Q1-T,2/4 Flashcards

(34 cards)

1
Q

What are descriptive statistics?

A

They tell you about your population (age, gender, etc.)

Descriptive statistics do not include statistical tests and are performed first to help decide what statistics to perform later.

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2
Q

What do central tendency measures include?

A
  • Mean
  • Median
  • Mode

Central tendency measures summarize a data set with a single value representing the center of the data distribution.

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3
Q

What are 4 measures of dispersion?

A
  • Variance
  • Standard deviation
  • RMS error
  • Inter-quartile range

Measures of dispersion describe the spread or variability of a data set.

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4
Q

What are measures of data range?

A
  • Min
  • Max
  • Outliers
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5
Q

What is the purpose of power analysis?

A

To help design the experiment before it is conducted

Power analysis assesses the sample size required to detect an effect of a given size with a certain degree of confidence.

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6
Q

What does correlation refer to?

A

The relationship between variables

Correlation measures the degree to which two variables move in relation to each other.

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7
Q

What does regression analyze?

A

The separation between variables

Regression is used to understand how the typical value of the dependent variable changes when any one of the independent variables is varied.

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8
Q

What is ANCOVA?

A

Analysis of covariance

ANCOVA combines ANOVA and regression, allowing for the comparison of one or more means while controlling for other variables.

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9
Q

What are non-parametric statistics?

A

They have fewer or no assumptions about the underlying shape of the data

Non-parametric statistics are useful when data do not meet the assumptions necessary for parametric tests.

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10
Q

What are parametric statistics?

A

They are usually more statistically descriptive and hence more desirable

Parametric statistics assume that the data follows a certain distribution, typically a normal distribution.

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11
Q

What are carryover effects?

A

When the effects of receiving one treatment affect participants in subsequent conditions

E.g., participants may get better at performing a task across trials

This can lead to confounding results in experimental trials.

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12
Q

What is the observer (Hawthorne) effect?

A

When subjects modify behavior in response to their knowledge of being studied

This effect can introduce bias in research findings.

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13
Q

What is desired variability in an experiment?

A

Experiment variability due to the conditions of interest; planned, systematic variability

Desired variability is typically the effect the researcher aims to measure.

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14
Q

What is undesired variability?

A

Experiment variability in the measurement process or in the experimental material and/or process

E.g., poor choice of subjects

Unplanned variability can lead to confounding variables affecting the results.

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15
Q

What are the three fundamental ethical principles identified in the Belmont report?

A
  • Respect for persons
  • Beneficence
  • Justice

These principles guide ethical considerations in human subject research.

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16
Q

What does ‘respect for persons’ entail?

A

Respect each person’s autonomy and their ability to choose to participate in the study

It also involves protecting individuals with limited autonomy.

17
Q

What is the principle of beneficence?

A

Maximizing benefit and minimizing harm in research

This principle emphasizes the ethical obligation to protect the welfare of research participants.

18
Q

What does the principle of justice involve?

A

Distributing risks and benefits equally among participants and the public

Justice ensures fairness in the recruitment of participants and the distribution of benefits from research.

19
Q

Why is the term ‘participant’ preferred over ‘subject’?

A

Participant gives a sense of consensual participation

The term ‘subject’ is often viewed as dehumanizing in the context of research.

20
Q

What are common reasons for conducting human experiments?

A
  • To test a hypothesis
  • To validate models
  • Concept validation
  • Improve product design

Human experiments are essential for understanding complex human behaviors and responses.

21
Q

When should human experiments be avoided?

A
  • Existing data available
  • Critical consequences to subjects
  • Study is not well designed

Ethical considerations must guide the decision to conduct human experiments.

22
Q

What is the experimental design process?

A
  • Research question (hypothesis)
  • Design experiment
  • Collect data
  • Analyze data
  • Draw conclusions
  • Repeat (or give up)

This process is iterative and essential for conducting rigorous research.

23
Q

What is an independent variable?

A

What you manipulate or observe; also called explanatory

Independent variables are central to experimental design as they are the presumed cause of changes in the dependent variable.

24
Q

What is a dependent variable?

A

What you measure; also called outcome

Dependent variables reflect the effects of the independent variable in an experiment.

25
What are the types of dependent variables?
* Performance based (e.g., reaction time) * Subjective (e.g., surveys) * Psychophysiological responses (e.g., blood pressure) * Meta-metrics (e.g., workload) ## Footnote Understanding the types of dependent variables is crucial for selecting appropriate measurement methods.
26
What are the characteristics of quantitative data?
* Numerical * Can be ranked/ordered ## Footnote Quantitative data is essential for statistical analysis and hypothesis testing.
27
What is discrete quantitative data?
Assumes values that can be counted (e.g., number of children in a family) ## Footnote Discrete data consists of distinct, separate values.
28
What is continuous quantitative data?
Can assume all values between two specific values (e.g., temperature) ## Footnote Continuous data can take on an infinite number of values within a given range.
29
What is qualitative data?
Categorical (e.g., gender, city, prior experience) ## Footnote Qualitative data provides descriptive information that can help contextualize quantitative findings.
30
What is nominal data?
Qualitative classification only; no order or ranking (e.g., gender) ## Footnote Nominal data is used for labeling variables without any quantitative value.
31
What is ordinal data?
Data that can be ordered or ranked, but the scale is indeterminate (e.g., 1-5 star rating) ## Footnote Ordinal data allows for relative ranking but does not specify the magnitude of differences between ranks.
32
What is interval data?
Ranking but without an absolute zero (e.g., temperature in C or F) ## Footnote Interval data allows for meaningful comparisons but lacks a true zero point.
33
What is ratio data?
Ranking but with absolute zero (e.g., temperature in K, weight, age) ## Footnote Ratio data provides the most information, allowing for comparisons and meaningful ratios.
34
What are inferential statistics?
The tests you perform after running descriptive stats to learn about your data (t-tests and such).