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Data Analysis > Unit 1 > Flashcards

Unit 1 Flashcards

Introduction to data analysis

1
Q

What is present wherever knowledge is developed?

A

Statistics

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2
Q

What are the 2 types of research?

A

Qualitative research and Quantitative research

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3
Q

What are the essential stages in research?

A

Research question
Documentation
Hypothesis formulation
Study design
Collect data
Analyze data
Interpret results

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4
Q

What is population in statistics?

A

consists of all the elements that share the characteristic under study:
people, animals, institutions, cities, etc.

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5
Q

what are some examples of quantifiable (finite) populations?

A

Social Security registrations
deaths in 2021
secondary school students.

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6
Q

what are examples of hardly quantifiable (infinite) populations?

A

Coin tosses
People with high motivation
Human beings

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7
Q

What distinguishes finite populations from infinite populations?

A

Finite populations have a specific, countable number of elements (like secondary school students), while infinite populations cannot be easily counted (like coin tosses or highly motivated people).

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8
Q

What is a sample in statistics?

A

a subset of the total number of elements that make up the population

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9
Q

What is important in samples?

A

it must be representative

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10
Q

What factors contribute to a representative sample?

A

includes size, sampling methods, simple random sampling

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11
Q

What are units of analysis?

A

elements between which some quality is compared, they are the subjects or individuals being studied, in a general way.

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12
Q

What is an example of a unit analysis when studying motivation?

A

An example would be people with high motivation at UCAM, where each individual serves as a unit of analysis.

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13
Q

What are statistics?

A

Statistics are values representing quantitative properties of a sample, such as sample means (X̄), sample standard deviations (S), and sample proportions (P)

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14
Q

What are parameters in statistics?

A

values representing quantitative properties of a population, such as means (𝜇), standard deviations (𝜎), and proportions (𝜋)

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15
Q

What do the letters “N” and “n” stand for?

A

“N” stands for the size of the population, while “n” denotes the size of the sample

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16
Q

Which letters are used for parameters?

A

Parameters use Greek letters (e.g., 𝜇 for the mean, 𝜎 for standard deviation)

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17
Q

Which letters are used for statistics?

A

Statistics use Latin letters (e.g., X̄ for the mean, S for standard deviation)

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18
Q

What is the difference between a population and a sample?

A

The population is the entire group being studied, while the sample is a subset of that group used for analysis.

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19
Q

How does statistics contribute knowledge to psychology?

A

Statistics provides tools to analyze and interpret data, helping psychologists summarize information and draw meaningful conclusions.

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20
Q

What is the goal of descriptive statistics?

A

to summarize a set of information in order to interpret it and draw conclusions

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21
Q

What are descriptive statistics?

A

they summarize and organize a set of data from a sample to provide insights, such as graphs, percentages and means

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22
Q

What is the goal of inferential statistics?

A

based on probability calculations, and from the sample data, estimate, predict or generalize conclusions.

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23
Q

What question do we address with inferential statistics?

A

they help us determine if the observations from a sample can be generalized to a larger population

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24
Q

What are some examples of methods used in inferential statistics?

A

Hypothesis testing and regression analysis
-> help estimate unknown properties of the population.

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25
Q

What are variables?

A

Variables are features of the units of analysis that can change or vary between people, over time, or between situations

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26
Q

What is a constant in statistics?

A

Modality or value shared by all the units of analysis
-> the opposite to a variable

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27
Q

How are hypotheses defined in terms of variables?

A

all hypotheses are defined in terms of at least two variables
-> one representing a proposed cause and the other representing a proposed result

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28
Q

In an experiment/quasi, what are the proposed cause and proposed result?

A

Proposed cause: Independent variabel
Proposed result: Dependent variable

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29
Q

What is a dependent variable?

A

The outcome or effect that is measured in an experiment.

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30
Q

What is an independent variable?

A

The factor that is manipulated or changed to observe its effect on the dependent variable.

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31
Q

In cross-section/long studies, what are the variables for proposed cause and proposed result?

A

Proposed cause: Predictive variable
Proposed result: Result variable

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32
Q

What is an example of an independent and dependent variable? (Energy Drinks)

A

“Energetic drinks decrease fatigue”
independent variable: energetic drinks
dependent variable: fatigue

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33
Q

How are variables typically notated in research?

A

with letters from the Latin alphabet, such as X, Y, U, V, etc.

34
Q

How are variables coded for data analysis and why?

A

numerically because it simplifies data analysis

35
Q

What are the values of variables?

A

categories that each unit of analysis can assume

36
Q

What are the two key properties required when constructing categories for variables?

A

mutual exclusion and exhaustiveness.

37
Q

What is meant by “mutual exclusion” in the context of variables?

A

that each category must exclude the others—if an individual is in one category, they cannot be in another.

38
Q

What is meant by “exhaustiveness” in the context of variables?

A

that all possible values are covered, and each individual fits into one of the categories.

39
Q

In which 3 types can variables be classified?

A

Qualitative
Quasi-quantitative
Quantitative

40
Q

What are qualitative variables?

A

categories that cannot be sorted, added, subtracted, or otherwise mathematically manipulated. They are simply equal or different.
-> without numeric values

41
Q

What category types does qualitative measurement have?

A

Excluding categories
Dichotomous: 2 categories (gender)
Polytomous: more than 2 categories (cities)

42
Q

What are examples of qualitative variables?

A

Examples of qualitative variables include gender, group, and type.

43
Q

What are quasi-quantitative (ordinal) variables?

A

categories that can be ordered but cannot be added or subtracted. (lacks meaningful numeric differences)

44
Q

What category types does quasi-quantitative measurement have?

A

ordered categories: can be ranked (low,medium,high)

45
Q

What could be an example for quasi-qualitative variables?

A

educational level, degree of agreement and social class

46
Q

What are quantitative variables?

A

values that can be sorted, added, subtracted, divided, etc.
-> involves numeric values

47
Q

What category types (variables) does quantitative measurement have?

A

Theoretically infinite values
Discrete: specific values (number students)
Continue: any value within range (height)

48
Q

What are examples of quantitative variables?

A

grade, height, weight, time

49
Q

Which are the 2 types of quantitative values?

A

Continuous and Discrete

50
Q

What are continuous values? Name examples.

A

intermediate values like height or temperature

51
Q

What are discrete values? Name examples.

A

No intermediate values like number of students or number of sessions

52
Q

What are the scales/levels of measurement and how are they determined?

A

they are determined by the relationship between categories and numbers, indicating varying degrees of arbitrariness.
-> Nominal, Ordinal, Interval, Ratio

53
Q

What does nominal mean?

A

categorical data without order (gender, city)

54
Q

What does ordinal mean?

A

Categorical data with meaningful order (educational level)

55
Q

what does interval mean?

A

Numeric data with equal distances between values but no true zero (temperature)

56
Q

what does ratio mean?

A

Numeric data with equal distances and a true zero point (weight, income)

57
Q

How can scales/levels of measurement be transformed?

A

Scales can be transformed while maintaining the relationship between categories and numbers. For example, weight can be categorized as Underweight, Normal Weight, Overweight, and Obesity.

58
Q

What is important to maintain during transformation of measurement scales

A

The relationship between categories and numbers must be preserved after transformation.

59
Q

What type of variables does the nominal scale apply to?

A

it is applicable to qualitative variables.

60
Q

How is the assignment of numbers in the nominal scale characterized?

A

The assignment is highly arbitrary; numbers are used symbolically.

61
Q

What is the only association between categories and values in the nominal scale?

A

equal or unequal

62
Q

Give examples of other variables that can be measured on a nominal scale.

A

Examples include city of residence, treatment received, etc.

63
Q

What type of variables does the ordinal scale apply to?

A

to quasi-quantitative variables

64
Q

Give examples of variables that can be measured on an ordinal scale

A

educational level, grades, severity of damage

65
Q

What is the relationship between categories and values in the ordinal scale?

A

equal/unequal and order (greater than/less than)

66
Q

What is the mathematical significance of differences between values on the ordinal scale?

A

The difference or distance between values has no mathematical meaning; they do not hold mathematical properties.

67
Q

What type of variables does the interval scale apply to?

A

to quantitative variables

68
Q

What relationships do attributes and values have on the interval scale?

A

The relationships indicate equal/unequal, order (greater than/less than)
-> differences have numerical meaning.

69
Q

What mathematical operations are valid on the interval scale?

A

The interval scale allows for some mathematical properties, including addition and subtraction.

70
Q

Is the origin of 0 an absolute value?

A

No.
-> it is arbitrarily selected (0 is not absence of temperature)

71
Q

can the interval scale have negative values?

A

yes

72
Q

What is a key distinction about proportions in the interval scale? Name an example.

A

The proportions of values are not meaningful; for example, 20 degrees is not half as hot as 40 degrees, but the distance from 0 to 20 is half the distance from 0 to 40.

73
Q

Why is it incorrect to say that year 2024 is double the “sun laps” of year 2012?

A

Even though 2024 is a bigger number, it doesn’t mean that there are “twice” as many years. The years are just measured as intervals, so we can’t use them like regular numbers to show proportions.

74
Q

What type of variables does the ratio scale apply to?

A

quantitative variables

75
Q

What makes the ratio scale non-arbitrary and more accurate?

A

uses real numbers and integrates all mathematical properties.

76
Q

What relationships do attributes and values have on the ratio scale?

A

They indicate equal/unequal, order (greater than/less than), and the distance between values has numerical meaning.

77
Q

Give an example of a calculation using the ratio scale.

A

20kg - 10kg = 10kg and 20kg : 2 = 10kg

78
Q

What does the origin of 0 represent on the ratio scale?

A

The origin of 0 is absolute; it means the absence of the feature being measured (e.g., 0 kg means no weight).

79
Q

Can the ratio scale have negative values?

A

No

80
Q

How is the ratio scale proportional?

A

it is proportional; e.g.: smith weighs 80kg, it is double the weigh of 40kg

81
Q

What does the measurement scale define?

A

The measurement scale defines the values used, not the attributes themselves.

82
Q

How is a measurement scale chosen?

A

based on
- study variable + method used to measure it
- scale determines type of analysis that can be conducted
- best to use most accurate measurement possible

Data Analysis (5 decks)

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