Assignment Instructions (Research Methods) The answers to these questions can be

Assignment Instructions (Research Methods)
The answers to these questions can be found in the videos and on the terminology pages in this Canvas module.
What makes quantitative research quantitative?
What makes qualitative research qualitative?
What makes mixed method research mixed?
What is a random sample?
What is a confounding variable?
Step-by-Step Instructions:
Copy and paste or type the questions listed above into a Word or Google document, and minimize the document.
Watch the YouTube videos and my video, and take notes related to the questions.
Review the terminology pages.
Review your notes and answer each question in your own words.
Be concise, two complete sentences maximum per question.
DO NOT quote the video or me.
DO NOT use any external resources other than those included in this module.
If you are using Word, remember to save your document as you continue to watch the videos.
When you have viewed all the videos and answered all the questions, submit the document on the next page.
Here are some videos that will present information about research methods in a variety of ways. Some are more or less dry than others. Don’t hesitate to do some searches on your own and let me know if you find any that helped you more than these.
There are seven videos that range from three to nine minutes in length. Read these assignment questions and write them into your notes to help you keep them in mind as you watch the videos. Remember to jot down the answers as they come up in the YouTube videos or in my video, and when you read them on the terminology pages that follow.
What makes quantitative research quantitative?
What makes qualitative research qualitative?
What makes mixed method research mixed?
What is a random sample?
What is a confounding variable?
HERE ARE THE VIDEO LINKS:
Quantitative versus Qualitative (Greg Martin of Global Health; 4-mins):

Epidemiology Cohort & Case Control Study (Greg Martin of Global Health; 4-mins):

Randomized Trials and Confounds (Greg Martin of Global Health; 4-mins):

Quantitative versus Qualitative (9-mins):

Epidemiology Cohort Study (5.5-mins):

Cohort Effects Lecture (3-mins):

Qualitative Interview Research (5-mins):

The 9-minute video link below was prepared to replace the entire chapter. It is short but sweet. Combined this video and the external videos on previous pages will provide a framework for you to consider when you are reading and critiquing articles, until you actually take a statistics or research method course:

Basic Research Terms (Variables & Samples)
Before you watch the videos on the next module page, read the terms and meanings below aloud. If your major is in the Behavioral and Social Sciences, this soft introduction to research terms will help prepare you for future coursework.
Terms Related to Variables
Study Variables:
Independent Variable—the variable a research manipulates
Dependent Variable—the variable a research observes or measures
Variables Types:
Categorical variable—a variable with two or more categories identified by a name/label that do not have a rank or sequence association
Continuous variable—a variable with an infinite numerical possibilities
Discrete variable—a variable with a fixed number of possibilities
Ordinal variable—a variable with two or more categories that have a ranked order of some kind (see categorical vs ordinal)
Comparison of Variable Types:
Continuous vs Discrete variables—a variable with an infinite numerical possibilities as opposed to a variable with a fixed number of possibilities
Categorical vs Ordinal variables—a variable with two or more categories that do not have a rank or sequence association as opposed to a variable with two or more categories that have a ranked order of some kind
Variable-Related Problems:
Artifacts of design—the results when research design does not take into account factors or constructs that may impact the performance of one group differently than another (e.g. anxiety)
Confounding variable—an extraneous (unforeseen/unexpected) factor in a study that can influence results or the analysis of the results (e.g. an artifact of design)
Other Basic Terms
Terms Related to Individuals:
Anonymity—guarantee that participant names will not associated with their responses
Confidential—only the research team will have access to the information provided by the participants (Whitbourne, 2017)
Participant—individuals who are observed during a study or who submit responses during a study at a predetermined time and/or place (e.g. office, lab, or virtually)
Respondent—individuals who submit responses to a research instrument at their own convenience (e.g. mail or online surveys)
Terms for Study Tool:
Instrument—the survey, assessment, or other device that is used to collect the quantitative or qualitative data to be analyzed
Terms Related to Groups:
Population—the entire population of a designated areas, all ages, genders, etc.
Sample—participants in research who are representative of a population or a particular sub-population
Sub-population—a subset or group within a population, e.g. women, an age group, etc.
Survey response rate—the percentage of voluntary responses received to a survey or other instrument (number of responses divided by the number distributed)
Terms for Sample Types:
Convenience sample—selection of research participants based access and availability, typically through an established group (organization, institution, geographic area, etc.) that meets a particular criteria
Random sample—selection of research participants based on a statistical method that ensured all members on the population or sub-population made an equal chance of being included
Voluntary response sample—respondents to a publicly announced question that is always assumed to be biased because only people with an opinion respond
Types of Research Design
Cross-Sectional—a study that assess, tests, or measures variables in people from multiple ages/cohorts and compares the results of one age/cohort to another, e.g. people in their 20s might be compared to the people in their 30s. These studies only allow between-subject comparisons, meaning the results can only be compared between two different individuals of the same or different ages/cohorts.
Experimental Studies—laboratory studies in which researchers have control of the environment and all the variables that can influence the results of the experiment, including control over the independent variable being manipulated, which is expected to have an influence on the dependent variable. Age cannot be manipulated by researchers so studies about aging are NEVER true experiments.
Longitudinal—a study that follows the same group of people over time and assesses, tests, or measures variables at multiple intervals, e.g. at age 20, 30, 40, etc. These studies allow within-subject comparisons, meaning the results of a particular individual are compared to their own results at an earlier age, as well as the between subject differences described in cross-sectional.
Mixed Method—research that collects both quantitative and qualitative data and utilizes at least one qualitative design methodology in addition to quantitative methodologies.
Qualitative data—short and long answer responses to questions that provide an opportunity for interactive dialog, e.g. focus groups and interviews. The responses may be used in their original form or coded numerically and analyzed statistically. Raw qualitative data requires interpretation by a researcher, which adds a subjective element to the study. Coding procedures are used to minimize the subjective influence.
Qualitative design—research methodologies that collect qualitative data for evaluation or statistical analysis and provides researchers an opportunity for follow-up questions to clarify the initial response.
Quantitative data—measurements, rankings, numerical responses, or other responses that are readily converted to numbers (raw data not yet analyzed).
Quantitative design—research that collects quantifiable data for statistical analysis (e.g. surveys and scores)
Quasi-Experimental—study design that allows researchers to investigate variables that cannot be manipulated and/or there is no baseline/control available for comparison, for example an innate characteristic like age during a cross-sectional study.
Descriptive Statistics
Descriptive statistics are used in the analysis and reporting of both qualitative and quantitative research.
Measurements of Central Tendencies—three calculated/counted numbers that indicate some numerical center of a set or subset of numbers:
Mean—the average of a set or subset of numbers
Mode—the response with the highest number of repetitions in a set or subset of numbers
Median—the value of the response in the middle of a set or subset of numbers when listed in low-to-high order
Measurement of Dispersion—three calculated numbers that indicate the pattern of the responses when placed on a graph with a horizontal and vertical axis:
Standard deviation—the difference between a number in a set and the average of the numbers in the set; it is always expressed as a positive number and it is the most basic measure of dispersion
Variance—one number that conveys the overall dispersion of a set of numbers calculated by averaging the standard deviations of all the numbers in a set
Interquartile range—the two quarters of a numerical graph of positive and negative numbers that have zero as the high or low boundary
Correlational Design Terms
Correlation—is a method of statistical analysis used in quantitative and mixed-method research.
Correlation vs Causation—just because two things are present at the same time does not mean that one was the cause of the other
Correlation vs Relationship (Prediction)—statistical relationships are also based on correlations, but the analysis is more complex and provides a means to predict the occurrence in similar situations
Effect size—a simple way to determine if a difference between two groups is small, medium, or large
Negative Correlation—if one variable increases the other variable decreases or if one decreases the other increases
Positive Correlation—if one variable increase the other variable increases or if one decreases the decreases
Probability—a calculation made to determine how likely something is that results in 0 to 1, with 1 being 100% for sure
Statistical Significance—the results of a calculation done to determine if an occurrence was likely to have a cause or be due to change alone

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