Demonstrate awareness of research statistics and researchdesign resources for professional development
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DMS 1750 Case Studies CritiquesWeek 2: Research Statistics in Sonography &Research Design in Sonography
Learning Objective¥Students will demonstrate awareness of research statistics and researchdesign resources for professional development
Purpose of Learning Research Statistics andResearch Design¥Knowledge of statistics and research design is applied to help usbecome better job applicants, sonographers, critical thinkers, anddecision-makers.¥Sonographers are inundated with information, so itÕs important to beadept consumers of this knowledge.¥This means asking questions and considering the validity of claimsbefore we accept them as truth.¥Statistics and research methods can help us learn how to interpret andaddress data and information that we encounter.
Research Design
What Research Is (and Is Not)¥Research is used to inform and potentially improve professionalpractice¥Research helps answer professional questions¥Research guides decision-making¥Research contributes to the professional body of knowledge¥Research informs future innovationsButÉ¥Research does not tell what is true or correct or ÒrightÓ¥Research does not prove a point of viewÑit only gives some evidence
Getting Started: Framing a Research Design¥Research topic: a broad issue or area of study that is important toinvestigate¥Select topics from:¥Professional experience¥Industry trends¥Prior research studies¥Existing theories¥Communications within professional networks¥Collaborations with other professionals¥Research questions: to focus and establish boundaries for the researchstudy
Create a Hypothesis¥A proposition to be tested; also called the alternative hypothesis¥A statement of the relationship between two variables¥Links the research questions with the research design¥A testable or measurable statement¥Null hypothesis: a statement related to the alternative hypothesis,which states that nothing is happening, there is no relationship, orthere is no effect¥For example, if the alternative hypothesis is that an increase in study time willcorrelate with an increase in studentsÕ test scores, the null hypothesis would bethat there is no effect on test scores when students increase study time.¥In hypothesis testing, researchers either reject or do not reject the nullhypothesis (they do not ÒproveÓ the alternate hypothesis)
Choose a Research Design¥Framework for answering the research questions and testing thehypothesis (if applicable)¥Includes:¥Approach to the research study¥Type of research design¥Selection of study participants¥Research Methods¥Data collection methods¥Data collection procedures¥Data analysis strategies & statistics
Defining the Research Approach¥Inductive research: start with data collection from observations ortests, identify patterns in the data, suggest a theory¥Deductive research: hypothesis testing; start with theory, createhypothesis, collect/analyze data from observation or tests, confirm orreject the null hypothesis¥Abductive research: designed to explain incomplete observations,surprising facts, or puzzles.
Collect Research Data¥The research questions indicate what types of data must be collected toanswer the questions¥Types of data:¥Qualitative: words, descriptions, concepts, ideas, experiences, meanings¥Quantitative: numerical data¥Primary data: original information collected to answer the research questions (e.g.from surveys or interviews)¥Secondary data: information collected by other researchers and repurposed for theresearch questions (e.g. census data, labor statistics)¥Descriptive data: data collected through observations of existing actions that theparticipants are taking; no research intervention¥Experimental data: data collected after systematically intervening in theparticipantsÕ behavior or treatment
Data Analysis: Apply Statistics
Research Statistics Overview¥There are two types of research statistics:¥Descriptive statistics: statistics that summarize a data set within a study¥Inferential statistics: statistics that help a researcher draw conclusions orestablish probabilities about the outcomes of a study
Descriptive Statistics¥Statistics that describe a data set may include:¥Number of study participants (n)¥Mean (average)¥Median¥Mode¥Range¥Quartiles¥Variance¥Standard deviation¥Visuals such as distributions, histograms, and stem-and-leaf diagrams¥For example, when case studies report on research done with humansubjects, there is often a table describing the population: the overall size ofthe group, sizes of the studyÕs subgroups, and demographic characteristics
Examples of Descriptive Statistics in Action¥Median home price¥Mean: average high and low temperature for a given day¥Median income and Range of income for sonographers as a job category¥Mode: most frequently purchased category of a product in the last month ina retail business
Advanced Descriptive Statistics¥Within descriptive statistics, itÕs possible to analyze data from morethan one variable through bivariate and multivariate analysis¥Bivariate statistics are a type of descriptive statistics that help a researcherdescribe data from 2 variables and analyze a potential relationship betweentwo variables¥Correlation statistics (regression analysis, PearsonÕs r, SpearmanÕs rho)¥Covariance¥z-scores¥Scatterplots¥Multivariate analysis is a type of descriptive statistics to help a researchdescribe data from more than 2 variables, through tools like:¥Cross-tabulations¥Contingency tables
Examples of Bivariate and Multivariate DescriptiveStatistics¥Bivariate: correlation between height and weight in children¥Multivariate: cross-tabulations in a political poll result
Inferential Statistics¥Researchers can get data from a sample and draw conclusions or makepredictions about the population from which the sample is drawn¥Inferential statistics are used to:¥Make estimates about a population¥Test hypotheses about populations
Sampling Error & Confidence Intervals¥Because the sample being studied is smaller than the population itrepresents, the data collected from the sample will inherently have asampling error.¥Sampling error is the difference between the true value for thepopulation and the value that the researcher identifies in data from thesample¥Confidence intervals describe the range of values that are most likelytrue for the population
Examples of Inferential Statistics in Action¥A 20 subject sample size participates in a study of a new bloodpressure drug. Inferential stats (t-test) helps the researcher makeinferences about the results to the population and in comparison toexisting treatments.¥20 patients are randomly assigned as 4 sample groups to use 4 differentblood pressure medications. Blood pressure is measured before andafter the patient started using their assigned medication to find themean blood pressure reduction for each medication. Inferential stats(ANOVA) are used to draw inferences about the effects of the drugson the population and in comparison to each other.¥Political polling research firms poll a small sample of people aboutpolitical issues and candidates. Using inferential statistics, researcherscan determine the preferences of the whole population based on the
Key Vocabulary for Statistics and Research Methods¥Population¥Sample¥Statistics¥Variable¥Analysis¥Data set¥Correlation¥Research question¥Hypothesis¥Methodology
Resources to Learn Statistics and Research Methods¥Khan Academy: Statistics and Probability¥Corporate Finance Institute: Inferential Statistics¥Khan Academy: Introduction to Experiment Design¥Khan Academy: Types of Statistical Studies¥Khan Academy: Observational Studies and Experiments¥Khan Academy: Identify the Population and Sample¥Khan Academy: Correlation and Causation¥Khan Academy: Data Collection and Conclusions¥University of Norther Iowa: Research Methods
www.cahe.edu
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