Wordsmyth and more
Frequently asked questionsWhat is the definition of a hypothesis. A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question. A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data). Frequently asked questions: MethodologyQuantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color. To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature. Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes. Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods . To define your scope of research, consider the following:
Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample . Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation . The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode. Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity . Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs . On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure. Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods. Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :
Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease. Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:
Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research. Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased . Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something. While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something. Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity. Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity. Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level. When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure. For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test). On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover. A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives. Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching. In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity. The higher the content validity, the more accurate the measurement of the construct. If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question. Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ). On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.
Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion. The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language. Reproducibility and replicability are related terms.
Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants. Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random. Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample . This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research . Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias . Snowball sampling is best used in the following cases:
Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups. The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ). Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample. On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data. Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants. However, in convenience sampling, you continue to sample units or cases until you reach the required sample size. In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population. A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population. Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics. Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population . When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method . This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling . The two main types of social desirability bias are:
Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias . Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel. Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible. A systematic review is secondary research because it uses existing research. You don’t collect new data yourself. Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication. Scientists and researchers must always adhere to a certain code of conduct when collecting data from others . These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity. Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe. Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud. These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure. Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations . You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos. You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals. Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication. For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded. In general, the peer review process follows the following steps:
Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process. Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication. Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript. However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.
Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity . If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results. Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment . Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic. Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research. Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way. Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason. You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it. To implement random assignment , assign a unique number to every member of your study’s sample . Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups. Random selection, or random sampling , is a way of selecting members of a population for your study’s sample. In contrast, random assignment is a way of sorting the sample into control and experimental groups. Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study. Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable. In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic. Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors. Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry. Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data. For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do. After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values. Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured. In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing. Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these. Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities. Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias . The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants. Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics . You can use several tactics to minimise observer bias .
Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting. The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects. Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation. You can think of naturalistic observation as ‘people watching’ with a purpose. Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly. Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects. Questionnaires can be self-administered or researcher-administered. Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording. Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions. In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
Depending on your study topic, there are various other methods of controlling variables . An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways. A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups). For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables. A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires. A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined. To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement. Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution. Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them. The type of data determines what statistical tests you should use to analyse your data. A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’). A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis. Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research. Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it. Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study . Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.
Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies. The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study . Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long. A correlation reflects the strength and/or direction of the association between two or more variables.
A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research . A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables. Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables. Controlled experiments establish causality, whereas correlational studies only show associations between variables.
In general, correlational research is high in external validity while experimental research is high in internal validity . The third variable and directionality problems are two main reasons why correlation isn’t causation . The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses. Overall, your focus group questions should be:
Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups . Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes. This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly. A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews . The four most common types of interviews are:
An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic. Unstructured interviews are best used when:
A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:
The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee. There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions. A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:
More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups . When conducting research, collecting original data has significant advantages:
However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable. Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations. A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship. A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related. If something is a mediating variable :
Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships. Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds. You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect . In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design . Discrete and continuous variables are two types of quantitative variables :
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age). Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips). You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results . Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable. You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both. Yes, but including more than one of either type requires multiple research questions . For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question. You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable . To ensure the internal validity of an experiment , you should only change one independent variable at a time. To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists. A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables. Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables. There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation. In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables. In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable . In statistical control , you include potential confounders as variables in your regression . In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables. In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports). The process of turning abstract concepts into measurable variables and indicators is called operationalisation . In statistics, ordinal and nominal variables are both considered categorical variables . Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them. A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes. Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity . If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable . ‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables. Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest. An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study. A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable. There are 4 main types of extraneous variables :
The difference between explanatory and response variables is simple:
The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent. Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term. On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables. An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study. Independent variables are also called:
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable. In statistics, dependent variables are also called:
Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research . In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data. Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions. Deductive reasoning is also called deductive logic. Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions. Inductive reasoning is also called inductive logic or bottom-up reasoning. In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories. Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down. Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions. There are many different types of inductive reasoning that people use formally or informally. Here are a few common types:
It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests. While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise. Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance. Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method. Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface. Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests. You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity . When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research. Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity. Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity. There are two subtypes of construct validity.
Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented. With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised. There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect. The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings). The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures. Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results. This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa. Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors. There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition . A sampling error is the difference between a population parameter and a sample statistic . A statistic refers to measures about the sample , while a parameter refers to measures about the population . Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible. Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling . There are three key steps in systematic sampling :
Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups. For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups. You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying. Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure. For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions. In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment). Once divided, each subgroup is randomly sampled using another probability sampling method . Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame. But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples . In multistage sampling , you can use probability or non-probability sampling methods. For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study. Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. The clusters should ideally each be mini-representations of the population as a whole. There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.
Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area. However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole. If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied, If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling. The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey. Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset. Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage. This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from. In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included. Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling . Probability sampling means that every member of the target population has a known chance of being included in the sample. Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling . Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable. While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design . Advantages:
Disadvantages:
In a factorial design, multiple independent variables are tested. If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions. Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects. Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .
Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment . Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings. In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned. In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions. In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions. The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group. A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship. A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable. In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact. Triangulation can help:
But triangulation can also pose problems:
There are four main types of triangulation :
Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you. To design a successful experiment, first identify:
When designing the experiment, first decide:
Exploratory research explores the main aspects of a new or barely researched question. Explanatory research explains the causes and effects of an already widely researched question. The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants. An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either. These are four of the most common mixed methods designs :
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings. Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation. Operationalisation means turning abstract conceptual ideas into measurable observations. For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations. Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure. Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance. There are five common approaches to qualitative research :
There are various approaches to qualitative data analysis , but they all share five steps in common:
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis . In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question . Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives. Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ). In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section . In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods. The research methods you use depend on the type of data you need to answer your research question .
Ask our teamWant to contact us directly? No problem. We are always here for you.
Our support team is here to help you daily via chat, WhatsApp, email, or phone between 9:00 a.m. to 11:00 p.m. CET. Our APA experts default to APA 7 for editing and formatting. For the Citation Editing Service you are able to choose between APA 6 and 7. Yes, if your document is longer than 20,000 words, you will get a sample of approximately 2,000 words. This sample edit gives you a first impression of the editor’s editing style and a chance to ask questions and give feedback. How does the sample edit work?You will receive the sample edit within 24 hours after placing your order. You then have 24 hours to let us know if you’re happy with the sample or if there’s something you would like the editor to do differently. Read more about how the sample edit works Yes, you can upload your document in sections. We try our best to ensure that the same editor checks all the different sections of your document. When you upload a new file, our system recognizes you as a returning customer, and we immediately contact the editor who helped you before. However, we cannot guarantee that the same editor will be available. Your chances are higher if
Please note that the shorter your deadline is, the lower the chance that your previous editor is not available. If your previous editor isn’t available, then we will inform you immediately and look for another qualified editor. Fear not! Every Scribbr editor follows the Scribbr Improvement Model and will deliver high-quality work. Yes, our editors also work during the weekends and holidays. Because we have many editors available, we can check your document 24 hours per day and 7 days per week, all year round. If you choose a 72 hour deadline and upload your document on a Thursday evening, you’ll have your thesis back by Sunday evening! Yes! Our editors are all native speakers, and they have lots of experience editing texts written by ESL students. They will make sure your grammar is perfect and point out any sentences that are difficult to understand. They’ll also notice your most common mistakes, and give you personal feedback to improve your writing in English. Every Scribbr order comes with our award-winning Proofreading & Editing service , which combines two important stages of the revision process. For a more comprehensive edit, you can add a Structure Check or Clarity Check to your order. With these building blocks, you can customize the kind of feedback you receive. You might be familiar with a different set of editing terms. To help you understand what you can expect at Scribbr, we created this table:
View an example When you place an order, you can specify your field of study and we’ll match you with an editor who has familiarity with this area. However, our editors are language specialists, not academic experts in your field. Your editor’s job is not to comment on the content of your dissertation, but to improve your language and help you express your ideas as clearly and fluently as possible. This means that your editor will understand your text well enough to give feedback on its clarity, logic and structure, but not on the accuracy or originality of its content. Good academic writing should be understandable to a non-expert reader, and we believe that academic editing is a discipline in itself. The research, ideas and arguments are all yours – we’re here to make sure they shine! After your document has been edited, you will receive an email with a link to download the document. The editor has made changes to your document using ‘Track Changes’ in Word. This means that you only have to accept or ignore the changes that are made in the text one by one. It is also possible to accept all changes at once. However, we strongly advise you not to do so for the following reasons:
You choose the turnaround time when ordering. We can return your dissertation within 24 hours , 3 days or 1 week . These timescales include weekends and holidays. As soon as you’ve paid, the deadline is set, and we guarantee to meet it! We’ll notify you by text and email when your editor has completed the job. Very large orders might not be possible to complete in 24 hours. On average, our editors can complete around 13,000 words in a day while maintaining our high quality standards. If your order is longer than this and urgent, contact us to discuss possibilities. Always leave yourself enough time to check through the document and accept the changes before your submission deadline. Scribbr is specialised in editing study related documents. We check:
Calculate the costs The fastest turnaround time is 24 hours. You can upload your document at any time and choose between four deadlines: At Scribbr, we promise to make every customer 100% happy with the service we offer. Our philosophy: Your complaint is always justified – no denial, no doubts. Our customer support team is here to find the solution that helps you the most, whether that’s a free new edit or a refund for the service. Yes, in the order process you can indicate your preference for American, British, or Australian English . If you don’t choose one, your editor will follow the style of English you currently use. If your editor has any questions about this, we will contact you.
Home » What is a Hypothesis – Types, Examples and Writing Guide What is a Hypothesis – Types, Examples and Writing GuideTable of Contents Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy. Types of HypothesisTypes of Hypothesis are as follows: Research HypothesisA research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments. Null HypothesisThe null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables. Alternative HypothesisAn alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate. Directional HypothesisA directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight. Non-directional HypothesisA non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight. Statistical HypothesisA statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result. Composite HypothesisA composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome. Empirical HypothesisAn empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena. Simple HypothesisA simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor. Complex HypothesisA complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome. Applications of HypothesisHypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:
How to write a HypothesisHere are the steps to follow when writing a hypothesis: Identify the Research QuestionThe first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field. Conduct a Literature ReviewBefore writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge. Determine the VariablesThe next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable. Formulate the HypothesisBased on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence. Write the Null HypothesisThe null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance. Refine the HypothesisAfter formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable. Examples of HypothesisHere are a few examples of hypotheses in different fields:
Purpose of HypothesisThe purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction. The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect. In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon. When to use HypothesisHere are some common situations in which hypotheses are used:
Characteristics of HypothesisHere are some common characteristics of a hypothesis:
Advantages of HypothesisHypotheses have several advantages in scientific research and experimentation:
Limitations of HypothesisSome Limitations of the Hypothesis are as follows:
About the authorMuhammad HassanResearcher, Academic Writer, Web developer You may also likeInformed Consent in Research – Types, Templates...Research Approach – Types Methods and ExamplesAPA Table of Contents – Format and ExampleEvaluating Research – Process, Examples and...Data Analysis – Process, Methods and TypesResearch Paper Conclusion – Writing Guide and...Metaphor in Pragmatics: Literal Meaning, Metaphorical Meaning and Other Dangerous Things
Cite this chapter
Part of the book series: UNIPA Springer Series ((USS)) 90 Accesses This chapter responds to criticisms of Conceptual Metaphor Theory by offering an alternative framework for the construction of metaphorical meaning. It provides a comprehensive overview of how the pragmatic literature explores the interplay between literal and metaphorical meanings. The first paragraph discusses the traditional dichotomy between indirect and direct access hypotheses to metaphorical meaning. Then we will examine Giora’s Graded Salience Hypothesis, showing that these hypotheses describe different types of metaphor rather than mutually exclusive options. The third paragraph looks at the radical contextualist hypothesis within Relevance Theory, showcasing an effort to reintegrate the study of metaphor into a broader theory of language and to counter recent trends in Metaphor Studies that emphasize autonomy and independence. This is a preview of subscription content, log in via an institution to check access. Access this chapterSubscribe and save.
Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions It is necessary to clarify that in pragmatics there are three different notions—proposition, sentence and utterance. The proposition represents an abstract mental object endowed with truth conditions and encoded in an abstract “Language of Thought”. The sentence constitutes the translation of the proposition into a historical-natural language and finally the utterance is the concrete realization of the sentence in context. ACCORDING to [ 11 ], the relation between an utterance and a speaker’s thought is an interpretive resemblance: a proposition interprets the speaker’s thought. If the thought and the utterance have the same form, the utterance is said to be literal, if this similarity is not present, it is said to be a non-literal utterance, which can take a value on the figurative continuum. Recanati, F. 1995. The alleged priority of literal interpretation. Cognitive Science 19: 207–232. Article Google Scholar Dascal, M. 1987. Defending literal meaning. Cognitive Science 11 (3): 259–281. Borjesson, K. 2014. The semantics-pragmatics controversy . Berlin: De Gruyter. Book Google Scholar La Mantia, F. 2011. Preso alla lettera. Il significato letterale come problema normative. Diritto and Questioni pubbliche 11: 195–231. Google Scholar La Mantia, F. 2015. Tra norme e convenzioni. Ipotesi sul senso letterale. In Convenzioni e convenzionalismo , eds. S. Boscolo, D. Daninos, G. Mancin, and G. Pravato, 29–33. Milano: Mimesis. Lyons, J. (1987) Semantics. In New horizons in linguistics , ed. J. Lyons, vol 2, 152–178. London: Penguin. Recanati, F. 2004. Literal meaning . Cambridge: Cambridge University Press. Gibbs, R. 1994. The poetics of mind: Figurative thought, language and understanding . Cambridge: Cambridge University Press. Lakoff, G., and M. Johnson. 1980. Conceptual metaphor in everyday language. The Journal of Philosophy 77 (8): 453–486. Giora, R. 2003. On our mind. Salience, context and figurative language . Oxford: Oxford UP. Sperber, D., and D. Wilson. 1986. Relevance: Communication and cognition . Oxford: Blackwell. Carston, R. 2002. Thoughts and utterances . Oxford: Blackwell. Bezuidenhot, A. 2001. Metaphor and what is said: A defense of a direct expression view of metaphor. Midwest Studies in Philosophy 22 (1): 156–186. Sperber, D., and D. Wilson. 2008. A deflationary account of metaphor. In The Cambridge handbook of metaphor and thought , ed. R.W. Gibbs, 84–105. Cambridge: Cambridge University Press. Chapter Google Scholar Carston, R. 2010. Metaphor: Ad hoc concepts, literal meaning and mental images. Proceedings of the Aristotelian Society 110: 295–321. Carston, R. 2018. Figurative language, mental imagery and pragmatics. Metaphor and Symbol 33 (3): 1–46. Carston, R., and C. Wearing. 2011. Metaphor, hyperbole and simile: A pragmatic approach. Language and Cognition 3 (2): 283–312. Borg, E. 2004. Minimal semantics . New York: Oxford UP. Cappelan, H., and E. Lepore. 2005. Insensitive semantics: A defense of semantic minimalism and speech act pluralism . Oxford: Blackwell. Grice, H. P. 1975. Logic and conversation. In Syntax and semantics 3: Speech acts , eds. P. Cole, and J. Morgan . New York: Academic Press. Searle, J.R. 1979. Expression and meaning: Studies in the theory of speech acts. Cambridge: Cambridge University Press. Grice, H.P. 1957. Meaning. The Philosophical Review 66: 377–388. Adornetti, I. 2015. Pragmatica del discorso e della conversazione. Una prospettiva cognitiva . Roma-Messina: Corisco. Bianchi, C. 2009. Pragmatica cognitiva. I meccanismi della comunicazione . Roma-Bari: Laterza. Domaneschi, F. 2014. Introduzione alla pragmatica . Roma: Carocci. Domaneschi, F., and V. Bambini. 2020. Pragmatic competence. In Routledge handbook of skill and expertise , eds. C. Pavese, and E. Fridland. Abingdon: Routledge. Allbritton, G., G. McKoon, and R. Gerrig. 1995. Metaphor-based schemas and text comprehension: Making connections through conceptual metaphors. Journal of Experimental Psychology: Learning, Memory and Cognition 21: 612–625. Blasko, D.G., and C.M. Connine. 1993. Effects of familiarity and aptness on metaphor processing. Journal of Experimental Psychology: Learning, Memory and Cognition 19 (2): 295–308. Gibbs, R. 1983. Do people always process the literal meanings of indirect requests? Journal of Experimental Psychology: Learning, Memory and Cognition 9 (3): 524–533. Glucksberg, S., P. Gildea, and H. Booklin. 1982. On understanding non literal speech: Can people ignore metaphors? Journal of Verbal Learning and Behavior 21: 85–98. Ritchie, G. 2004. Metaphors in conversational context: Toward a connectivity theory of metaphor interpretation. Metaphor and Symbol 19 (4): 265–287. Ortony, A., Schallert, D., Reynolds, R., Antos, S. 1978. “Interpreting Metaphors and Idioms: Some Effects of Context on Comprehension” in Journal of Verbal Learning and Verbal Behavior, 17, pp. 465–477. Inhoff, A. W., Susan D. L., Carroll P. J. 1984. “Contextual Effects on Metaphor Comprehension in Reading” in Memory & Cognition 12, 6 pp. 558–67. Gentner, D., and P. Wolff. 1997. Alignment in the processing of metaphor. Journal of Memory and Language 37 (3): 331–335. Gibbs, R. 2002. A new look at literal meaning in understanding what is said and implicated. Journal of Pragmatics 34 (4): 457–486. Giora, R. 2008. Is metaphor unique? In The Cambridge handbook of metaphor and thought , ed. R. Gibbs, 143–160. Cambridge: Cambridge UP. Weiland, H., Bambini, V., Schumacher, P. 2014. “The role of literal meaning in figurative language comprehension: Evidence from masked priming ERP” in Frontiers in Human Neuroscience, 8, 583. Giora, R. 1999. On the priority of salient meanings: Studies of literal and figurative language. Journal of Pragmatics 31: 1601–1618. Giora, R. 1997. Understanding figurative and literal language: The graded salience hypothesis. Cognitive Linguistics 8 (3): 183–206. Sperber, D., and D. Wilson. 2004. Pragmatics. In Oxford handbook of philosophy of language , eds. F. Jackson, and M. Smith. Oxford: Oxford University Press. Tendhal, M. 2006. A hybrid theory of metaphor: Relevance theory and cognitive linguistics . PhD thesis, University of Dortmund. Sperber, D., and D. Wilson. 1995. Relevance: Communication and cognition . Oxford: Blackwell. Pilkington, A. 2000. Poetic effects: A relevance theory perspective . Amsterdam: John Benjamins. Pilkington. 2010. Recanati, F. 2001. Literal/nonliteral. Midwest Studies in Philosophy 22: 459. Carston, R. 2007. Lexical pragmatics, ad hoc concepts and metaphor: A relevance theory perspective. Italian Journal of Linguistics 22 (1): 153–180. Carston, R. 2011. Metaphor and the literal/nonliteral distinction. In Cambridge handbook of pragmatics , eds. K. Allan, and J. Jaszczolt. Cambridge: Cambridge UP. Camp, E. 2003. Saying and seeing-as: The linguistic uses and cognitive effects of metaphor , PhD Thesis, University of Berkeley. Rubio-Fernández, P. 2007. Suppression in metaphor interpretation: Differences between meaning selection and meaning construction. Journal of Semantics 24: 345–371. Rubio Fernandez, P., C. Cummins, and Y. Tian. 2016. Are single and extended metaphors processed differently? A test of two relevance-theoretic accounts. Journal of Pragmatics 94: 15–28. Di Paola, S., Domaneschi, F., Mazzone, M. 2019. Some words are mosquitos in the night. Literalness in metaphor interpretation. XPRAG.it , University of Cagliari. Carapezza, M. 2017. Il gioco linguistico del significato letterale. RIFL, Special Issue: Italian Society of Philosophy of Language . Carapezza, M. 2019. The language game of lost meaning: Using literal meaning as a metalinguistic resource. Intercultural Pragmatics 16 (3): 305–318. Vecchio, Sebastiano. 2016. Prismi agostiniani. Acireale: Bonanno. Tendhal, M., and R.W. Gibbs. 2008. Complementary perspectives on metaphor: Cognitive linguistics and relevance theory. Journal of Pragmatics 40: 1823–1864. Keysarn B., Shen Y., Glucksberg, S., Horton, W. 2000. “Conventional language: how metaphorical is it?” in Journal of Memory and Language, vol. 43, pp. 576–593. Wilson, D. 2009. “Parallels and differences in the treatment of metaphor in Relevance Theory and Cognitive Linguistics” in Studies in Pragmatics, 11 pp. 42–60. Mazzone, M. 2009. «La metafora fra teoria della pertinenza e teoria concettuale» in Bazzanella, C. (ed.), La forza cognitiva della metafora, Paradigmi, vol. XXVII, n. 1, pp. 41–54. Download references Author informationAuthors and affiliations. Department of Humanistic Sciences, University of Palermo, Palermo, Italy Stefana Garello You can also search for this author in PubMed Google Scholar Corresponding authorCorrespondence to Stefana Garello . Rights and permissionsReprints and permissions Copyright information© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG About this chapterGarello, S. (2024). Metaphor in Pragmatics: Literal Meaning, Metaphorical Meaning and Other Dangerous Things. In: The Enigma of Metaphor. UNIPA Springer Series. Springer, Cham. https://doi.org/10.1007/978-3-031-56866-4_4 Download citationDOI : https://doi.org/10.1007/978-3-031-56866-4_4 Published : 31 March 2024 Publisher Name : Springer, Cham Print ISBN : 978-3-031-56865-7 Online ISBN : 978-3-031-56866-4 eBook Packages : Religion and Philosophy Philosophy and Religion (R0) Share this chapterAnyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative
Policies and ethics
Literal meaning: A first step to meaning interpretation
Abstract and FiguresDiscover the world's research
hypothetical Definition of hypothetical
Examples of hypothetical in a SentenceThese examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothetical.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples. Word History1588, in the meaning defined above Dictionary Entries Near hypotheticalhypothesize hypothetical imperative Cite this Entry“Hypothetical.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothetical. Accessed 10 Aug. 2024. Kids DefinitionKids definition of hypothetical, more from merriam-webster on hypothetical. Nglish: Translation of hypothetical for Spanish Speakers Britannica English: Translation of hypothetical for Arabic Speakers Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free! Can you solve 4 words at once?Word of the day. See Definitions and Examples » Get Word of the Day daily email! Popular in Grammar & UsagePlural and possessive names: a guide, commonly misspelled words, how to use em dashes (—), en dashes (–) , and hyphens (-), absent letters that are heard anyway, how to use accents and diacritical marks, popular in wordplay, 8 words for lesser-known musical instruments, it's a scorcher words for the summer heat, 7 shakespearean insults to make life more interesting, plant names that sound like insults, 10 words from taylor swift songs (merriam's version), games & quizzes. Look up a word, learn it forever.Hypothetical, /ˌˈhaɪpəˌˈθɛdəkəl/, /haɪpəˈθɛtikəl/. Other forms: hypotheticals Everyone who has ever taken a science class knows the word "hypothesis," which means an idea, or a guess, that you are going to test through an experiment. A hypothetical is related to that. It means something based on an informed guess. Hypotheticals are fun. How would you do in a hypothetical arm-wrestling competition against your Grandma? There are people in the Pentagon whose jobs are to consider all kinds of hypotheticals––what if Luxembourg armed itself with nuclear weapons? What if France developed the ability to pelt Switzerland with cannons firing cheeses?
Vocabulary lists containing hypotheticalPersuade yourself to study this list of words related to argumentative writing. You'll learn all about making claims, supporting arguments with evidence, and maintaining an objective tone. It's no fallacy that reviewing these words will improve your credibility as a writer. Take our word for it, the English language has many ways to describe things that might, or might not, be. For more synonym lists, explore our Say What You Mean resources. How can you perform well on the reading section of the SAT if you don’t fully understand the language being used in the directions and in the questions? Learn this list of 25 words that are based on our analysis of the words likely to appear in question stems, answer options, and test directions. Following our Roadmap to the SAT ? Head back to see what else you should be learning this week. Sign up now (it’s free!)Whether you’re a teacher or a learner, vocabulary.com can put you or your class on the path to systematic vocabulary improvement..
literal interpretationMeanings of literal and interpretation. Your browser doesn't support HTML5 audio (Definition of literal and interpretation from the Cambridge English Dictionary © Cambridge University Press)
Word of the Day an area of coral, the top of which can sometimes be seen just above the sea Robbing, looting, and embezzling: talking about stealing Learn more with +Plus
{{message}} There was a problem sending your report.
Advertisement Supported by Class of 1999 Here’s Why ‘The Matrix’ Is More Relevant Than EverOne scene reflects the themes — A.I., fake news, transgender lives and Gen X — that make the film a classic.
By Alissa Wilkinson Neo, the hero of “ The Matrix ,” is sure he lives in 1999. He has a green-hued cathode-ray-tube computer screen and a dot-matrix printer. His city has working phone booths. But he’s wrong: He lives in the future (2199, to be exact). Neo’s world is a simulation — a fake-out version of the late 20th century, created by 21st-century artificial intelligences to enslave humanity. When we first saw Neo, though, it really was 1999. The idea of A.I. feeding on human brains and bodies seemed like a thought experiment. But the movie’s warnings about A.I. — and everything else — have sharpened over time, which explains why it’s been harnessed by all kinds of people in the years since: philosophers, pastors, techno-boosters and techno-doomers, the alt-right. Judged solely on cultural relevance, “The Matrix” might be the most consequential release of 1999. The genius of the movie — what makes it incredibly rewatchable 25 years later — is that the writer-directors Lilly and Lana Wachowski didn’t try to control the meaning. Instead, they seeded symbolism throughout. Look with me at how one introductory scene manages to draw together many thematic threads, explaining why in today’s world of pervasive internet, A.I., fake news and extremism, “The Matrix” feels more relevant than ever. We are having trouble retrieving the article content. Please enable JavaScript in your browser settings. Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times. Thank you for your patience while we verify access. Already a subscriber? Log in . Want all of The Times? Subscribe . |
IMAGES
COMMENTS
hypothesis: [noun] an assumption or concession made for the sake of argument. an interpretation of a practical situation or condition taken as the ground for action.
Hypothesis definition: a proposition, or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation (working hypothesis ) or accepted as highly probable in the light of established facts.. See examples of HYPOTHESIS used in a sentence.
HYPOTHESIS definition: 1. an idea or explanation for something that is based on known facts but has not yet been proved…. Learn more.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
In its ancient usage, hypothesis referred to a summary of the plot of a classical drama.The English word hypothesis comes from the ancient Greek word ὑπόθεσις hypothesis whose literal or etymological sense is "putting or placing under" and hence in extended use has many other meanings including "supposition".. In Plato's Meno (86e-87b), Socrates dissects virtue with a method used by ...
HYPOTHESIS meaning: 1. an idea or explanation for something that is based on known facts but has not yet been proved…. Learn more.
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
The hypothesis predicts that children will perform better on task A than on task B. The results confirmed his hypothesis on the use of modal verbs. These observations appear to support our working hypothesis. a speculative hypothesis concerning the nature of matter; an interesting hypothesis about the development of language
Full Definition of HYPOTHESIS. 1. a: an assumption or concession made for the sake of argument . b: an interpretation of a practical situation or condition taken as the ground for action . 2: a tentative assumption made in order to draw out and test its logical or empirical consequences . 3
definition 2: a proposition assumed to be true for the purposes of a particular argument; premise. Let's start out with the hypothesis that these kinds of tests are fair. synonyms: premise, proposition, supposition. similar words: assumption, axiom, postulate, presumption. definition 3: in logic, the first member of a conditional proposition.
literal meaning hypothesis does not fit into an adequately constrained, psy- chologically valid model of human language behavior. In the first section, I consider some of the recent arguments in philos- ophy and linguistics over whether literal meaning is context-free meaning and on the importance of literal meaning in understanding nonliteral ...
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question. A hypothesis is not just a guess. It should be based on ...
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
This paper evaluates the psychological status of literal meaning. Most linguistic and philosophical theories assume that sentences have well-specified literal meanings which represent the meaning of a sentence independent of context. Recent debate on this issue has centered on whether literal meaning can be equated with context-free meaning, or ...
The Graded Salience Hypothesis breaks down the equation of literal meaning and primary meaning, arguing that it is not the literal meaning that is activated first but the salient meaning, i.e., the meaning encoded in the mental lexicon, whose degree of salience depends on its conventionality, familiarity and frequency: the most "popular" or ...
Traditionally the literal meaning is a major component in the process of interpretation of meaning (Abuarrah, 2018). Thus, the basic capital that must be possessed by the speech participants to be ...
hypothetical: [adjective] involving or being based on a suggested idea or theory : being or involving a hypothesis : conjectural.
1. Introduction: the literal/non-literal distinction in the firing line. We are generally good at distinguishing between literal and non-literal meaning. In 'The brightest object visible from earth is the sun', 'the sun' is used literally; in 'Juliet is the sun' it is not. Recanati ( 2004, 68) submits that we have a folk-theoretic ...
hypothetical: 1 n a hypothetical possibility, circumstance, statement, proposal, situation, etc. "consider the following, just as a hypothetical " Type of: hypothesis , possibility , theory a tentative insight into the natural world; a concept that is not yet verified but that if true would explain certain facts or phenomena adj based ...
Comprehension of idioms is the act of processing and understanding idioms.Idioms are a common type of figure of speech.Based on common linguistic definitions, an idiom is a combination of words that contains a meaning that cannot be understood based on the literal definition of the individual words. An example of an idiom is hit the sack, which means to go to bed.
Examples of LITERAL INTERPRETATION in a sentence, how to use it. 16 examples: This would rescue the literal interpretation but seriously reduce the scope of the hypothesis…
The framework interpretation (also known as the literary framework view, framework theory, or framework hypothesis) is a description of the structure of the first chapter of the Book of Genesis (more precisely, Gen 1:1-2:4a), the Genesis creation narrative. Biblical scholars and theologians present the structure as evidence that Gen. 1 presents a symbolic, rather than literal, presentation ...
What the jury found Donald Trump did to E. Jean Carroll was in fact rape, as commonly understood, even if it didn't fit New York law's narrow definition, says Judge Lewis A. Kaplan.
One scene reflects the themes — A.I., fake news, transgender lives and Gen X — that make the film a classic. By Alissa Wilkinson Neo, the hero of "The Matrix," is sure he lives in 1999. He ...
Former president Donald Trump described his domestic opponents as "vermin" that posed a greater threat to the United States than countries such as Russia, China or North Korea, drawing rebuke ...