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PSYCH 211/D > Chapter 2 > Flashcards

Chapter 2 Flashcards

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1
Q

Describe the purpose of organizing raw data in a survey.

A

Organizing raw data is essential to make it informative and relevant to the questions being investigated, as raw data alone may not provide clear insights.

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2
Q

Define absolute frequency in the context of frequency distributions.

A

Absolute frequency (f) refers to the number of times a certain value occurs in a distribution.

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3
Q

How are nominal values listed in frequency distributions?

A

Nominal values are listed in any order in frequency distributions.

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4
Q

Explain the significance of relative frequency in data analysis.

A

Relative frequency (rf) allows for comparison of how often a value occurs relative to the total number of values, making it useful for comparing groups of different sizes.

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5
Q

What is cumulative frequency and how is it calculated?

A

Cumulative frequency (cf) is the sum of frequencies from the bottom up for each value in a distribution.

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6
Q

Describe the types of variables and their order in frequency distributions.

A

Ordinal, interval, and ratio variables are listed from highest to lowest, while nominal values can be listed in any order.

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7
Q

Define frequency distribution and its components.

A

A frequency distribution lists all possible values a variable can take and how often each value is present, including those with a frequency of zero.

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8
Q

How can organizing data improve the understanding of survey results?

A

Organizing data helps to clarify patterns, trends, and relationships within the survey results, making it easier to draw conclusions.

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9
Q

Describe absolute frequency in the context of data analysis.

A

Absolute frequency refers to the number of times each given value is observed within a dataset, such as the number of homosexual and heterosexual couples in a study.

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10
Q

How is relative frequency calculated?

A

Relative frequency is calculated by dividing the frequency of a given observation by the total number of observations, often expressed as a percentage by multiplying by 100.

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11
Q

Define the significance of comparing absolute and relative frequency.

A

Comparing absolute and relative frequency is significant for understanding data from groups of different sizes or when a single group has an unusual size, making the data easier to interpret.

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12
Q

How can percentages be derived from relative frequency?

A

Percentages can be derived from relative frequency by multiplying the relative frequency by 100.

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13
Q

Explain the importance of using relative frequency in data analysis.

A

Using relative frequency is important because it allows for meaningful comparisons between groups of different sizes, providing a clearer understanding of the data.

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14
Q

What example illustrates the calculation of relative frequency?

A

An example of calculating relative frequency is (2/25) * 100, which equals 8%, indicating that 2 out of 25 observations represent 8% of the total.

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15
Q

Describe the process of calculating Cumulative Relative Frequency.

A

Cumulative Relative Frequency is calculated by dividing each cumulative frequency by the total number of observations.

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16
Q

How does Cumulative Relative Frequency enhance data interpretation?

A

Cumulative Relative Frequency provides more meaningful conclusions than cumulative frequency alone.

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17
Q

Define Cumulative Relative Frequency in the context of relationship satisfaction.

A

Cumulative Relative Frequency can show the percentage of couples reporting their relationship satisfaction at a certain level, such as 7 or below.

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18
Q

Describe the purpose of grouping data.

A

Grouping data helps manage too many possible values by organizing them into intervals, making analysis easier.

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19
Q

Define ordinal variables and give an example.

A

Ordinal variables are those that have a meaningful order but not a consistent difference between values, such as ranking students from 1st to last.

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20
Q

How do interval and ratio variables differ from ordinal variables?

A

Interval and ratio variables are continuous and can take infinitely many possible values, unlike ordinal variables which are discrete.

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21
Q

What is the significance of using equal sized intervals when grouping data?

A

Equal sized intervals ensure consistency in data representation and facilitate easier comparison and analysis.

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22
Q

Explain the concept of mutually exclusive intervals in data grouping.

A

Mutually exclusive intervals mean that each data point can only belong to one interval, preventing overlap and ensuring clarity in categorization.

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23
Q

How should data be organized when listing values from highest to lowest?

A

Data should be sorted in descending order, starting with the highest value and ending with the lowest.

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24
Q

What is a common scenario that necessitates grouping data into intervals?

A

Grouping is often necessary when there are more than 20 values of a variable, making it impractical to analyze each value individually.

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25
Q

Describe a grouped frequency distribution.

A

A grouped frequency distribution organizes data into intervals, showing the frequency of data points within each interval.

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26
Q

Define the purpose of sentencing time in a grouped frequency distribution.

A

Sentencing time in a grouped frequency distribution is used to analyze the duration of sentences given to youth for identical crimes and similar histories.

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27
Q

How are intervals determined in a grouped frequency distribution?

A

Intervals in a grouped frequency distribution are determined by setting an interval width and calculating the midpoint of each interval.

28
Q

What is meant by equal width in a grouped frequency distribution?

A

Equal width in a grouped frequency distribution means that each interval has the same range of values.

29
Q

Explain the concept of mutually exclusive intervals.

A

Mutually exclusive intervals in a grouped frequency distribution ensure that each data point falls into one and only one interval, preventing overlap.

30
Q

How can the midpoint of an interval be calculated?

A

The midpoint of an interval can be calculated by averaging the lower and upper boundaries of the interval.

31
Q

What are the possible values in a grouped frequency distribution?

A

The possible values in a grouped frequency distribution refer to the range of data points that can be categorized into intervals.

32
Q

Describe the concept of exact limits of intervals in relation to continuous variables.

A

Exact limits of intervals refer to the boundaries that define the range of values for continuous variables, determined by the precision of the data.

33
Q

How are exact intervals determined based on the precision of data?

A

Exact intervals are determined by adding and subtracting half of the smallest unit of measurement from the measured value.

34
Q

Define the exact limits for a measurement of ‘2’ when rounded to the nearest whole number.

A

The exact limits for a measurement of ‘2’ rounded to the nearest whole number are 1.5 and 2.5.

35
Q

Explain the significance of levels of precision in measurements.

A

Levels of precision indicate how finely a measurement is made, affecting the exact limits of the interval around that measurement.

36
Q

Define the axes used in graphing distributions.

A

Frequency is often represented on the y-axis (ordinate), while values of the variable (or groups) are on the x-axis (abscissa).

37
Q

How should axes be handled in graphing distributions?

A

Axes that do not begin at zero should be broken.

38
Q

Explain the use of bar graphs in data representation.

A

Bar graphs are used for discrete data on the x-axis, where each bar represents a possible value of a variable.

39
Q

What does the height of a bar in a bar graph represent?

A

The height of the bar indicates the frequency with which the value occurred.

40
Q

Identify the types of data that can be depicted on the y-axis of a bar graph.

A

The y-axis can depict ordinal, interval, and ratio data.

41
Q

Differentiate between single and multiple bar graphs.

A

Single bar graphs display one set of data, while multiple bar graphs can show comparisons between different sets of data.

42
Q

Describe the characteristics of a histogram.

A

A histogram displays continuous data on the x-axis with attached bars, where intervals have exact limits defined as plus or minus half of the smallest unit of measurement.

43
Q

How are the limits of intervals in a histogram defined?

A

The limits of intervals in a histogram are defined as exact limits, which are plus or minus half of the smallest unit of measurement.

44
Q

Explain the difference between a histogram and a frequency polygon.

A

A histogram uses bars to represent data, while a frequency polygon uses points plotted over the midpoints of intervals.

45
Q

Define the term ‘midpoint’ in the context of a frequency polygon.

A

The midpoint in a frequency polygon is the value that lies in the center of an interval, such as 17 for the interval 15-19.

46
Q

How are points represented in a frequency polygon?

A

Points in a frequency polygon are plotted over the midpoints of intervals, connecting them to form a line.

47
Q

What is the significance of apparent limits in a histogram?

A

Apparent limits may provide clearer or more meaningful labels on the graph, but the exact limits are crucial for data analysis.

48
Q

Describe the type of graph suitable for comparing socioeconomic status between Floridians and Californians.

A

A comparative bar graph or a box plot would be suitable for visualizing the differences in socioeconomic status between the two groups.

49
Q

Define an ogive in the context of statistical data representation.

A

An ogive is a cumulative frequency graph that shows the number of observations below a particular value, allowing for the determination of relative standing.

50
Q

How can you determine the relative standing of a value in a dataset?

A

Relative standing can be determined by calculating the cumulative frequency and identifying the percentile rank of the value within the dataset.

51
Q

Do cumulative frequency polygons help in understanding data distribution?

A

Yes, cumulative frequency polygons provide a visual representation of the cumulative frequencies, helping to understand the distribution and trends in the data.

52
Q

Explain how to find the value needed to be in the 10th percentile of a dataset.

A

To find the value needed to be in the 10th percentile, calculate the cumulative frequency and identify the data point that corresponds to 10% of the total observations.

53
Q

What is the significance of plotting cumulative frequency over the upper exact limit of each interval?

A

Plotting cumulative frequency over the upper exact limit of each interval allows for a clear representation of how many data points fall below each threshold, aiding in the analysis of data distribution.

54
Q

Describe the characteristics of frequency distributions.

A

Frequency distributions can be characterized by symmetry (symmetrical vs. skewed) and kurtosis (spread or scatter of observed values).

55
Q

Define skewness in the context of frequency distributions.

A

Skewness refers to the asymmetry of a distribution, where the majority of observations are concentrated on one side.

56
Q

How can you identify a positively skewed distribution?

A

A positively skewed distribution has a tail that points toward the right, indicating that the majority of observations are concentrated on the left.

57
Q

How can you identify a negatively skewed distribution?

A

A negatively skewed distribution has a tail that points toward the left, indicating that the majority of observations are concentrated on the right.

58
Q

Explain the concept of kurtosis in frequency distributions.

A

Kurtosis refers to the spread or scatter of observed values in a distribution, indicating how peaked or flat the distribution is.

59
Q

What is an example of a skewed distribution?

A

An example of a skewed distribution is average income, where most individuals earn below the average, creating a positive skew.

60
Q

Define kurtosis in the context of data distribution.

A

Kurtosis is an indication of variability within data relative to a normal distribution.

61
Q

Describe the characteristics of a platykurtic distribution.

A

A platykurtic distribution is flat compared to a normal distribution.

62
Q

What is a leptokurtic distribution?

A

A leptokurtic distribution is skinny compared to a normal distribution.

63
Q

How does a mesokurtic distribution compare to a normal distribution?

A

A mesokurtic distribution is moderate and resembles a normal distribution.

64
Q

List the types of kurtosis classifications.

A

The types of kurtosis classifications are platykurtic, leptokurtic, and mesokurtic.

65
Q

Explain the significance of kurtosis in data analysis.

A

Kurtosis helps in understanding the shape of the data distribution and its variability compared to a normal distribution.

PSYCH 211/D (9 decks)

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