We have four categories that can be applied.
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Type: Calculation Case Conceptual Creating graphs Data manipulation
Interpreting
graph Interpreting output Performing analysis Test choice
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Program: SPSS JASP R STATA Excel Calculator Jamovi
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Language: English
Dutch
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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 Literacy
Type 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