, stresses the importance of literature reviews in scientific inquiry. That is also why google scholars homepage uses these words

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2
Q

The conceptual model

A

Whereas a literature review summarizes the existing research on a topic, a conceptual model visually summarizes the new study.

Specifically, the conceptual model explains which variables are included in a study (and justifies why these are included) and how they relate to each other

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3
Q

four types of variables

A

Dependent variables
Independent variables
Mediating variables
Moderating variables

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4
Q

Independent variable

A

Influences the dependent variable
- in a positive or negative way

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5
Q

Dependent variable

A

The variable of primary interest

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6
Q

Mediating variable

A

A variable that explains the mechanisms at work between X and Y

X –> MED –> Y

CEO communication style –> employee morale –> employee productivity

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7
Q

Moderating variable

A

A variable that alters the strength and sometimes even the direction (positive /negative ) of the relationship between X and Y

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8
Q

Moderating variable example

A

X – MOD –> Y

Advertising spending – recession (yes/no) –> sales

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9
Q

Control variable

A

anything that is held constant or limited in a research study.

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10
Q

Can a moderator variable have a direct effect on the direct variable

A

Yes

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11
Q

A fitting problem statement for a conceptual model where the moderator variable also has a direct effect on the Direct variable would be:

A

To what extent are X and MOD related to Y and how does the relationship between X and Y depend on MOD

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12
Q

Literature review

A

The key to writing an interesting literature review is structure. A good literature review is structured around relevant themes about your subject that help to highlight similarities between prior findings.

In a good literature review you synthesize existing studies rather than summarize them. That means that you should not just simply list all the articles findings about your subject, but you should integrate previous studies and create your own story

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13
Q

Never describe studies conologically

A

the key to writing an interesting literature review is structure. Describing studies chronologically is not a good way as it tends to be enumerative (boring). It is more meaningful to structure a literature review around relevant themes about your subject that highlight relationships between studies, as well as controversies and/or gaps

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14
Q

Never argue that a lit review is not needed because the topic has never been researched before

A

If the topic you want to study has never 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

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15
Q

Before you visually show the conceptual model:

A

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.

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16
Q

Never define your vairables one after the other

A

Instead integrate the definitions in the text that briefly describes your conceptual model

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17
Q

Never use synonyms for your variable names

A

You are not writing a novel but a scientific report. Using exactly the same variable names throughout your report provides clarity

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18
Q

Never define a variable by copy-pasting the first definition you encounter in the literature

A

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

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19
Q

A variable cannot be defined by using examples

A

You first need to provide a formal definition, after which you can supplement that definition with examples. Examples can never replace a reference

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20
Q

Never strive for complexity

A

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

For example, a model with one independent variable and multiple moderators is a parsimonious model. In a similar vein, a model with multiple independent variables and one moderator is a parsimonious model.

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21
Q

What is a research hypothesis

A

A tentative statement about the coherence between two or more variables

22
Q

A research hypothesis is a tentative statement

A

This means that a research study will test, using data, whether this statement is sound

23
Q

A research hypothesis pertains two or more variables

A

A main effect hypothesis is about the relationship between two variables. A mediator and a moderator hypothesis are about the relationship between three variables

24
Q

Directional hypotheses

A

Indicate the expected direction of the relationship; is the expected association positive or negative

For example: higher workloads are associated with lowe employee morale is a directional hypothesis. The hypothesized main effect is negative

25
Q

Directional hypotheses

A

Indicate the expected direction of the relationship; is the expected association positive or negative

For example: higher workloads are associated with lowe employee morale is a directional hypothesis. The hypothesized main effect is negative

26
Q

Undirectional hypotheses

A

Expect a relationship, but they do not indicate the direction

For example: Workloads are associated with employee morale is an undirectional hypothesis. The hypothesis does not indicate whether the main effect is likely to be positive or negative

27
Q

When to use undirectional hypotheses

A

Use sparingly; only use them when the theory points in two directions that are equally likely

28
Q

Why start with research hypothesis

A

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.

29
Q

What makes a good research hypothesis

A

Should be testable; means that it should be phrased in terms of (measurable) variables

SHould not be based on your gut feeling but should be justified using logical arguments based on prior research studies

30
Q

Finding support for a hypothesis

A

Hypotheses are neither true nor false in an absolute sense. You can therefore 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 (consistent with) or not supported by (not consistent with) the data. A hypothesis is not right; it is simply not proven wrong.

When you see the word prove in a headline in the popular press, be skeptical! No single study can prove a hypothesis once and for all. A more accurate headline would be: “Study supports the hypothesis that ….”

31
Q

Choosing variable names

A

Before formulating your research hypotheses, you must decide on the names you will use to refer to your variables. Variable names hsould (i) not overpromise, (ii) leave no room for ambiguity, nad (iii) be short

32
Q

Variable names should not overpromise

A

In choosing a variable name, you should make sure it is to the point and does not overpromise

33
Q

Variable names should be unambiguous

A

In choosing a varaible name, you should make sure it is unambiguous. Avoid variable name that can be interpreted in multiple ways

34
Q

Variable names should be short

A

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

35
Q

Guidelines to formulate research hypotheses

A

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 distinnction is whether a variable is quantitative or categorical

36
Q

Quantitative variable

A

Captures a quantity.
E.g. household size or number of persons in a household)

37
Q

Categorical variable

A

Has different levels or categories that are not ordered along an underlying dimension

38
Q

A main effect hypothesis when botht he DV and IV are quantitative

A

If two quantative variables are related, you can expect the relationship/association between these two variables to be either positive or negative

When you expect that an increase in X corresponds to an increase in Y, the relationship between X and Y is positive. Visually, a positive relationship looks like the cloud of data points climbs to the upper right-hand corner of the graph.

39
Q

Hypotheses can be offered for variables with any number of levels, this quickly become overly complex therefore:

A

We therefore limit ourselves to the case of two levels

40
Q

How to word a mediator hypothesis

A

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, while the second hypothesis concerns the relationship between the mediator MED and the dependent variable Y. To emphasize the mediating role of MED, you can label the hypotheses as a and b components of one and the same hypothesis (e.g., H1a and H1b). As before, you need to take variable type (quantitative vs. categorical) into account.

41
Q

For three quantitative variables, the hypotheses could be formulated as follows:

A

H1a: When X increases, MED increases/decreases.
H1b: When MED increases, Y increases/decreases

Example

A research study investigated whether personalized ads are associated with more privacy concerns among consumers than non-personalized ads, and whether these privacy concerns are negatively related to consumers’ attitudes toward the advertised brand. Personalized vs. non-personalized ad is a categorical IV with two levels. Consumers’ privacy concerns is a quantitative MED, while consumers’ attitudes is a quantitative DV. The mediator hypothesis could be expressed as:

 H1a: Personalized ads are associated with more privacy concerns among consumers
          than non-personalized ads.

 H1b: When consumers' privacy concerns increase, their attitude toward the advertised
          brand decreases.
42
Q

Partial mediation

A

When the mediating variable doesnt fully affect the direct variable through the indirect variable

43
Q

How to word a moderator hypothesis
A moderator changes the strength of a relationship between two variables. A moderator can make:

A

a positive relationship stronger (more positive)
a positive relationship weaker (less positive and possibly even negative)
a negative relationship stronger (more negative)
a negative relationship weaker (less negative and possibly even positive)

44
Q

When all three involved variables (DV, IV, and MOD) are quantitative, and the MOD is expected to strengthen the positive relationship between the DV and the IV, the moderator hypothesis can be expressed as follows:

A

H: The positive relationship between X and Y strengthens when MOD
increases.

Example
A researcher expects that the number of open innovation activities firms undertake positively affects their sales. In addition, he expects that the positive effect of open innovation on firm sales becomes more positive when firms also invest more in R&D themselves. That is, he expects the effects of open innovation and R&D on firm sales to be synergistic (produce a combined effect greater than the sum of the individual effects).

The main-effect hypothesis can be expressed as:

 H: The relationship between open innovation and firm sales is positive.

The moderator hypothesis can be expressed as:

 H: The positive relationship between open innovation and firm sales strengthens as
      firms' R&D expenditures increase.
45
Q

When all three involved variables (DV, IV, and MOD are) quantitative, and the MOD is expected to weaken the positive relationship between the DV and the IV, the moderator hypothesis can be expressed as follows:

A

H: The positive relationship between X and Y weakens/decreases when MOD increases.

Example

A researcher employed by Albert Heijn expects the brand sales of mineral water to increase when price-promotion depth increases. However, he expects that the positive effect of price-promotion depth on brand sales becomes less positive for higher-priced brands.

The main-effect hypothesis can be expressed as:

 H: The relationship between price-promotion depth and brand sales is positive.

The moderator hypothesis can be expressed as:

 H: The positive relationship between price-promotion depth and brand sales weakens as
      brand price increases.
46
Q

When all three involved variables (DV, IV, and MOD) are quantitative, and the MOD is expected to strengthen the negative relationship between the DV and the IV, the moderator hypothesis can be expressed as follows:

A

H: The negative relationship between X and Y weakens when MOD increases.

Example

A researcher would like to know whether production offshoring (defined as the extent to which a firm outsources manufacturing to another country) is related to a firm’s innovativeness (measured as the number of new product introductions per year). He expects that the main effect of production offshoring on firms’ innovativeness is negative: if firms offshore a larger percentage of their manufacturing, they will become less innovative. The reason is that firms learn by doing: they learn from manufacturing products, and in the process, they may develop new product ideas. If they offshore manufacturing, they lose these sources of ideas for innovation). However, he does not expect this relationship to be equally strong for every firm. He expects that if firms invest more in R&D, they may be able to reduce the negative effect of production offshoring on their innovativeness.

The main-effect relationship can be expressed as:

 H: The relationship between production offshoring and firm innovativeness is 
      negative. 

The moderator hypothesis can be expressed as:

 H: The negative relationship between production offshoring and firm
      innovativeness weakens as firms' R&D expenditures increase.

Since the main effect is expected to be negative, it is denoted by a minus sign above the corresponding arrow in the conceptual model. Since the moderator is expected to weaken the main effect (i.e., make it less negative), it is denoted by a plus sign.

The graph on the right-hand side would support the researcher’s moderating hypothesis: for every 5% increase in production offshoring, firms’ new-product introductions hardly drop if firms spend a lot on R&D. In contrast, if firms do not spend a lot on R&D increases, the number of new product introductions drops with 4 for every 5% increase in production offshoring

47
Q

1- The IV is quantitative and the MOD is categorical (2 Levels

A

When the IV is quantitative and the MOD is categorical (with two 2 Levels), you can formulate the following hypothesis:

H: The relationship between X and Y is stronger/larger (weaker/smaller) for
Level_1_of_MOD than for Level_2_of_MOD.

Example

A researcher hypothesizes that the number of open innovation activities firms undertake positively affects firm profitability, but does not expect this effect to be uniform across all firms. Specifically, he expects that the relationship between open innovation and performance is more positive for publicly listed firms than for private firms. The moderation hypothesis can be expressed as follows:

 H: The positive relationship between open innovation and firm profitability is larger for           publicly listed firms than for private firms.
48
Q

2- The IV is categorical (2 levels) and the MOD is quantitative

A

When the IV is categorical (with two 2 Levels) and the MOD is quantitative, you formulate the following hypothesis:

 H: The difference (or: gap) in Y between Level_1_of_IV and Level_2_of_IV is
      larger/smaller (or: increases)/decreases when MOD increases.

Example

A researcher hypothesizes that men earn more than women. He expects the salary difference between men and women becomes larger for people with higher education levels. The moderation hypothesis can then be expressed as follows:

 H: The salary difference between men and women is smaller when their
 education level increases.
49
Q

3- The IV and MOD are both categorical (2 levels)

A

When the IV and the MOD are categorical (with two 2 Levels), you formulate the following hypothesis:

 H: The difference in Y between Level_1_of_IV and Level_2_of_IV is larger (or smaller) for   
 Level_1_of_Mod than for Level_2_of_Mod.

Example

A researcher hypothesizes that men earn more than women. He expects the salary gap between men and women to be smaller in service industries than in high-tech industries. The moderation hypothesis can then be expressed as follows:

 H: The salary difference between men and women is larger in high-tech industries than in
 service industries.
50
Q

how to justify hypotheses

A

You justify a hypothesis by providing logical arguments, based on the existing literature. These arguments collectively make the hypothesis plausible.

After you have built up your line of reasoning, you conclude with your hypothesis, using sentences such as:

This leads to the following hypothesis:
We therefore hypothesize:
As such:
Therefore:
Thus:
after which your formal hypothesis can be presented on the next line.

51
Q

1- Do not simply claim that author X said so

A

A common mistake is to claim that a certain author has stated that your hypothesis is true, so it must be true. Don’t do this. This author might be wrong. Focus on the consensus in the literature, rather than the word of one person.

Even if the author is right, the reader still does not understand why your hypothesis makes sense. So, you should provide reasons / explain underlying mechanisms (as to why X is related to Y, or why MOD strengthens or weakens the relationship between X and Y).

52
Q

2- Do not summarize one article after the other

A

At all times, avoid summarizing one article after the other to justify a hypothesis. Instead, create your own synthesis of the literature at hand.