Tags
To add tags to your r-exams markdown file add exextra[]:` to theMeta-information` of your markdown file.
We have four categories that can be applied.
Click to copy
- Type: Calculation Case Conceptual Creating graphs Data manipulation Interpreting graph Interpreting output Performing analysis Test choice
- Program: SPSS JASP R STATA Excel Calculator Jamovi
- Language: English Dutch
- Level: Statistical Literacy Statistical Reasoning Statistical Thinking
You can use more than one tag per category.
Meta-information
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exextra[Type]: Calculation, Data manipulation
exextra[Program]: SPSS
exextra[Language]: English
exextra[Level]: Statistical LiteracyType descriptions
Calculation
A question containing simple (hand/calculator) calculations
Example:: M1 = 10, M2 = 24, s_pooled = 1.23. What is the value of Cohen’s d?
Case
Questions that belong to a longer description of a research study. Oftentimes multiple questions are asked about the same case/description.
Example:: NA
Conceptual
Basic question asking about simple facts.
Example:: Which of the following properties is not a condition for establishing a causal relationship? a. Alternative explanations for the relationship between cause and effect can be excluded. b. The data shall be collected with a randomized experiment. c. There must be a relationship between the cause and the effect. d. The cause must precede the effect in time.
Creating graphs
The student is asked to create a graph using data supplied with the question (either by hand or using a program).
Example:: NA
Data manipulation
The student is asked to combine data, screen data, create new variables in a dataset, or calculate descriptive statistics using the data supplied with the question.
Example:: NA
Interpreting graph
The graph is supplied with the question. The student is asked to look at the graph and describe what is going on, draw conclusions based on the graph, etc.
Example:: NA
Interpreting output
The output is either supplied with the question or the student has run an analysis to create the output (combine with “Performing analysis”). The student is asked to look at the output and report results/draw conclusions based on it.
Example:: NA
Performing analysis
The student is asked to conduct an analysis using a statistical program (combine with program type).
Example:: NA
Test choice
The student is presented with a description of research/study and is aksed to choose which hypotheses test should be used.
Example:: A researcher randomly assigns 100 students to a control group and an experimental group. All students take a math test. Half of the students in each group take the test on paper and half of the students take the test on a computer. The researcher determines the number of correctly answered questions for each student. With which technique should the researcher analyze his data? a. ANOVA b. Cross-table analysis c. Two-way ANOVA d. ANCOVA
Level descriptions
Statistical Literacy (Bloom: Knowing)
Identify, Describe, Translate, Interpret, Read, Compute
Example: Understanding and using the basic language and tools of statistics: knowing what basic statistical terms mean, understanding the use of simple statistical symbols, and recognizing and being able to interpret different representations of data
Statistical Reasoning (Bloom: Comprehending)
Explain why, Explain how
Example: The way people reason with statistical ideas and make sense of statistical information. Statistical reasoning may involve connecting one concept to another (e.g., center and spread) or combining ideas about data and chance. Statistical reasoning involves understanding concepts at a deeper level than literacy, such as understanding why a sampling distribution becomes more normal as the sample size increases. Reasoning also means understanding and being able to explain statistical processes and being able to interpret particular statistical results (e.g., why a mean is much larger or smaller than a median, given the presence of an outlier).
Statistical Thinking (Bloom: Application, Analysis, Synthesis, and Evaluation)
Apply, Critique, Evaluate, Generalize
Example: Involves a higher order of thinking than does statistical reasoning. Statistical thinking has been described as the way professional statisticians think. It includes knowing how and why to use a particular method, measure, design or statistical model; deep understanding of the theories underlying statistical processes and methods; as well as understanding the constraints and limitations of statistics and statistical inference. Statistical thinking is also about understanding how statistical models are used to represent random phenomena, understanding how data are produced to estimate probabilities, recognizing how, when, and why to use inferential tools in solving a statistical problem, and being able to understand and utilize the context of a problem to plan and evaluate investigations and to draw conclusions