Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
An overview of social research methods, focusing on the role of theories, epistemological considerations, values and ethical considerations, and implications for practice. It covers various research approaches, including inductive and deductive research, objectivism and constructionism, interdisciplinary research, and empiricism. The document also discusses different research methods, such as participant observation, symbolic interactionism, grounded theory, and various data collection techniques.
Typology: Exams
1 / 32
Why is social research important? - Social research allows us to test ideas/ assumptions in a structured way. Knowing how to perform/evaluate research is important whenever you get the question: Why do you think so? Hypothesis - an informed speculation, which is set up to be tested, about the possible relationship between two or more variables. A hypothesis should be testable and falsifiable. Context of social research methods - social research and social research methods are embedded in wider contextual factors. They are not practised in a vacuum. Contextual factors -
Epistemological considerations - raise questions on how the social world should be studied and whether a scientific approach is the right stance.
Constructionism - social phenomena are dependent on social actors and social interaction; focus on change; subjectivity (also as a researcher!) -> You can not objectively observe. -> E.g. culture; emotions (depend on experiences of individuals). Deductive approach - research is conducted with reference to hypotheses that are inferred from theory (Theory/Hypothesis ---> Findings) -> Associated with quantitative approach Inductive approach - theory is generated from research (Findings ---> Theory) -> Associated with qualitative approach Quantitative research (commonly deductive) - research strategy that relies on the collection of numerical data, and the statistical analysis of those numbers. -> E.g. (i) Perform an online survey with a questionnaire about environmental attitudes among players and non-players. (ii) Perform an experiment in which people play a serious game in the lab after which you measure environmental attitudes. Qualitative research (commonly inductive) - research strategy with an emphasis on the description or interpretation of data, commonly expressed in words. -> E.g. (i) Become a member of a gaming community and have conversations with players. (ii) Form a focus group inviting people who play serious games. Interdisciplinary research -
integrated data, methods, tools and concepts form different sciences in order to create a common understanding of a complex issue or question. -> E.g. integration of law and psychology in research on 'unbiased sentencing'. Elements of the process of social research - social research practice comprise elements that are common to all or at least most forms of social research.
Research question - an explicit statement in the form of a question of what it is that a researcher intends to find out about. A research question not only influences the scope of an investigation but also how the research will be conducted. -> E.g. what is the extent of brain damage in Phineas Gage? (neuroscience's most famous patient) Case study - analysis of a single case. The term is sometimes extended to include the study of just two or three cases for comparative purposes. However, a multiple-case study is the more common term for the examination of two or more cases. Content analysis - an approach to the analysis of documents and texts that seeks to qualify content in terms of predetermined categories and in a systematic and replicable manner. The term is sometimes used in connection with qualitative researcher as well. Representative samples - a sample that reflects the population accurately, so that it is a microcosm of the population. Population - the universe of units from which a sample is to be selected. Survey research - a cross-sectional design in relation to which data is collected predominately by self-administered questionnaires or by structured interview on a sample of cases drawn from a wider population and at a single point in time in order to collects a body of quantitative or quantifiable data in connection with a number of variables. These are then examined to detect patterns of relationships between variables. Qualitative methods of sampling - purposive, snowball, and theoretical sampling. Purposive sampling -
non-random sampling of people who are relevant to the RQ. Snowball sampling - sampling where subjects propose other subjects/spread the word to other subjects. Theoretical sampling - sampling guided by the emerging theory. Data collection - Structured interview: a research interview usually in the context of survey research in which all respondents are asked exactly the same questions in the same order with the aid of a formal interview schedule. Participant observation: research in which the researcher immerses himself in a social setting for an extend period of time, observing behaviour, listening to what is said in conversations both between others and with the field worker, and asking questions (e.g. ethnography). Semi-structured interviewing: a term covering wide range of interview types. Data analysis - The application of statistical techniques to data that has been collected. Transcription: creating a text version of a recorded interview or focus group session. Thematic analysis: the extraction of key themes in one's data in connection with qualitative data. Coding: a process whereby data is broken down into its component parts and those part are then given labels. Codes: numbers assigned to data about people or other unit of analysis when the data is not inherently numerical. Quantitative analysis: - the application of statistical techniques to the data to test your hypotheses. Qualitative analysis: - analysis that focuses on the description, understanding, or interpretation of information.
Writing up - the reporting and dissemination of results. The messiness of social research - although we can attempt to formulate general principles for conducting social research, we have to recognize that things do not always go entirely to plan. Symbolic interactionism - a theoretical perspective in sociology and social psychology that views social interaction as taking place in terms of the meanings actors attach to action and things. Grounded theory - an iterative approach to the analysis of qualitative data that aims to generate theory out of research data by achieving a close fit between the two Empiricism - a general approach to the study of reality that suggests that only knowledge gained through experience and the sense is applicable. -> Ideas must be subjected to rigorous testing before they can be considered knowledge. Naive empiricism - the belief that the accumulation of facts is a legitimate goal in its own right. Naive/empirical realism - through the use of appropriate methods, reality can be understood. -> Assumes that there is a perfect correspondence between reality and the term used to describe it. Critical realism -
a specific form of realism that recognizes the reality of the natural order and the events of the social world and which holds that the social world can only be understood when identifying the structures that generate these events. Retroductive research - making an inference about the causal mechanism that lies behind and is responsible for regularities that are observed in the social world. Operationalization (How do you make your research question testable?) - Translate the theoretical concepts in your research question into measurable and controllable variables (also called indicators). Variable - an attribute in terms of which cases vary. Constant - an attribute that does not vary. Dependent variable - what you measure or observe (this is not changed). Independent variable - what you change, control or manipulate in order to measure the effect on the dependent variable. -> N.B. many variables in social research are non-manipulable! (e.g., gender, ethnicity, social status) Reliability (stability) - is concerned with the question of whether the results of a study measure consistently over time. Test-retest stability - refers to when test results are equal/similar in two different points in time.
Validity - indicates whether the conclusions of a research study are well-founded. Internal validity - the causal relationship between variables is real, i.e. there is causality. Causality - a change in one variable is the consequence of another variable. -> A concern with establishing a "cause and effect" connection between variables, rather than the mere relationship between them. Threats to internal validity - -History (e.g., world-events) -Testing (e.g., habituation) -Instrumentation (e.g., questionnaire is updated) -Mortality (e.g., selective withdrawal) -Maturation (e.g., becoming practiced) -Selection (i.e., non-random assignment) -Confounding variables Confounding variables - variables beyond the operationalized dependent or independent variables that could influence the findings. Naturalism - a rare instance of a term with different and contradictory meanings. (i) viewing all objects of study (natural & social) as belonging to the same realm. -> Consequent commitment to the principles of natural scientific method.
(ii) being true to the nature of the phenomenon being investigated. (iii) a style of research that seeks to minimize the intrusion of artificial methods of data collection. External validity - a concern with the question of whether the results of a study can be generalized beyond the specific research context in which it was conducted. -> The experimental setting of a study has an effect in the results of the study. Threats to external validity - -Interaction of selection and treatment -Interaction of setting and treatment -Interaction of history and treatment -Interaction effects of pretesting -Reactive effects of experimental setting Ecological validity - findings are applicable to everyday life. Measurement validity - indicators really measure the concept in question. -> face validity: does the measure reflect the concept in question, e.g. does it reflect depression at face value? -> concurrent validity: do we also see expected effects/correlations with variables not relevant to study? -> convergent validity: are the findings of a measure comparable with findings from other methods that measure the same concept? Replication/reproducibility - the degree to which the results of a study can be reproduced (vs. 'reliability' which shows the stability of a measurement at different times).
Research design - provides a framework for the collection and analysis of data. -> The choice of research design reflects the priority being given to a range of dimensions in the research process. Cross-sectional design - a research design entailing the collection of data on a sample of cases in connection with two or more variables and at one point in time. -> Yields: Quantitative data -> Examines relationships between variables (i.e., correlation) -> Doesn't provide conclusions about causality (= weak internal validity)! Longitudinal design - surveys the same sample across two or more different time points. -> E.g. Panel study & Cohort study Comparative design - Comparing two or more cases, or two or more samples. Often quantitative in survey form, but can also be qualitative. -> E.g., cross-national research, cross-cultural research, cross-institutional research (Do people in different cultures recognize emotions similarly?). Panel study - longitudinal-design study of a large sample that is followed over time (study age and cohort). -> E.g. Understanding society; studying a panel of 40.000 households from the United Kingdom; adults are interviewed every 12 months on finances, employment, expectations, family and friends etc. Cohort study -
longitutional-design study of a sample consisting of a group that experiences some event (such as being born) in a selected time period (can only study age). -> E.g. The Up series: 8 documentaries spanning 49 years following the lives of fourteen British children since 1964. Classic experimental design (randomized controlled trial) -
Demand characteristics - participants in experiments may adjust their behavior/responses according to what they believe is expected. Reactive effect - bias in responding because participants know that they are being studied. Single-blinde experiment - information that could bias the results is withheld from the participants. Double-blinde experiment - information that could bias the results is withheld from both the participants and the experimenter. Meta-analysis - a quantitative statistical analysis of several separate but similar experiments/studies in order to test the pooled data for statistical significance. Steps in the publication process - -Decide on journal -Submit a paper -Paper is peer reviewed Peer review - papers are evaluated by experts in the relevant topic. These reviewers, together with the action editor, decide whether the paper merits publication. Impact factor (IF) - a measure reflecting the average number of citations per paper published in a journal during the two preceding years.
Plagiarism - passing off the ideas or words of someone else as one's own or use the ideas or words of someone else without crediting the source. General setup of research article - -Introduction (literature review, concepts and theories, research question, & hypothesis) -Methods (sample selection & data collection) -Results (results & data analysis) -Discussion (implications & improvements) Publication bias - Findings are selectively published based on whether they show positive results or not. This "File drawer effect" is especially common for papers that have: (i) null findings (e.g., non-significant results) and (ii) non-confirmatory results The issue of publishability - The aim of science to challenge prevailing assumptions and to generate novel ideas and evidence may create drive for publishing novel, exciting results that are not stable and cannot be replicated. Solution to problems of publication process - Open-Access Science: scientific articles are available online without paying a fee to the publisher. Instead, the scientists pays a fee to have the paper published. ASKING QUESTIONS - -Identify the most often used question types in survey research -Understand and reproduce commonly made errors when designing a questionnaire Self-report questionnaire/self-administered questionnaire -
a questionnaire that the respondent answers without the aid of an interviewer (e.g. digitally through source). Structured interview - a research interview usually in the context of survey research in which all respondents are asked exactly the same questions in the same order with the aid of a formal interview schedule. Pros and Cons of structured interviews - Pros: -interviewer can probe/prompt the respondent -interviewer can help clarifying -personal Cons: -interviewer can evoke social desirable responses -slow and expensive -interviewer effects -interviewer variability Pros and Cons of self-report survey (vs. interview) - Pros: -No interviewer effects/variability -Convenient -Quick & cheap -Sensitive questions Cons: -less room for open-ended/complex questions -Cannot ensure that the 'right' person answers -Cannot collect additional data -Risk for respondent fatigue
-Greater risk of missing data -Lower response rates Pros and Cons of online questionnaires (vs. paper & pencil) - Pros: -Quick & cheap -Many options for formatting -Broad range -Data accuracy -Control the flow of the questionnaire Cons: -Lower response rate -Restricted to online populations Types of questions - -Personal-factual information (including behavior) What is your country of citizinship? -Factual questions about others How often does your partner cook dinner? -Normative standards or values Do you support the death penalty? -Knowledge Who is the current president of the United States? -Attitudes Did you like maths in high school? -Beliefs Should the EU have stricter immigration laws? -Behavioral intentions Would you invite this person for a dinner party?
-Feelings/sensations How angry does this movie clip make you feel? Open questions - a question that does not present the respondent with a set of possible answers to choose from. Pros: -respondents answer on their own terms -allow for new, unexpected responses -exploratory Cons: -time consuming -difficult to code Closed questions - a question that presents the respondent with a set of possible answers to choose from. Pros: -quick and easy -precoded -easy to compare answers Cons: -restrictive range -not exhaustive Common errors in survey questions -
a hypothetical situation in story form, after which participants are asked for behavioral intentions, values, beliefs, norms etc. Experience/event sampling - various methods that seek to capture affective states and/or behaviour at certain points in time. These 'points in time' are determined by the researcher and when they occur, research participants have to record such things (e.g. what they are doing or how they are feeling). -> E.g. diary studies Diary studies - a research method that collects qualitative information by having participants record entries in a log or diary about the activity or experience being studied (longitudinal technique). -> offers vast amount of contextual information without the costs of a true field study. Response bias - participants are answering questions in a biased way. -> Examples: extreme responding, mild responding, acquiescence bias -> Counter method: reverse questions, instruct participants to use the full scale Social desirability bias - the tendency for participants to respond in such a way that they think is seen as favorable by others. -> Can impact the measurement relating to controversial issues. -> Can inflate the response to questions about health, intelligence, benevolent behavior etc. QUANTITATIVE RESEARCH - Content: Sampling, structured observation, content analysis -Identify the most important sampling techniques in social sciences -Describe important considerations when using content analysis -Describe important considerations when performing structured observation
Sample - the segment of the population that is selected for research. A sample consist commonly of people (research participants), but it can also consist of other "objects" (e.g., books, internet sites, bacteria) Sampling frame - the listing of all units in a population from which the sample will be selected. Random sampling - a sample that reflects the population accurately because each unit has an equal probability of selection. E.g. the sample has similar demographic characteristics as the population. Sampling bias - a distortion in the representativeness of the sample that arises when members if the population (sampling frame) stand little or no chance of being selected for inclusion in the sample. -> Caused by the use of a non-random sampling method Probability sample - sample selected using random sampling. Types of probability samples: simple random sample, systematic sample, stratified random sample Systematic sample - each unit is selected from the sampling frame according to fixed intervals (e.g. every 5th unit). Stratified sampling - each unit is randomly sampled from a population that has been divided into categories (strata). Sampling error -
error in the findings derived from differences between random sample and population. -> Note that sampling error can occur even with a probability sample! -> As sample size increases, sampling error decreases. -> The greater the heterogeneity of the population the larger a sample will need to be. Non-sampling error - error in the findings due to the differences between the population and the sample that arises either from deficiencies in the sampling approach (e.g. due to inadequate sampling frame) or non- response, or other methodological & analytical mistakes. Non-response - refers to the event where some members of the sample refuse to cooperate, cannot be contacted, or for some other reason cannot supply the required data. Census - the enumeration of an entire population. Attrition - sample gets smaller in number because participants drop-out of the study for various reasons. Convenience sample - the researcher simply uses what he/she can get (i.e., what is available) -> Convenience samples are rarely representative Convenience samples in psychology - it is very expensive, and impractical to use a true representative sample in psychological laboratory experiments as psychology seeks principles of behaviour that should hold for all humans. Purposive sample -
researcher may look for subjects with certain characteristics, because these characteristics are relevant to the research question. Matching - the matching of subjects across different samples on certain characteristics (i.e., gender, age, economic status). When do you need a representative sample? - when the research explicitly aims to generalize its findings to a real-world population. Content analysis - an approach to the analysis of documents and text that quantifies content in terms of predefined categories. Sources for content analyses - -Mass media (e.g., TV, newspapers, internet) -Written sources (e.g., books, plays) -Oral sources (e.g., speeches, radio) -Visual data (e.g., photographs, paintings) -State documents (e.g., policy documents) -Personal documents (e.g., tweets, personal archives) Types of content that can be analysed - -Significant actors -Words (e.g., frequency) -Subjects and themes -Dispositions (e.g., ideology) -Context (e.g., placement of the article) Intra-coder reliability -
the consistency with which a researcher codes. Inter-coder reliability - the consistency (amount of agreement) between the coding of different researchers. Characteristics of coding schemes - -Mutually exclusive categories -Exhaustive categories -Clear instructions to coders Structured observation - a method of systematically observing people's behaviour -By grouping behaviour into categories -Aggregates and compares behaviour of everyone in the sample Advantages of structured observation - Exposes what do people really do, instead of what they say they do. Counters issues such as: -Social desirability effect -Gap between intended and actual behavior -Memory bias concerning behavior Disadvantages of structured observation - -Expensive, time consuming -Presence of the observer (i.e., observer bias) -How can we know what the intention or motivation behind the behavioris? -You need consistency across observations! -Ethical consideration
Types of observation - (i) Observation in the laboratory (ii) Naturalistic observation: subjects are observed in their natural environment without any manipulation by the researcher. Secondary analysis - the analysis of data by researchers who were not involved in the collection of that data, for purposes that might not have been envisaged by those responsible for the data collection. -> E.g. Use of official statistics (collected by state agencies) by economists, policy makers, applied psychologists, etc.. Advantages of secondary analysis - -reduced time and cost -removes problem of reactivity Reactivity - the response of research participants to the fact that they know they are being studied. -> Results in untypical behaviour Criticisms of quantitative research - -Failure to distinguish between objects in the natural world and social phenomena -Artificial and spurious sense of precision and accuracy -Lack of ecological validity -Static view of social life Experimental methods - Content: Important steps when setting up an experiment -Reproduce the strengths and weaknesses of experimental design -Understand and reproduce important aspects of experimental design -Explain the difference between a 'between' and 'within' subjects design
Characteristics of experimental research - -Study effects in isolation -Control important factors -Draw conclusions about causality (experiments allow for conclusions about causality) -> Note the importance of internal validity for experimental designs! Steps in experimental research - -Literature review -Concepts and theories -Formulate a research question -Formulate a hypothesis (a priori) -Decide on your sample -Collect data -Analyze data (test your hypothesis) -Report results Confirmatory research - research that tests priori hypothesized relationships between variables. Exploratory research - research that explores data for possible relationships between variables. Control group/condition - group in which variables that also occur in the experimental group/condition are kept the same, except the independent variable of interest. -> If you find a difference between groups, then you have to be sure that this difference can be attributed to the manipulation, and not to other factors. -> Counters threats to internal validity such as: maturation, expectation, learning, habituation, etc.