Advanced Empirical Methods Flashcards

Question

*You want to research the following topics *The Influence of Artificial Intelligence on Media Culture *Media and Information Literacy can combat Misinformation and Disinformation *Commercial advantages of sensationalizing health topics on YouTube *What kind of theories could you use to study them? *Name the theory and explain why it is useful for studying the topic

Answer 1

1. Technological Determinism Theory Founder: Marshall McLuhan Key Idea: "The medium is the message" — technology, especially communication tools, determines how society is structured and how culture evolves. 2. Agenda-Setting Theory Founders: Maxwell McCombs and Donald Shaw Key Idea: The media doesn't tell people what to think but what to think about by prioritizing certain topics, thus influencing public opinion. 3. Uses and Gratifications Theory Founders: Elihu Katz, Jay G. Blumler, and Michael Gurevitch Key Idea: Audiences are active participants who use media to satisfy specific needs, such as information, entertainment, or social interaction. 4. Cultivation Theory Founders: George Gerbner and Larry Gross Key Idea: Long-term exposure to media content, especially television, shapes perceptions of reality, often aligning them with the media’s portrayal. 5. Social Learning Theory Founder: Albert Bandura Key Idea: People learn behaviors by observing and imitating others, particularly role models in media, and this is reinforced by rewards or punishments. The "Two-Step Flow of Communication" concept, developed by sociologists Paul Lazarsfeld, Bernard Berelson, and Hazel Gaudet, emerged from their study of voter behavior during a U.S. presidential campaign, as detailed in their book “The People's Choice (1944)”. Step 1 - media influences opinion leaders, Step 2 - Opinion leaders influence their social groups by sharing and interpreting information. The key principle of the Two-Step Flow of Communication relevant to this research is the role of social interaction in spreading information, as people often trust information shared through personal communication more than directly from media. Instagram functions as a platform where modern-day opinion leaders—such as influencers, activists, and content creators—curate and interpret content related to the conflict. In the first step of the communication flow, these opinion leaders consume and filter information from traditional media or firsthand sources. In the second step, they share this information with their followers, often framing it in ways that resonate with their audience's values, emotions, and worldviews. By enabling social interaction through comments, likes, and shares, Instagram fosters a participatory environment where information about the conflict is disseminated, interpreted, and reinforced within social networks, ultimately shaping public perceptions in Germany.

Answer 2

§ An experiment requires a specific research design. This makes it possible to identify cause-effect relations. § By conducting a survey, we collect mostly attitudes and opinions among a population. § Via content analysis, we can systematically describe content. § By using the observation method, we can determine people’s actual behaviour. § In the field of Communication, researchers use primarily surveys and content analyses.

Answer 3

1. Forms and Questionnaries 2. Interview 3. Observation 4. Documents and Records 5. Focus Groups 6. Oral Histories 7. Combination Research 8. Online Tracking 9. Online Marketing Analytics 10. Social Media Monitoring

Answer 4

1. Define the question 2. Collect the data 3. Clean the data 4. Analyze the data 5. Visulize and share findings

Answer 5

1. Evaluate the Graphic The graphic shows the probability of antidepressant prescriptions based on tree density in a person's living area. It highlights three levels: low, medium, and high tree density, with corresponding probabilities of 7 in 100, 6 in 100, and 5 in 100, respectively. 2. What Topic Is Studied? The relationship between tree density in residential areas and the likelihood of receiving antidepressant prescriptions. It explores whether living in greener areas (with more trees) reduces the need for antidepressants. 3. How Are Cause-Effect Relations Visualized? Tree density is shown as the independent variable (cause), while antidepressant prescription rates are the dependent variable (effect). The relationship suggests that higher tree density correlates with fewer antidepressant prescriptions. 4. Is Anything Missing in This Data Analysis? The analysis does not account for confounding variables, such as: Socioeconomic factors (e.g., income or access to healthcare). Urban vs. rural settings (tree density might differ by location). Other environmental or lifestyle factors affecting mental health. It does not explain why tree density might influence antidepressant use (e.g., stress reduction or exercise opportunities). 5. Can You See Any Bias? Sampling bias: The graphic doesn’t specify how the data was collected or whether the sample is representative of the broader population. Interpretation bias: The graphic implies a causal relationship, but the data might only show correlation, not causation. Suggested Answer This graphic visually represents the correlation between tree density and antidepressant use but lacks context about other influencing factors. Without additional data on confounding variables and research methods, it's difficult to draw firm conclusions about causation. The simplicity of the data might oversimplify a complex relationship, potentially leading to bias in interpretation.

Answer 6

Here’s how you can respond to this task: --- **1. Evaluate the Graphic** - The graphic illustrates the scale of Ukrainian refugees, showing that **1 million people have already fled Ukraine**, with **4 million more expected to flee**, representing **11% of the country’s population**. - The visual uses people and blocks to simplify and quantify the data. --- **2. What Topic Is Studied?** - The graphic focuses on the **scale of displacement of Ukrainian citizens** due to war, highlighting the proportion of the population affected by the crisis. --- **3. Is Anything Missing in This Data Analysis?** - The graphic doesn’t provide: - Context about **timeframe** (e.g., over what period these displacements occurred). - Details about **where refugees are going** or their circumstances after fleeing. - **Demographics** of the displaced population (e.g., age, gender, socio-economic status). - Underlying **causes of displacement**, such as war zones or other factors. - It oversimplifies a complex situation without explaining the full impact of the crisis (e.g., humanitarian challenges or international response). --- **4. Can You See Any Bias?** - **Visual bias:** The use of human figures might evoke an emotional response, potentially influencing viewers' perception of the scale. - **Framing bias:** The graphic emphasizes a significant number without providing broader context (e.g., how this compares to other refugee crises or Ukraine's total population size). - **Selection bias:** It focuses only on those fleeing, not accounting for internally displaced people or those unable to leave. --- **Suggested Answer:** The graphic effectively visualizes the scale of displacement but lacks depth. It omits context about the causes, demographics, and global response, which are critical to understanding the full picture. While the use of human figures is impactful, it risks oversimplifying the complexity of the refugee crisis, potentially leading to emotional or skewed interpretations.

Answer 7

**1. Empirical** 1.1. Measuring type and analysis а) Qualitative б) Quantitative 1.2. Methods for data collection ***Qualitative:*** а) Survey б) Content analysis ***Quantitative:*** а) Observation б) Physiological measurement **2. Non-empirical** 2.1. Research Designs Experimental: Theology, Hermeneutics Nonexperimental

Answer 8

1. Introduction 1.1. What is unknown? 1.2. How and why should we fill the gap? 2. Methods 3. Results 4. Discussion 5. Conclusion

Answer 9

Theory in research follows (the deductive approach) or arises from (the inductive approach) of data collection

Answer 10

1. Ontology: the nature of reality and of what really exists 2. Epistemology: the relationship between the inquirer and what is known 3. Axiology: what we value: the ultimate worth of research 4. Methodology: the strategy and justifications in constructiong a specific type of knowledge

Answer 11

1. Philosophical Foundations: ontology, axiology, epistemology 2. Theory: theoretical stance 3. Methodology: systematization 4. Method: techniques used

Answer 12

Qualitative research is a scientific method used **to gather non-numerical data**. Rather than focusing on measurements or metrics, it **seeks to understand concepts, experiences, or phenomena** by exploring participant perspectives. This method is often employed in fields like psychology, sociology, media/communication, and marketing to gain deeper insights into consumer behaviour, motivations, and cultural trends. Neuman, W.L. (2014). It often takes an **inductive view** of the relationship between theory and research, which means theory emerges from research. The focus will be data collection methods in qualitative research, sampling, focus groups, semi-structured interviewing, etc… Bryman, A., et al 2022 (pp. 279-303; 305-316; 347-366)

Answer 13

1. Subjectivity 2. Contextualization 3. Flexibility 4. Richness and depth 5. Interpretation and meaning-making 6. Inductive reasoning 6. Naturalistic setting

Answer 14

1. One-on-one interview: Conducting in-depth interviews (details follow) 2. Focus Groups: Answers the “why, what, and how” questions. Used to explain complex processes. 3. Ethnographic research: Method applied in naturally occurring environment. (target audiences’ environments) to understand the cultures, challenges, motivations, and settings that occur. Instead of relying on interviews and discussions, you experience the natural settings firsthand. 4. Case study research: The case study method has evolved over the past few years and developed into a valuable quality research method. 5. Record keeping: This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in new research. 6. Qualitative observation: It deals with the 5 major sensory organs and their functioning – sight, smell, touch, taste, and hearing

Answer 15

1. Etnography 2. Grounded Theory 3. Narrative 4. Case Study 5. Phemonenology 6. Historical

Answer 16

**1. Analytical objectives** - This research method focuses on describing **individual experiences** and beliefs. - Quantitative research method focuses on describing the **characteristics of a population**. **2. Types of questions asked** - **Open-ended** questions - **Closed-ended** questions 3. **Data collection Instrument** - Use **semi-structured methods** such as in-depth interviews, focus groups, and participant observation - Use **highly structured methods** such as structured observation using questionnaires and surveys **4. Form of data produced** - Descriptive data - Numerical data **5. Degree of flexibility** - Participant responses affect how and which questions researchers ask next - Participant responses do not influence or determine how and which questions researchers ask next

Answer 17

§ Limited generalizability: May be difficult to generalize findings to larger target populations. § Subjectivity concerns § Time-intensive § Resource-intensive: Costly and labour-intensive. § Difficulty in replication § Researcher bias: The researcher may inadvertently influence the research process and results. § Complexity in data analysis: Analysing qualitative data, including coding and thematic analysis, can be complex and time-consuming.

Answer 18

1. Non-experimental Measure outcomes before and after program for participants only No comparison group 2. Quasi-experimental Measure outcomes for program participants and non-perticipants withput random assignment "Control" for bias Comparison group 3. Experimental Randomized Control Trial (RCT) Randomize participants to treatment or control group Measure outcomes for both groups Explicit comparison group

Answer 19

* Empiricism/empirical: * Defined as information collected in a laboratory or the field, based on specific and systematic analyses. * The main characteristics of an empirical approach are: * Collecting experiences (information), * Questioning * Observing/Describing The main characteristics of non - empirical approach are: * Generally concerned with the reasons underlying human behaviour i.e. the why or how as opposed to the what, where, and when.) * Library Research * Literature Research * Scholarly Research * Think Pieces

Answer 20

Intersubjective traceability means that the empirical research is conducted independently of the person and the personal preferences of the researcher.

Answer 21

Exploratory Research § To identify new opportunities § Understand consumer behaviour § Support in diagnosing problems § Help to formulate hypotheses § Assist in market segmentation § Testing the feasibility of new ideas § Helps to prepare for large-scale research Explanatory Research § Exposes If-Then-relations between two or more facts. § Explanatory research is defined as a way of connecting ideas to understand cause and effect. Researchers use the variables to explain why or how to detail what is happening between the two variables.

Answer 22

1. Define research problem 2. Conduct Literature review 3. Formulate hypothesis 4. Collect data 5. Analyze data 6. Draw conclusions

Answer 23

1. Literature research 2. In-depth interview 3. Case studies 4. Focus groups Difference: Exploratory: identify problem, hypothesize solution Explanatory: develop RQ, formulate hypothesis

Answer 24

- Look for the Frequency of Words - Patterns/Themes - Sequence Occurrence

Answer 25

Content analysis is an empirical method for the **systematically, intersubjectively transparent** **description of content** and **formal characteristics** of **messages**. (Softwares Nvivo, ATLAS.it, Otter) ▪ Content analysis can be applied both in qualitative as well as in quantitative research. ▪ It can be used for primary and secondary research approaches.

Answer 26

The qualitative content analysis method focuses on analysing recorded communication taken from artefacts. For example, extracts from books, newspaper articles, interviews

Answer 27

Content Analysis Thematic Analysis Grounded Analysis Discourse Analysis Narrative Analysis Phenomenology/Heuristic Analysis

Answer 28

Content Analysis is a research method that quantitatively analyzes texts. It focuses on **counting the frequency of words, categories, or themes**. The data is broken into smaller units, categorized, and their frequency is measured to identify patterns. Example: Counting how many times certain words or categories are mentioned in an article. Thematic Analysis is a qualitative method aimed at **identifying and interpreting meanings** within the data. It seeks to find key themes that represent ideas, concepts, or experiences expressed by participants. Example: Analyzing interviews to uncover main themes such as emotional states or interpersonal relationships. Commonalities: Both methods use coding, identify themes, and work with qualitative data. Difference: Content Analysis focuses on quantitative aspects (how often something appears), while Thematic Analysis emphasizes qualitative aspects (what meaning lies behind it).

Answer 29

▪ Objects of content analysis can be media products: - articles from newspapers - lyrics of songs - TV and radio shows - movies - advertisements - video clips - blogs - propaganda material ▪ In the content analysis of the data collected through interviews, the objects of analysis are not the interviewed persons, but their statements (interview transcripts).

Answer 30

Original Text -> Paraphrasing -> Generalization -> Reduction -> Page

Answer 31

1. Purpose: Qualitative: Answers the question "Why?" Quantitative: Answers the question "How many/how much?" 2. Data Type: Qualitative: Focuses on observations, symbols, and words. Quantitative: Focuses on numbers and statistical results. 3. Approach: Qualitative: Observes and interprets data. Quantitative: Measures and tests data. 4. Analysis: Qualitative: Groups common data; uses non-statistical analysis. Quantitative: Relies on statistical analysis. Quantitative: You can analyze campaign speeches for the frequency ofterms such as unemployment, jobs, and work * You can use statisticalanalysis to find differencesover time or between candidates Qualitative * You can locate the word unemployment in speeches, and identify whatother words or phrases appearnextto it - such as economy, inequality or laziness * Analyze the meanings ofthese relationships to better understandthe intentions andtargets of different campaigns.

Answer 32

Qualitative data analysis method that involves reading through a data set Identifying patterns in meaning across the data to derive themes Reflexivity: The researcher’s subjective experience plays a central role in meaning-making from data

Answer 33

* Coding is a qualitative data analysis strategy in which **some aspect of the data** **is assigned a descriptive label** that allows the researcher to **identify related content** across the data * It is the process of labelling and organizing your qualitative data to identify different themes and the relationships between them

Answer 34

1. Familiarize yourself with the data 2. Generate initial codes 3. Search for themes 4. Review potential themes 5. Define and name themes 6. Produce the report

Answer 35

* Coding means categorizing data into chunks or segments * Start with a maximum of 8-10 codes – you can develop and add more as you go through the analysis * Be flexible in refining codes * If possible, refine codes in discussions with others * You can expand the code and refine the properties of the code or merge codes into existing codes

Answer 36

* It is a research method in which the features of textual, visual, or aural materials are systematically categorized and recorded so that they can be analysed. * Employed in the field of communication * Central to content analysis is the process of coding, which involves following a set of instructions about what features to look for in a text and then making the designated notation when that feature appears. Requires careful attention to * Unitizing: segmenting the texts for analysis * Sampling: selecting an appropriate collection of units to analyze * Reliability: different researchers making codes consistently * Validity:using a coding scheme that adequately represents the specified phenomena

Answer 37

* Code: the label you attach to a phrase or other short sequence of the text you are analysing. * Example: 'politician' for each mention in the text of any political figure. * Category: a grouping you impose on the coded segments to reduce the number of different pieces of data in your analysis

Answer 38

Formal categories describe the formal characteristics of each particular unit of analysis. Formal categories, like sociodemographic data in surveys, are not placed at the center of surveys, but they deliver important additional information.

Answer 39

Complete categories Precise separation of categories Inside the category as well as among categories. ▪ Categories are precisely separated when ▪ particular values exclude themselves mutually and ▪ when all values refer to the same characteristic (variable). Example: 1 = German Studies, 2 = Humanities…

Answer 40

* Increase validity: Qualitative coding provides organization and structure to data so that you can examine itin a systematic way to increase the validity of your analysis. * Decrease bias: Qualitative coding enables you to be aware of potential biases in the way data is analyzed. * Accurately represent participants: Qualitative coding allows you to evaluate if your analysis represents your participant base, and helps you avoid over-representing one person or group of people. * Enable transparency: Qualitative coding enables other researchers to methodically and systematically review your analysis.

Answer 41

* Developing codes based on the Data itself Here, you, as a researcher, have no preconceived codes. In other words, you read through the text and allow the codes to emerge from the data. * It is suitable for exploratory research from bottom-up and iterative. Especially when there are limited existing theories or understanding of a particular phenomenon. Deductive * This approach uses an articulated theory or existing theoretical framework as a basis for a pre-defined set of codes. These sets of codes are designed in advance and contained in a codebook. * You approach the data with a pre-defined code for deductive coding. This approach works better when testing a theory rather than when exploring a phenomenon. * Top-down approach. * Used in assessing or conducting a study on Motivation theory to a unique context

Answer 42

Categories canbeorganized inavariety of ways. * Within each category, you can group codesthat aresimilar to eachother or pertain to the same topics or general concepts. Meaning unit Because at the health facility there are trained stall, and in case you have a Problem, they would know and help you accordingly. Condensed meaning unit At the health facility, there are trained staff In case you have a problem, they would know and help you Codes Health providers are trained Competent Heath providers identify and manage complications Categories It is safer Theme Experiences related to the promotion of facility childbirth

Answer 43

- systematic (principle-oriented), - intersubjectively traceable analysis of - large amounts of text material. * It includes detailed analyses (focusing on small units of meaning) and aims at an extensive category system, which is the basis of a summarizing interpretation of the material.

Answer 44

Maying's analysis concept includes three steps: 1. Summarizing content analysis, 2. Explicative (explaining) content analysis and 3. Structuring content analysis.

Answer 45

▪ The source text is reduced to a comprehensible short version, which includes only the most important content. ▪ The aim is to reduce material and keep the essential contents. What belongs to the work process is: Paraphrasing: Crossing out elaborate phrases, transforming expressions into grammatical short forms. Generalization: Concrete examples are generalized. Reduction: Similar paraphrases are summed up.

Answer 46

Answer for a Study Card: Method: Use Interviews to gather data. Data Type: Collect Qualitative Data for in-depth insights. Target Audience: University students (both male and female). Categories and Sub-Categories for Interview Questions: Usage Frequency: Times per week/month. Reasons for Use: Convenience, cost, or time-saving. Preferred Apps: Most used apps and reasons for preference. Barriers: Issues like delivery fees, app reliability, or menu options. Findings Analysis: Analyze data using Mayring's Qualitative Content Analysis by summarizing, explaining, and structuring the responses.

Answer 47

▪ Unclear text elements are made comprehensible by using extra materials (e.g. other interview sections, information about the interviewee). The objective is to extend the comprehensibility of the text.

Answer 48

The summarizing and explicative short version of the text is now **arranged and structured** according to the **theoretical set of questions**. For this purpose, you establish a category scheme and, after testing it once, you improve the category system before the final evaluation follows. Your aim is: * to filter out particular aspects * to conduct a cross-sectional study or * to assess the material under specific criteria.

Answer 49

Content structuring analysis: elaborating specific topics and contents Standardising structuring: identifying frequently recurring or theoretically interesting values. Scaling structuring: values are ranked on ordinal level.

Answer 50

1.Defining the categories * Explicitly define text elements which belong to one category. 2. Standard examples * Mentioning concrete text examples, which fall under one category and are exemplary for this category. * These standard examples have a prototype function for this particular category. 3. Coding rules * Where there are de-limitation problems among categories, formulate rules to allow for a clear correlation.

Answer 51

▪ The problem of polysemy of terms is that it can never be controlled by conducting a content analysis. Terms have multiple meanings (e.g., “Bank…). ▪ Inference to the recipients and communicator level is complicated when e.g. there is no extra survey conducted on the part of the recipients ▪ Sequential aspects that the person responsible for coding has to follow when coding evaluation data (for example). ▪ The content of a unit of analysis is constructed, according to the circumstances, across several sentences ▪ It changes the direction of evaluation from positive to negative and comes to a completely different end than its starting point. ▪ Solution: Such issues must be clearly and precisely coded in the codebook.

Answer 52

* Classic sample-to-population (statistical) generalization * Analytic generalization (qualitative) * Case-to-case transfer (transferability)

Answer 53

* Analytic generalization is most often linked with qualitative research, although it is implicitly embedded within theory-driven quantitative research as well. * In an idealized model of analytic generalization, qualitative researchers develop conceptualizations of processes and human experiences through in-depth scrutiny and higher-order abstraction. * Qualitative researchers distinguishbetweeninformationthatis relevantto all(or many) study participants, in contrastto aspectsofthe experience that areunique to particular participants. * Through inductive analysis,together with the use of confirmatory strategies that address the credibility of the conclusions, qualitative researchers can arrive at insightful, inductive generalizations regarding the phenomenon under study.

Answer 54

* Case-to-case transfer involves the use of findings from an inquiry to a completely different group of people or setting, is more widely referred to as transferability but has also been called reader generalizability * Transferability is most often discussed as a collaborative enterprise. The researcher’s job is to provide detailed descriptions that allowreaders tomake inferences about extrapolating thefindings to other settings.

Answer 55

In-Depth Exploration * Acase study in qualitative research involves a detailed and in-depth exploration of a specific individual, group, event, or phenomenon.It aims to provide a **comprehensive understanding of the case** in its **real-life context**. Contextual Analysis * The focus of a case study is on the **context and the unique characteristics** of the case. Tools commonly used in case studies include: * Surveys * Interviews * Observations * Artefacts Holistic Perspective * Acase study examines the interplay ofmultiple factorsand considers the intricaciesofthe casewithin its natural setting, allowing for a nuanced and comprehensive analysis. Inductive Approach

Answer 56

1. Define Clear Objectives 2. Select Appropriate Cases 3. Develop a Theoretical Framework 4. Use Multiple Data Sources 5. Ensure Data Quality 6. Employ Systematic Data Analysis 7. Maintain Reflexivity 8. Develop Rich Descriptions 9. Draw Conclusions and Implications

Answer 57

A case study can be descriptive, explanatory or exploratory. * **Descriptive**: In a descriptive case study, the purpose is to ‘describe’ a phenomenon in detail in its real-world context. It is used extensively in sociology and anthropology. * **Explanatory**:The study looks for causal factors to explaina particular phenomenon. The primary focus of such a case study is to explain‘why’ and ‘how’ certain conditions come into being,that is, why certain sequence of events occur or do not occur. * **Exploratory**:The purpose is to study a phenomenon with the intention of ‘exploring’or identifying fresh research questions, which can be extensively used in subsequent research studies.

Answer 58

**Answer:** 1. **Research Question:** - Why do young people aged 18-21 in the Fridays For Future movement believe they have an impact during the climate summit, and how do they perceive this impact? 2. **Type of Case Study:** - **Exploratory Case Study**: Since the focus is on exploring perceptions and understanding the reasons behind their views, this type is most suitable. 3. **Data Needed:** - **Qualitative Data**: Personal experiences, opinions, and perceptions of participants. 4. **Data Collection Methods:** - **Interviews:** Conduct in-depth interviews with participants in the 18-21 age range who attended or followed the climate summit. - **Focus Groups:** Facilitate discussions among Fridays For Future members to understand shared perceptions. - **Observational Data:** Analyze social media posts or activities related to the movement during the summit. - **Surveys:** Optional for capturing broader opinions within the movement.

Answer 59

A qualitative sample plan includes answers to the following questions: * Is the sampling relevant to your conceptual frame and research questions? * Can reliable descriptions or explanations be produced using the sampling plan selected? * Is the sampling plan feasible, in terms of time, money, manpower, and access to people under study? * Is the sampling plan effective enough for its findings to be generalisable to the entire universe of the population from which the sample is obtained? An important element in case study strategy is the relation betweentheory andcasestudy research. * A case study can aptly be used for testing the hypothesesandnotthe entire theory.

Answer 60

Thematic Analysis Constant Comparison Coding and Categorization Member Checking Triangulation of Data (multiple sources of data) Contextualization and Thick Description There are two types of validity: external and internal. External validity * Can address the issue whether the findings of a study can be carried over (generalised) to other cases. External Validity: Assesses the extent to which the results can be generalized to other groups, situations, or conditions. Key concept: generalizability. Tested through replication of results. Internal Validity: Determines how accurately causal relationships between variables are established within the study. Key concept: accuracy and control of factors. Requires consideration of all external influences to avoid false conclusions.

Answer 61

* Nominal data / variables (e.g. hair colour/nationality) * Ordinal data (variables have natural, ordered categories: e.g. hot, hotter, hottest) * Metric data (e.g. age)

Answer 62

Discrete variables - counting numbers with specific values (e.g. age) * Continuous variables measurable number e.g. 1.7 feet

Answer 63

Measuring is the systematic assignment of an amount of numbers or symbols to the values of a variable, hence also to the objects.

Answer 64

Nominal (categorical, qualitative) data * Variables whose value cannot be organized in a logical sequence and which can only be distinguished by their name. (e.g. gender) Ordinal data (rank variable) * Variables that have two or more values which can also be ordered or ranked. That means that variable values have a natural sequence. (e.g. school grades). Metric data * Values that can be organized according to size AND can represent a multiple of a unit (e.g. height). Ordinal Data: Data with values that can be ranked or ordered, but the exact difference between values is unknown. Example: School grades (A, B, C) or race positions (1st, 2nd, 3rd). Metric Data: Data with values that can be ordered and have meaningful differences, including a measurable unit. Example: Height (120 cm, 130 cm) or temperature (20°C, 30°C).

Answer 65

Discrete variables * A discrete variable is one that has specific values and cannot have values between these specific values. (e.g. school grades, gender, number of mistakes) Continuous variables * There is in theory an infinite number of values between any two values that a continuous variable can take. (e.g. size of screws, height, age).

Answer 66

Difference Between Interval and Ratio Scales Interval Scale: Measures data where equal intervals between values represent equal differences. No true zero: A value of zero does not mean "nothing" (e.g., zero degrees Celsius doesn't mean no temperature). Examples: Temperature in Celsius or Fahrenheit. IQ scores. Ratio Scale: Measures data where equal intervals exist, and there is a true zero point, meaning zero indicates "none" or "nothing." Allows for comparisons like "twice as much" or "half as much." Examples: Height (e.g., 0 cm means no height). Weight (e.g., 0 kg means no weight). Income (e.g., $0 means no income).

Answer 67

An index is formed by summing up several single indicators. Basis for the ranking: * An extensive questionnaire * Different indicators (pressure, corruption), moderators, impact factors * The index is the sum of all measured variables and hence it is itself a new variable. * The index must comprehensively and one-dimensionally represent the area of variables, that is the indicators of a theoretical construct. * The measurements that enter an index must all have the same measuring level, e.g. nominal, dichotomous scales.

Answer 68

Scales (or rating scales) are measuring instruments that consist of several individual measurements (items), which all cover identical or similar variables and refer to the same theoretical construct ▪ Likert scale: ▪Consists of several at least 5-stage items (interval scaled), which are combined to an index by summation. ▪ Semantic differential: ▪Consists normally of about 10 to 20 opposing pairs. Typical usage of a Likert scale with the aim to empirically determine the meaning of a concept. Multipoint rating option.

Answer 69

* Honesty * Transparency * Independence * Responsibility * Scientific integrity * Follow guidelines of good research practice

Answer 70

* Currency: timeliness of the information * Relevance: consider the audience and compare with a variety of sources * Authority: origin of source and credentials * Accuracy: reliability, check evidence and bias errors * Purpose: identify type of information – fact/opinion- and the intent of the author.

Answer 71

* Working with relationships * Multivariate analysis methods are used in the evaluation and collection of statistical data **to clarify and explain relationships** between **different variables** that are associated with this data

Answer 72

* Regression analysis helps in a randomized trial to test for imbalances * Measure the **influence of one variable** on **another variable** * Helps predict a variable through one or more variables Example: Does your age, level of education, and the amount of hours of your work have a connection to the amount of money you earn? * The salary in this case would be the dependent variable * The age, level of education, and the amount of hours of work would be an independent variable Regression models provide a mathematical representation between a set of explanatory variables and a response variable. * The coefficients in a regression model indicate how much we expect the response to change when the explanatory variable is observed to change. * Regression-to-the-mean occurs when more extreme responses revert to nearer the longterm average * Regression models can incorporate different types of response variable, explanatory variables and nonlinear relationships.

Answer 73

* The coefficients in a regression model indicate how much we expect the response to change when the explanatory variable is observed to change. * Regression-to-the-mean occurs when more extreme responses revert to nearer the longterm average * Regression models can incorporate different types of response variable, explanatory variables and nonlinear relationships.

Answer 74

* Simple Linear * One independent variable infers dependent variable * Example: education level to predict salary * Multiple Linear * Several independent variables: education level, age, time to predict salary * Logistic * Categorical variable. Variable is yes or no * Is a person at risk of burnout? * Dependent variable in all cases can be metric, ordinal or normal

Answer 75

Allows us to study the relationship between two variables. * Linear regression aims at finding the best-fitting straight line through the points. * The best-fitting line is known as the regression line. * If data points are closer when plotted to making a straight line, it means the correlation between the two variables is higher. Here, the relationship is strong. * Example: the regression line shows the predicted score on ecommerce sales for each possible value of the online advertising costs

Answer 76

Assesses the relationship between a dependent variable and an independent variable * Example: relationship between the age and price for used cars sold in the last year by a car dealership company

Answer 77

Assesses the relationship between multiple independent variables Non-collinearity * Independent variables need to show a minimum correlation with one another. * If independent variables are highly correlated with each other, it will be difficult to assess the true relationships between the dependent and independent variables. Example: more than one explanatory variable * Predicted values of the dependent variable (heart disease) across the full range of observed values for the percentage of people biking to work. * The effect of smoking on the independent variable, researchers calculated these predicted values while holding smoking constant at the minimum, mean, and maximum observed rates of smoking.

Answer 78

* Variable: a logical grouping of attributes * A variable is anything that can take on different values. For example, height, weight, age, attitude, and IQ are variables because there are different heights, weights, ages, races, attitudes, and IQs. By contrast, if something cannot vary, or take on different values, then it is referred to as a constant. * Dependent Variable * Independent Variable Cases: are the things that- we are observing and to which we are assigning values. Cases: counties; variable: type of voting equipment; values: manual mark, punch card, optical scan, electronic

Answer 79

The dependent variable is called “dependent” because it is influenced by the independent variable. * For example: a researcher may be interested in examining the effects of a new medication on symptoms of depression among college students. * Dependent variable: a measure of depression (because it is influenced by - i.e., is dependent on- * Independent variable (i.e., the medication). * The dependent variable is a measure of the effect (if any) of the independent variable.

Answer 80

Independent Variable * The independent variable is called “independent” because it is independent of the outcome being measured. The independent variable is what causes or influences the outcome. * The independent variable is the factor that is manipulated or controlled by the researcher. (e.g. how much medication?) * In most studies, researchers are interested in examining the effects of the independent variable.

Answer 81

❖Control in Experiments: In an experiment, the independent variable is the one the researcher actively changes or assigns to participants. For example, in a study on the effect of sleep on cognitive performance, the researcher can manipulate the amount of sleep participants receive (e.g., 4 hours vs. 8 hours). ❖Cause-and-Effect Relationship: By manipulating the independent variable, resulting changes in the dependent variable helps determine causality. E.g., if increasing study time (independent variable) leads to higher test scores (dependent variable), a causal link can be inferred/concluded. ❖Distinction from Non-Manipulable Variables: Not all independent variables can be manipulated directly. Variables like age, gender, or socioeconomic status can only be measured or categorized but not manipulated. These are often treated as quasi-independent variables in research. In true experimental designs, manipulation is a defining characteristic of the independent variable.

Answer 82

Refers to the deliberate adjustment or control of a variable by researchers in an experiment to determine its causal effect on the dependent variable. The independent variable is the factor hypothesized to influence the outcome being studied, and its manipulation is key to testing causal relationships.

Answer 83

To Test Causation: * Manipulating the independent variable allows researchers to observe changes in the dependent variable. ✓ For example, adjusting the intensity of light in a room (independent variable) can help study its effect on reading speed (dependent variable). Control of Confounding Factors: ✓ Manipulating only one variable ensures that any observed effect is due to that variable and not external factors. Hypothesis Testing: ✓ Experiments often involve predictions about how changes in an independent variable will affect the dependent variable. Manipulation directly tests these predictions.

Answer 84

* Setting Different Conditions or Levels: * Example: To study the effect of caffeine on memory: * Group 1: No caffeine * Group 2: 50 mg caffeine * Group 3: 100 mg caffeine * Random Assignment: * Participants are randomly assigned to groups to ensure fairness and eliminate bias. * Direct Changes: * Physically or contextually altering the independent variable. * Example: Changing the temperature in a room to observe its effect on productivity.

Answer 85

Direct Manipulation: ❖ Physically changing variables, such as room temperature or noise levels. Instructional Manipulation: ❖ Varying instructions given to participants (e.g., “focus on speed” vs. “focus on accuracy”). Event Manipulation: ❖ Altering the occurrence of events (e.g., showing participants happy or sad videos). Importance of Manipulating Independent Variables: I. It distinguishes experiments from observational studies. II. It enables researchers to make causal inferences. III.It increases the study's internal validity by reducing alternative explanations. By deliberately changing the independent variable, researchers can identify its precise effect on the dependent variable, leading to robust and reliable conclusions. Importance of Manipulating Independent Variables: I. It distinguishes experiments from observational studies. II. It enables researchers to make causal inferences. III.It increases the study's internal validity by reducing alternative explanations. By deliberately changing the independent variable, researchers can identify its precise effect on the dependent variable, leading to robust and reliable conclusions.

Answer 86

* Interval/ratio variable * Ordinal variables * Nominal variables * Dichotomous variables

Answer 87

Here’s the explanation in English: --- **1. Interval/Ratio Variables:** **Definition**: Quantitative variables with equal intervals between values. Ratio scales have a true zero point, while interval scales do not. - **Interval variables**: Zero does not mean the absence of the value. **Example**: Temperature in Celsius (0°C does not mean "no temperature"). - **Ratio scales**: Zero means the absence of the measured quantity. **Example**: Weight (0 kg = no weight). --- **2. Ordinal Variables:** **Definition**: Qualitative variables that are ordered, but the difference between values is unknown or inconsistent. - **Example**: Satisfaction ratings (1 = dissatisfied, 5 = very satisfied). The order matters, but the intervals between levels are not equal. --- **3. Nominal Variables:** **Definition**: Qualitative variables that represent categories without any order. - **Example**: Eye color (blue, green, brown), nationality, car type. --- **4. Dichotomous Variables:** **Definition**: A special case of nominal variables with only two categories. - **Example**: Gender (male/female), answers to a question (yes/no). --- Key Differences: - **Interval/ratio** — quantitative variables. - **Ordinal/nominal/dichotomous** — qualitative variables.

Answer 88

* Nominal and ordinal variables are categorical variables - their values divide up cases into distinct categories. * The values of nominal-level variables have no inherent order. The variable eye colour can take on brown, blue, and green eyes; major— political science, sociology, biology, etc.

Answer 89

The values of ordinal-level variables have an inherent order. * These values can be placed in an order that makes sense—first to last (or last to first), least to most, best to worst, and so on

Answer 90

We want to establish an ordinal order to our variables * To analyze data we want to organize it

Answer 91

We want to transform a variable by combining some of its categories or values. * Example: we want to change a continuous variable into an ordinal categorical variable or merge the categories of a nominal variable. * In PSPP,this type of transform is called recoding. * Today, we want to recode our variables to merge them

Answer 92

If the p-value (probability value) from the test is less than the significance level (α = 0.05): ➢ You reject Ho . ➢ This means the data provides strong evidence against the null hypothesis. ➢ You conclude that there is a statistically significant effect or relationship in the data. Example: Rejecting Ho means there is a significant association between the variables.

Answer 93

With Likert scale data we cannot use the mean as a measure of central tendency as it has no meaning: what is the average of strongly agree and disagree? The most appropriate measure of it is the mode - the most frequent responses, or the median. The best way to display the distribution of responses i.e. (% that agree, disagree etc) is to use a bar chart.

Answer 94

Assign numbers to the scale from 1-5, you could assign 1 to ‘very poor’ and assign 5 to ‘excellent’ depending on what the scale measures. Draw a table for your results, you can have headings on both axes of the table. The questions would be on one axis and the figure ratings from the last step on the other. Fill in the number of times each rating appears (mode)

Answer 95

If the data are ordinal, we can say that one score is higher than another. We cannot say how much higher, as we can with interval data, which tell you the distance between two points.

Answer 96

1. Experimental: measurements before and after intervention establishes causality 2. Descriptive: measurements made only once establishes only correlations between variables

Answer 97

Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set.

Answer 98

Inferential statistical techniques are used to analyze the sample's behavior. These include the models used for regression analysis and hypothesis testing. The first-step sample is used to draw conclusions. Assumptions or predictions about the entire population are used to draw inferences. **Inferential Statistical Techniques** are methods in statistics used to draw conclusions about a larger group (population) based on data from a smaller group (sample). These techniques help test hypotheses, make predictions, and estimate population parameters. --- Key Points: 1. **Purpose**: To learn about the entire population by analyzing a sample. 2. **Tools**: Use probability to determine how accurate the conclusions are. --- Examples: 1. **T-test**: Tests if there is a difference between the means of two groups. **Example**: Comparing the average income of men and women. 2. **ANOVA**: Analyzes the difference in means across three or more groups. **Example**: Comparing student performance across different schools. 3. **Regression Analysis**: Identifies relationships between variables. **Example**: Studying how education level affects salary. 4. **Confidence Intervals**: A range where the true value of a parameter is likely to fall. **Example**: The average height of a population is between 165 and 175 cm with 95% confidence. --- Difference: Unlike **descriptive statistics**, which simply describe the data, **inferential statistics** allow you to make predictions and test assumptions about the broader population.

Answer 99

Parametric tests assume that the data are continuous and follow a normal distribution. Although, with a large enough sample, parametric tests are valid with nonnormal data. The 2-sample t-test is a parametric test. Nonparametric tests are accurate with ordinal data and do not assume a normal distribution. There is a concern that nonparametric tests have a lower probability of detecting an effect that actually exists. The Mann-Whitney U test is an example of a nonparametric test. Here are examples of **parametric** and **nonparametric tests**: --- **Parametric Tests**: 1. **T-test (Student’s t-test)**: - **Example**: Compare the average height of men and women. - Group 1: Heights of men (in cm). - Group 2: Heights of women (in cm). 2. **ANOVA (Analysis of Variance)**: - **Example**: Compare the average test scores of students from three different schools. - Group 1: Scores of students from School A. - Group 2: Scores of students from School B. - Group 3: Scores of students from School C. 3. **Pearson’s correlation**: - **Example**: Study the relationship between hours spent studying and exam results. --- **Nonparametric Tests**: 1. **Mann-Whitney U test**: - **Example**: Compare customer satisfaction levels in two stores (1–5 scale, where 1 = very dissatisfied, 5 = very satisfied). - Group 1: Ratings from Store A. - Group 2: Ratings from Store B. 2. **Kruskal-Wallis test**: - **Example**: Compare customer satisfaction levels across three restaurants. - Group 1: Ratings from Restaurant X. - Group 2: Ratings from Restaurant Y. - Group 3: Ratings from Restaurant Z. 3. **Spearman’s rank correlation**: - **Example**: Study the relationship between stress levels (ranked) and hours of sleep. --- **Key Difference in Data**: - **Parametric**: Continuous data with a normal distribution (e.g., height, weight, scores). - **Nonparametric**: Ordinal or continuous data without a normal distribution (e.g., ranks, satisfaction ratings).

Advanced Empirical Methods Flashcards

(123 cards)