Exam preparations

Question 1

What is field notes?

Answer 1

Field notes are a qualitative research tool used to record observations, thoughts, and reflections during or after fieldwork. Field notes are a qualitative research tool used to record observations, thoughts, and reflections during or after fieldwork.

Often used from ethnographic studies. other things just that the verbal communication that is of interest

Question 2

What is archival data?

Answer 2

Archival data refers to information that has been collected and stored in a systematic way, typically for non-research purposes, but that researchers can later use to address specific questions or hypotheses.

The data is NOT collected by the researcher but sourced from ex historical records. Because it is pre-existing, researchers use it as a cost-effective and efficient way to investigate patterns, trends, and relationships without conducting primary data collection.

Types:
Records, photographs, audio recordings, statistics.

Question 3

What is grounded theory?

Answer 3

Grounded theory is a qualitative research methodology focused on developing theories directly from data rather than testing pre-existing theories.

Important features:
Data-driven
Constant comparision
Open-ended

Question 4

What is trustworthiness and how can we make the research trustworthy?

Answer 4

what is trustworhtiness and how can we make the research trustworthy?

key compomemts:
credibility
transferability
dependability

Question 5

What is ontology?

Answer 5

“Does gravity exist?” Yes! our view on the world; how do we look upon reality

Question 6

What is epistemology?

Answer 6

“How do we know gravity exists?” Through evidence!

what can we know about reality, our knowledge about something

Question 7

What is abduction?

Answer 7

going back and forth between inductive and deductive

Question 8

Can regression analysis detect causation?

Answer 8

Yes, with a casual research design.

Question 9

What is a population regression function?

Answer 9

describes the relationship between a dependent variable (outcome) and one or more independent variables (factors) for the entire population.

ex Imagine you want to know how study hours (independent variable) affect exam scores (dependent variable) for every student in the world.

Question 10

How can you check for linearity and homoskedasticity?

Answer 10

Scatterplots

Question 11

How can you check for multicollinearity?

Answer 11

Correlation matrices

Question 12

How can you check for autocorrelation?

Answer 12

Durbin-Watson

Question 13

How can you check for normality?

Answer 13

Shapiro-Wilk and Kolmogoro-Smirnov

Question 14

What are the consequences when the Linearity assumption is violated and what is the solution?

Answer 14

When the linearity assumption is violates, the relationship between X and Y is not linear.

Consequence: biased estimates

Fix: Use a nonlinear regression model

Question 15

What are the consequences when the Homoskedasticity assumption is violated and what is the solution?

Answer 15

When its violated we have heteroskedasticity.

Fix: Use Robust Standard Errors or Weighted Least Squares

Question 16

What are the consequences when the No Perfect Multicollinearity assumption is violated and what is the solution?

Answer 16

In that case we have multicollinearity: independent variables are highly or perfectly correlated.

Fix: Remove or merge correlated varaibles. Principal Component Analysis

Question 17

What are the consequences when the Normality of Errors assumption is violated and what is the solution?

Answer 17

The residuals (errors) are not normally distributed. Coefficient estimates remain unbiased, but hypothesis tests (e.g., t-tests, F-tests) may be invalid, especially in small samples.

Fix: Use non-parametric methods (which are not dependent on the normality assumption), or check if large samples mitigate this issue (Central Limit Theorem). Or use large samples.

Question 18

What are the consequences when the “No Autocorrelation” assumption is violated and what is the solution?

Answer 18

No autocorrelation = No Independence.

autocorrelation is just the term for when the independence assumption in ols regression is violated.

So, when violated, errors are correlated across observations.

• Adjust your model to directly address the source of autocorrelation (e.g., include lagged terms).
• Use robust standard errors (like Newey-West) to correct for the issues in residuals.

Question 19

It is necessary for independent and dependent variables to be normally distributed?

Answer 19

No - the independent and dependent variables do not need to be normally distributed. Regression can handle variables of any distribution, like skewed.

For the errors and residuals, yes. That is an assumption in OLS regression.

Question 20

How can we detect non-normal distributions of the residuals? (3)

Answer 20

1. Histogram. Non-normality appears as skewed distributions or outliers.
2. Q-Q Plot. Compares the distribution of residuals to a normal distribution. Points should align along a straight diagonal line if residuals are normal. Deviations from the line indicate non-normality.
3. Shapiro-Wilk/Kolgomorov-Smirnow

Question 21

What is a good fit of a regression and how can we measure it?

Answer 21

A good fit = The model explains a large proportion of the variability in the dependent variable. Residuals (differences between observed and predicted values) are small and randomly distributed.
The model meets assumptions (e.g., linearity, independence, homoscedasticity).
Predictions are accurate for the data.

Measured by R squared. It ranges from 0 to 1. 1 = Perfect fit.

Question 22

What are outliers and how can we mitigate their impact on a regression?

Answer 22

Outliers are extreme values. We can detect them via scatterplots or boxplots ex.

Three ways to handle it:
Transforming
Trimming (remove them)
Winsorizing (reduce their impact)

Question 23

What is the benefit from simple regression → multiple regression?

Answer 23

A simple regression only accounts for one independent variable to explain the dependent variable.

Question 24

What is Zero mean of the residuals?

Answer 24

Refers to the overall average of the residuals across all observations.

Question 25

What is Zero conditional mean?

Answer 25

Exogeneity. Ensures that the independent variables are uncorrelated with the error term u.

The covariance between independent variables and the residuals is zero.

Question 26

What is constant variance of the residuals?

Answer 26

Homoskedasticity

Question 27

What is omitted variables and its consequences?

Answer 27

Omitted variables are important factors that influence the dependent variable Y but are not included as independent variables X in the model. This creates omitted variable bias.

Question 28

How can you address omitted variables?

Answer 28

1. For panel or longitudinal data, fixed effects can control for omitted variables that are constant within individuals or groups.
2. Randomized Control Trials

Question 29

What is overspecification and its consequences?

Answer 29

Overspecification occurs in regression analysis when the model includes too many independent variables, some of which are irrelevant or redundant. These extra variables do not improve the model’s ability to explain the dependent variable Y and can even harm its performance.

Consequences: Multicollinearity

Question 30

Do dependent and independent variables need to be normally distributed?

Answer 30

No.

Vi kan ju ha en bra modell trots tex negative skewness.

Question 31

What is a Q-Q plot?

Answer 31

A Q-Q plot (Quantile-Quantile plot) is used to compare the distribution of a dataset to a theoretical distribution (e.g., normal distribution) by plotting their quantiles.

Assess if residuals from a regression model are normally distributed.

Question 32

What is a scatterplot?

Answer 32

It’s used to show the relationship between two variables by plotting their values as coordinate points.

Ex: Explore if study hours X and test scores Y have a linear relationship.

Question 33

What is sampling error and how can you solve it?

Answer 33

Sampling error is the difference between a sample statistic (e.g., sample mean, sample proportion) and the corresponding population parameter (e.g., population mean).

For example:
You survey 1,000 people from a city to estimate the average income. The sample mean might differ from the actual population mean due to sampling error.

SOLVE IT BY INCREASING THE SAMPLE SIZE

Question 34

What is random sampling?

Answer 34

Ensure the sample is chosen randomly so every member of the population has an equal chance of being selected. This reduces the likelihood of systematic bias.

Question 35

What is stratified sampling?

Answer 35

Divide the population into subgroups (strata) based on characteristics like age, income, or region, and take random samples from each.

Ensure that each subgroup is proportionally represented in the sample.

Question 36

What is cluster sampling?

Answer 36

Divide the population into clusters (e.g., geographic areas or naturally occurring groups) and randomly select clusters for sampling. Makes data collection more efficient by sampling whole clusters instead of individuals.

Question 37

What is a quasi-experiment and how is it different from RCT?

Answer 37

A quasi-experiment lacks the random assignment of participants to treatment and control groups, which RCT has.

Question 38

What is difference-in-difference?

Answer 38

Compares outcomes between a treatment group and a control group before and after an intervention.

Question 39

What is panel data?

Answer 39

Panel data (also known as longitudinal data) is a type of dataset that contains observations of multiple entities (such as individuals, firms, countries, etc.) over multiple time periods. It combines elements of cross-sectional data (data collected at one point in time) and time-series data (data collected over time for a single entity).

Question 40

What is fixed effect?

Answer 40

Fixed effects control for factors unique to each entity that do not change over time (time invariant, culture)

Its cross-sectional but with fixed time.

Time-invariant factors are automatically controlled in FE for because these models focus only on within-entity variation over time.

Question 41

What is random effect?

Answer 41

Time-invariant variables can be estimated because random effects assume that these factors are uncorrelated with the independent variables.

Question 42

Is one way panel regression model the same thing as fixed effect?

Answer 42

Not exactly. A one-way panel regression model can be a fixed effects model, but it can also be a random effects model depending on how the unobserved effects are treated.

Question 43

What is One-Way Panel Regression Model?

Answer 43

A one-way panel regression model accounts for unobserved effects that vary only across entities or only across time, but not both.

Question 44

What is a Two-Way Panel Regression Model? (TWFE)

Answer 44

A two-way panel regression model accounts for unobserved effects that vary both across entities and over time. It includes both entity fixed effects and time fixed effects, making it more comprehensive.

Question 45

What is a difference-in-difference? How do you set it up?

Answer 45

Difference-in-Differences (DiD) is a statistical method used to estimate the causal effect of a treatment or intervention by comparing changes in outcomes over time between a treatment group (exposed to the intervention) and a control group (not exposed). It leverages the idea that the difference in outcomes between the groups before the intervention can serve as a baseline, and any additional difference after the intervention reflects the causal effect.

Question 46

Is randomization used for Difference-in-Difference?

Answer 46

Usually no, because its not needed.

Question 47

Can regression detect causality?

Answer 47

Yes when you have a casual research design

Question 48

Why is an event study considered a quasi-experiment for casual inference?

Answer 48

An event study is considered a quasi-experimental design because it has no randomization. It relies on naturally occuring events rather than a random assignment of treatment.

Question 49

What is casual inference?

Answer 49

Causal inference is the process of determining whether and how a change in one variable (the cause) directly influences another variable (the effect). It aims to go beyond observing correlations or associations by establishing a cause-and-effect relationship between variables.

Causal inference revolves around estimating the treatment effect by comparing observed outcomes to counterfactuals, often using control groups or statistical methods to approximate the unobservable counterfactual.

Question 50

What is a contrafactual?

Answer 50

A counterfactual is the hypothetical scenario representing what would have happened in the absence of the treatment.

Question 51

What is a staggered rollout design?

Answer 51

In a staggered rollout design, the treatment or intervention is introduced to different entities at different points in time.

Ex. Different regions of the country implement the smoking ban in public places at different times:
Region A: January 2023
Region B: June 2023
Region C: December 2023

Question 52

What is a non-staggered rollout design?

Answer 52

In a non-staggered rollout design, the treatment or intervention is introduced to all treated entities at the same point in time.

Ex. A country implements a nationwide smoking ban in public places on January 1, 2023, applying to all regions simultaneously.

Question 53

What is event time?

Answer 53

Event time refers to a time scale centered around a specific event or intervention (e.g., a policy implementation, a treatment, or a natural disaster).

t = 0

Question 54

What is calendar time?

Answer 54

Calendar time refers to the actual date or time period when an observation occurs, using a universal, chronological scale (e.g., years, months, days).
Time is measured in absolute terms (e.g., January 2022, Q3 2023).

Question 55

What is pre-event window?

Answer 55

time periods before the event occurs

Question 56

What is the event window?

Answer 56

Same as event time. refers to the time period during which the event occurs, t = 0

Question 57

What is post-event window?

Answer 57

The post-event window includes the time periods after the event occurs

Question 58

What is typically the contrafactual in a CAPM study?

Answer 58

In the context of CAPM, the counterfactual is indeed what the return on an asset would have been if CAPM perfectly explained returns.

In CAPM we only consider:
1. Risk free rate
2. Market risk premium (expected return - risk free rate)
3. Beta

Only accounts for market risk (systematic risk), not firmspecific risk.

Question 59

What is TWFE? (Two-Way Panel Regression Model)

Answer 59

Two-Way Fixed Effects accounts for both entity-specific and time-specific unobserved factors. It isolates the effect of interest by controlling for unobserved heterogeneity

Question 60

How does a calendar-time portfolio method differ to an event study method?

Answer 60

The calendar-time portfolio method analyzes portfolio returns over regular calendar periods (e.g., monthly or yearly) to evaluate long-term performance or strategies. For example, you might group stocks into value and growth portfolios based on their book-to-market ratios and track their average returns month by month to see if value consistently outperforms growth.

The event study method, on the other hand, focuses on specific events and their impact on returns or outcomes. For example, to analyze how earnings announcements affect stock prices, you calculate abnormal returns for each stock relative to the event date and observe how returns behave before, during, and after the announcement.

The key difference is that the calendar-time method looks at overall trends across time, while the event study method zeroes in on the effects of a particular event.

Question 61

What do we mean by portfolio sorts?

Answer 61

Portfolio sorts refer to a method used in finance to group assets (e.g., stocks) into portfolios based on certain characteristics or metrics, such as size, value, momentum, or other financial variables.

Once asset are sorted, they are grouped into portfolios and divided into quantiles or other bins.

Question 62

What is the benefit to using portfolio sorts over a panel linear regression method?

Answer 62

Portfolio sorts are simple and intuitive, grouping assets into portfolios based on a characteristic (e.g., size or value) and comparing returns. They are robust to outliers, non-parametric (no linearity assumptions).

Panel regressions allow for controlling multiple variables and provide more precise estimates but can be sensitive to outliers and require assumptions about linearity.

Question 63

What is some of the draw downs of portfolio sorts, and how might we mitigate them?

Answer 63

Portfolio sorts are simple and robust but can lose precision, fail to control for covariates, and rely on arbitrary thresholds. Mitigate these drawbacks by complementing them with regressions, testing sensitivity, and refining sorting techniques.

Question 64

What is conditional portfolio sorts?

Answer 64

Assets are sorted into portfolios based on the primary characteristic while controlling for another characteristic.

Question 65

What is unconditional portfolio sorts?

Answer 65

Assets are sorted into portfolios based on a single characteristic without considering any other variables.

Question 66

What is abductive?

Answer 66

Going back and forth between inductive and deductive.

Question 67

The positivist perspective is characterized by objectivity, that the researcher and what they are studying are (DEPENDENT OR INDEPENDENT) of each other, and data is primarily quantitative.

Answer 67

Independent

Question 68

What is the role of the research question?

Answer 68

To motivate and focus the research.

Question 69

In social sciences, research questions may…

Answer 69

Motivate theory development

Question 70

When evaluating a research question, it is important that it:

Answer 70

Is relevant, researchable, and represents a gap in knowledge.

Question 71

What is a cross-sectional research design and how does it relate to causality?

Answer 71

Data is collected at a specific point in time from a cross-section of respondents, so concluding causal inference is weak.

Question 72

What is the research design?

Answer 72

It is the plan for how a research project will be conducted.

Question 73

With respect to Professor Wedlin’s lecture on research design, which statement DOES NOT describe why a research design is needed? A research design:

Facilitates the formation of a research question that is relevant and researchable.

Considers strategies and choices for what data to collect, how to collect it, and how to assess it.

Locates the study in a particular knowledge domain.

Turns a research question and objectives into a project.

Answer 73

Facilitates the formation of a research question that is relevant and researchable.

Question 74

What is a natural experiment?

Answer 74

Tänk natural experiment: CAUSALITY

An event occurs to a specific group of people outside the control of the researchers, but in such a way as to resemble random assignment.

Data is collected from before and after the event, and causality is established.

Question 75

How could you describe a research question and related hypotheses?

Both research questions and hypotheses must be stated such that there is at least one testable alternative.

A research question may be answered through related hypotheses that are stated as questions.

You may answer a research question by posing hypotheses. The hypotheses must be empirically testable.

Quantitative research questions are meant to be very specific, with a set of general hypotheses, which in turn answer the research question.

Answer 75

You may answer a research question by posing hypotheses. The hypotheses must be empirically testable.

Question 76

Constructs can have theoretical definitions and operational definitions. What is the purpose of operationalizing a construct?

Operationalization specifies the theoretical domain of a construct.

Operationalization specifies constructs within the accepted structure of hypotheses.

Operationalization specifies how a construct will be measured.

Operationalization specifies how a construct will be analyzed.

Answer 76

Operationalization specifies how a construct will be measured.

Question 77

The interview method is appropriate when:

Answer 77

The researcher wants to get deep knowledge about a certain phenomenon.

Question 78

Which answer would best fit the inductive approach?

The researcher discovers that not much empirical work has been done on a specific topic. Then, based on what theory she finds, decides to investigate.

The researcher is testing a theory by collecting data that is found to be most suitable given the existing theory.

The researcher is using an approach where he/she structures the findings according to theory.

The researcher, through a literature review, finds that there is a lack of theory explaining a certain phenomenon. Then, decides to investigate.

Answer 78

The researcher, through a literature review, finds that there is a lack of theory explaining a certain phenomenon. Then, decides to investigate.

Question 79

The method chapter:

Is a way of showing, and giving reference to, the theory of methodology, such as what ontology is, and when to expect different ontological arguments.

Is not the most important chapter of a research paper, compared to, for instance, the theoretical chapter.

Should in detail describe how the research has been conducted, such as who did what, how, and when.

Should briefly cover how the research has been conducted without going into detail into exactly who did what and when.

Answer 79

Should in detail describe how the research has been conducted, such as who did what, how, and when.

Question 80

When creating a questionnaire, what is a good way to make sure you properly cover the dimensions of each construct?

Start by looking at existing questionnaire on similar topics or theories.
Use existing questionnaires, but adapt them enough to avoid plagiarizing other researchers.
Avoid using existing questionnaires so that you do not plagiarize other researchers.
Read everything you can about the theoretical context so that you can then make the questionnaire.

Answer 80

Start by looking at existing questionnaire on similar topics or theories.

Question 81

What is recall bias?

Answer 81

You only remember certain things better than other which may bias your answers.

Question 82

What is self-selection bias?

Answer 82

conducting a survey on companies and sustainability, and only the companies interested in that area joins

Question 83

Assume that there are 500 people in a population and they are all on a list. You want a random sample of 50. You add the first 10 names to a hat and have a friend randomly pick one of the names. Starting with that name, you take every tenth person thereafter. In this way, you get a sample of 50. What kind of sample is this?

Answer 83

Probability sampling

Question 84

What is non-probability sample?

Answer 84

not all members of the population have a known or equal chance of being included in the sample. aka no random selection.

Question 85

What is the sampling frame?

Answer 85

EVERYONE

Question 86

What is the sampling list?

Answer 86

sampling list is a practical subset of that frame used for selecting the actual sample in a study

Question 87

Validity and reliability are important in science. With respect to measurement, what are they?

Answer 87

Validity is how well a measure reflects what it intends to measure, and reliability is about the consistency of measurements.

Question 88

Which statement best describes qualitative data coding?

Answer 88

It is the process of organizing and labeling data.

Question 89

When you code your data according to the Gioia, Corely & Hamilton method, which coding approach is most appropriate?

Answer 89

You move from data-text (empirics) to higher analytical levels by aggregating and condensing.

Question 90

Which type of reliability assessment is associated with qualitative analysis?

R-squared.

Cronbach’s alpha.

Inter-rater reliability.

The Levene test for homogeneity of variance.

Answer 90

Inter-rater reliability

Question 91

In a boxplot with outliers, what is the best measure of the center of the data?

Answer 91

The median, because the data is so extremely abnormally distributed you could not rely on the mean.

Question 92

What does the median indicate for the variable company size as measured by the number of employees?

Answer 92

It is a nonparametric statistic that indicates the center of the data.

Question 93

Reliability refers to:

The consistency of how well something is measured.

The number of relationships between multiple dimensions of a latent construct.

The dimensionality of how well something is measured.

The accuracy of how well something is measured.

Answer 93

The consistency of how well something is measured.

Question 94

In an OLS regression analysis, based on the Coefficients table, if you were going to write out a regression equation so that a person could calculate, “for a given value of X , Y would equal ___”, you would use the:

Answer 94

Unstandardized beta coefficients for the statistically significant independent variables.

Question 95

In regression, what is the error term?

Answer 95

Residual variance that is not explained by the regression coefficients.

Question 96

With respect to simple OLS regression, which of the following statements is correct?

You can use dummy variables as the dependent variable.

The constant indicates the slope of the regression line.

The beta coefficient for the X variable indicates the slope of the regression line.

A dummy variable changes the slope of a regression line.

Answer 96

The beta coefficient for the X variable indicates the slope of the regression line.

Question 97

In regression, if your model is too long (too many independent variables), the parameter estimates for the beta coefficients become less precise. If the model is too short, you are missing important independent variables and the parameter estimates for the beta coefficients become biased. Why is too short worse than too long?

Answer 97

Less precise estimates are random, so at least the error averages out.

Question 98

In regression, what is the residual variance?

It represents all the errors explained by the independent variables.

It is the cumulative variance of errors made when measuring the independent variables.

It is the cumulative variance of errors made when collecting the data.

It is the variance that is not explained by the regression coefficients

Answer 98

It is the variance that is not explained by the regression coefficients

Question 99

Consider the F statistic in a regression equation. To reject the null hypothesis (in layman terms this means we are happy with the regression equation), what should the Sig. number (p-value) be?

Answer 99

Below the critical cutoff p-value.

Question 100

Why is the Adjusted R Square different from the R Square?

Answer 100

It adjusts downward for each additional independent variable.

Question 101

In the Model Summary, how would you interpret the “R” statistic?

Normally, we interpret R as the amount of error in the regression equation and R squared as the amount of explained variance.

Normally, we take the square of the R statistic and interpret that as the level of explained variance in the regression equation.

Normally, we do not interpret R. We only interpret Adjusted R Square.

Normally, we take the R and divide it by the Standard Error of the Estimate to then get R squared.

Answer 101

Normally, we take the square of the R statistic and interpret that as the level of explained variance in the regression equation.

Question 102

How would you interpret the “R Square” statistic?

Answer 102

It is the explained variance in the regression equation.

Question 103

What information do you get from the standardized beta coefficients?

Answer 103

The relative effect size of each independent variable on the dependent variable.

Question 104

In regression, specification error refers to including irrelevant independent variables, not including important independent variables, or choosing the wrong functional form. In layman terms we talked about too long and too short models. Which of the following statements is true?

Answer 104

A too long model reduces the precision of the beta coefficients, whereas a too short model causes a systematic bias in the parameter estimates.

Question 105

With respect to OLS regression, which of the following statements is correct?

In simple regression (one X variable), the standardized beta is the slope of the regression line.

A dummy variable changes the slope of a regression line.

You can use dummy variables as the dependent variable.

The unstandardized beta constant indicates the Y-intercept of the regression line.

Answer 105

In simple regression (one X variable), the standardized beta is the slope of the regression line.

Question 106

What is a normative research question?

Answer 106

What should happen?

Question 107

What is a descriptive rq?

Answer 107

What is happening?

Question 108

What is a exploratory rq?

Answer 108

What might be happening?

Question 109

What is an explanatory rq?

Answer 109

Why or how is it happening?