Whats the difference between exploratory and explanatory research? It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined. 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. No Is bird population numerical or categorical? a. What are the pros and cons of naturalistic observation? Categorical data always belong to the nominal type. Data cleaning is necessary for valid and appropriate analyses. Why are convergent and discriminant validity often evaluated together? Whats the difference between a statistic and a parameter? 5.0 7.5 10.0 12.5 15.0 60 65 70 75 80 Height Scatterplot of . finishing places in a race), classifications (e.g. A correlation reflects the strength and/or direction of the association between two or more variables. Its essential to know which is the cause the independent variable and which is the effect the dependent variable. The temperature in a room. Categorical data requires larger samples which are typically more expensive to gather. You need to have face validity, content validity, and criterion validity to achieve construct validity. When would it be appropriate to use a snowball sampling technique? Shoe size is a discrete variable since it takes on distinct values such as {5, 5.5, 6, 6.5, etc.}. The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. Construct validity is often considered the overarching type of measurement validity, because it covers all of the other types. is shoe size categorical or quantitative? Select the correct answer below: qualitative data discrete quantitative data continuous quantitative data none of the above. You can't really perform basic math on categor. Some examples of quantitative data are your height, your shoe size, and the length of your fingernails. It is used in many different contexts by academics, governments, businesses, and other organizations. Quantitative Variables - Variables whose values result from counting or measuring something. Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down. The type of data determines what statistical tests you should use to analyze your data. How do you plot explanatory and response variables on a graph? The validity of your experiment depends on your experimental design. Blinding is important to reduce research bias (e.g., observer bias, demand characteristics) and ensure a studys internal validity. Categorical variables represent groups, like color or zip codes. You focus on finding and resolving data points that dont agree or fit with the rest of your dataset. Continuous random variables have numeric . In randomization, 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. Structured interviews are best used when: More flexible interview options include semi-structured interviews, unstructured interviews, and focus groups. They are often quantitative in nature. If your explanatory variable is categorical, use a bar graph. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. Question: Patrick is collecting data on shoe size. Its the same technology used by dozens of other popular citation tools, including Mendeley and Zotero. This type of validity is concerned with whether a measure seems relevant and appropriate for what its assessing only on the surface. 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. Whats the difference between correlation and causation? Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population. Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. Together, they help you evaluate whether a test measures the concept it was designed to measure. What are the disadvantages of a cross-sectional study? An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. What is the difference between criterion validity and construct validity? What does controlling for a variable mean? Naturalistic observation is a valuable tool because of its flexibility, external validity, and suitability for topics that cant be studied in a lab setting. What are the pros and cons of a between-subjects design? Then, youll often standardize and accept or remove data to make your dataset consistent and valid. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. To implement random assignment, assign a unique number to every member of your studys sample. Peer assessment is often used in the classroom as a pedagogical tool. The data fall into categories, but the numbers placed on the categories have meaning. To find the slope of the line, youll need to perform a regression analysis. Expert Answer 100% (2 ratings) Transcribed image text: Classify the data as qualitative or quantitative. When youre collecting data from a large sample, the errors in different directions will cancel each other out. Answer (1 of 6): Temperature is a quantitative variable; it represents an amount of something, like height or age. What are the assumptions of the Pearson correlation coefficient? In general, the peer review process follows the following steps: Exploratory research is often used when the issue youre studying is new or when the data collection process is challenging for some reason. In stratified sampling, researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment). The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures. Experimental design means planning a set of procedures to investigate a relationship between variables. IQ score, shoe size, ordinal examples. Whats the difference between random and systematic error? The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes. The downsides of naturalistic observation include its lack of scientific control, ethical considerations, and potential for bias from observers and subjects. How is inductive reasoning used in research? Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions. After data collection, you can use data standardization and data transformation to clean your data. You dont collect new data yourself. Oversampling can be used to correct undercoverage bias. You have prior interview experience. Whats the difference between clean and dirty data? You can only guarantee anonymity by not collecting any personally identifying informationfor example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos. Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. categorical. Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. What is the difference between quota sampling and convenience sampling? You can use this design if you think your qualitative data will explain and contextualize your quantitative findings. Continuous variables are numeric variables that have an infinite number of values between any two values. Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group. Quantitative data is collected and analyzed first, followed by qualitative data. At a Glance - Qualitative v. Quantitative Data. There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction and attrition. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who werent involved in the research process. Construct validity is about how well a test measures the concept it was designed to evaluate. The Pearson product-moment correlation coefficient (Pearsons r) is commonly used to assess a linear relationship between two quantitative variables. Quantitative variables are in numerical form and can be measured. How do explanatory variables differ from independent variables? lex4123. That way, you can isolate the control variables effects from the relationship between the variables of interest. Sometimes, it is difficult to distinguish between categorical and quantitative data. height, weight, or age). Using stratified sampling will allow you to obtain more precise (with lower variance) statistical estimates of whatever you are trying to measure. The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings). Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The higher the content validity, the more accurate the measurement of the construct. The square feet of an apartment. Dirty data contain inconsistencies or errors, but cleaning your data helps you minimize or resolve these. Now, a quantitative type of variable are those variables that can be measured and are numeric like Height, size, weight etc. These are the assumptions your data must meet if you want to use Pearsons r: Quantitative research designs can be divided into two main categories: Qualitative research designs tend to be more flexible. Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample. Discriminant validity indicates whether two tests that should, If the research focuses on a sensitive topic (e.g., extramarital affairs), Outcome variables (they represent the outcome you want to measure), Left-hand-side variables (they appear on the left-hand side of a regression equation), Predictor variables (they can be used to predict the value of a dependent variable), Right-hand-side variables (they appear on the right-hand side of a, Impossible to answer with yes or no (questions that start with why or how are often best), Unambiguous, getting straight to the point while still stimulating discussion. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning youll need to do. In your research design, its important to identify potential confounding variables and plan how you will reduce their impact. There are three types of cluster sampling: single-stage, double-stage and multi-stage clustering. Yes, it is possible to have numeric variables that do not count or measure anything, and as a result, are categorical/qualitative (example: zip code) Is shoe size numerical or categorical? Yes. Operationalization means turning abstract conceptual ideas into measurable observations. Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem. In this process, you review, analyze, detect, modify, or remove dirty data to make your dataset clean. Data cleaning is also called data cleansing or data scrubbing. of each question, analyzing whether each one covers the aspects that the test was designed to cover. Take your time formulating strong questions, paying special attention to phrasing. Categorical Can the range be used to describe both categorical and numerical data? For strong internal validity, its usually best to include a control group if possible. Whats the difference between concepts, variables, and indicators? Quantitative (Numerical) vs Qualitative (Categorical) There are other ways of classifying variables that are common in . A regression analysis that supports your expectations strengthens your claim of construct validity. Peer review enhances the credibility of the published manuscript. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.