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ACDS > psandca2 > Flashcards

psandca2 Flashcards

1
Q

is a branch of mathematics that deals with the systematic collection of data, summarizing and presenting data in an organized manner and analyzing data to interpret and draw conclusions from data analysis.

allows us to make sense of and interpret a great deal of information

A

Statistics

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2
Q

involves describing, organizing, and presenting data in an understandable form.
are statistical procedures that are used to summarize, organize, and simplify data (you dont compare just summarize)

Example: Who are your favorite professors in psychology? What is the average attention span of Grade 1 students?
How much money do parents spend on their child’s education on average?
What percentage of juvenile delinquents has an EQ level of below 75?

A

Descriptive statistics

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3
Q

is concerned with analyzing, interpreting, making predictions, inferences, and conclusions about the data. Example: Is there a correlation between gender and mathematical ability

allow us to compare samples and make generalization about the populations where they come from

Example: how does the average attention span of Grade 1 students compare against Grade 2 students?
On average, how does college education expenditure of Class A parents compare against class C parents?

A

Inferential statistics

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4
Q

is a collection of all the elements under consideration in the statistical study. Example: All the COVID-19 patients in the entire Philippines

Is composed of the entire group of individuals that the researcher wants to study

A

Population

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5
Q

is a part or subset of the population from which information is usually collected. Example : COVID-19 patients in Barangay New Era only

  • is a small group of individuals selected from a population
A

Sample

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6
Q

is a numerical summary (or characteristic) of the population.

A

Parameter

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7
Q

is a numerical summary (or characteristic) of the sample.

A

Statistic

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8
Q

(plural) are measurements or observations.

A

Data

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9
Q

is a collection of measurements or observations

A

data set

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10
Q

(singular) is a single measurement or observation and is commonly called a score

A

datum

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11
Q

data that have not been processed are called

A

raw scores.

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12
Q

data that are numerical in nature. Examples: Age, height, weight

A

Quantitative data

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13
Q

data that are attributes or characteristics which cannot be subjected to meaningful arithmetic computations. Example: gender, civil status

A

Qualitative data

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14
Q

data assume exact values only and can be obtained by counting. Example: No. of teeth, no. of children in your family
assume exact values only and can be obtained by counting. Example: No. of teeth, no. of children in your family

are separated by individisible categories

ex: person’s age in years, baby’s age in months

A

Discrete data

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15
Q

assume infinite values and can be obtained by measurement. Examples: Scores in an exam, size of one’s shoes

would literally take forever to count

ex: age (25 years, 11 months, 3 weeks, 2 days, 50 minutes, milli sec etc)

A

Continuous data

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16
Q

is a characteristic or property of a population or sample which makes the members different from each other. Example: Gender in a coed school is a variable.

is a characteristic or condition that is not constant (can change or has different values)

A

Variable

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17
Q

is a characteristic or property of a population or sample which makes the members like each other. Example: Gender in a class of all girls is constant.

A

Constant

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18
Q

is the process of assigning individuals, objects, or events to categories according to certain rules.

A

Measurement

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19
Q

weakest level of measurement where names, symbols or numbers are used simply for classifying subjects or categorizing subjects into different groups.

also known as categorical variables (can’t be added, subtracted, divided etc)

Examples: Gender (M=Male, F=Female); Status (1=Single, 2=Married,
3=Widowed, 4+Separated); College Major (art, biology, engineering)

hair color

A

Nominal (classificatory) scale or level

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20
Q

Contains the properties of the nominal scale, but in addition, the numbers assigned to the categories can be ranked or ordered in some low-to-high manner.

  • things that can be placed in order

Examples: teacher evaluation (1=poor, 2=fair, 3=good, 4=excellent)
Year level (1=freshman, 2=sophomore, 3=junior, 4=senior), hottest to coldest, riches to poorest, class ranking

A

Ordinal (or ranking) scale or level

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21
Q

Has all the properties of the ordinal scale, but in addition, the distances or intervals between any 2 numbers on the scale are of known size or magnitude.

must have a common and constant unit of measurement but the unit of measurement is arbitrary in that there is no “true zero” point.

ordered numbers with meaningful divisions

ex: temperature, IQ

A

Interval scale or level

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22
Q

Has all the properties of the interval scale, but in addition, it has a “true zero” point which represents none (or the complete absence of the variable being measured).

zero is meaningful

Examples: age (in years), number of correct answers on a test, time (in seconds), zero height means it doesnt exist, income earned, years of education, weight

A

Ratio scale or level

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23
Q

is the process of selecting samples from a given population.

involves the collection, analysis, and interpretation of data gathered from random samples of a population under study

A

Sampling

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24
Q
  • every member of the population being sampled has an equal probability of being selected.
    -it uses some form of random selection of research participants from accessible population
A

Probability sampling

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25
Q
  • each member of the sample is selected in such a way that each member of the population has an equal chance of being included. This is done through lottery, or through a table of numbers.
A

Simple random sampling

26
Q

is used when there is a ready list of the total population arranged in order. For example: The researcher would like to choose a sample of size 10 from the population of 500 persons. He divides 500 by 10 and gets 50. This means that he will have to include every 50th member of N after choosing a random start. He chooses 15 as the start so that the samples will include the 15th person in the list, then 65, 115, 165, 215, 265, 315, 365, 415, and 465, until 10 members are chosen.

A

Systematic sampling

27
Q

is used to ensure different important groups or strata in the population are adequately represented.

the population is divided into subsets or strata

Example: Population of College students is divided into 4

A

Stratified random sampling

28
Q

is sometimes called area sampling. The population is divided into separate groups called clusters. The clusters are randomly chosen. Each member in the selected cluster is included in the sample.

A

Cluster Sampling

29
Q

occurs when each member of the population does not have a known chance of being included in the sample. Instead, it is apt to the researcher to select who will be chosen.

does not involve the use of randomization

A

Non-Probability Sampling

30
Q

researcher chooses the closest, most available, or willing persons as respondents.
-the convenience sample may not be representative of the target population

A

Convenience sampling

31
Q

researchers select subjects that would meet the quota of each group of his desired sample. Example: The researcher gets 200 smokers and 200 non-smokers.

-stratified convenience sampling strategy

A

Quota sampling -

32
Q

starts with a selected or known respondent who in turn will point to the next possible respondent, who will in turn point to the next possible respondent (referral), and so on, until data are accumulated.

samples are rare and hard to find

A

Snowball or chain referral sampling -

33
Q
  • subjects are selected based on characteristics that each member possesses. Example: Married vs Unmarried subjects
  • is the basis of the researcher’s knowledge of the target population
A

Purposive sampling

34
Q

a type of sampling where subjects volunteer themselves as in telephone, internet, or text surveys.

A

Volunteer sampling -

35
Q

With only a handful of scores, data are simply arranged from highest to lowest.

A

ungrouped frequency distribution.

36
Q

this is a table that shows the scores in groups.

A

Grouped frequency distribution -

37
Q

a group of scores

A

Class interval -

38
Q

the end numbers of the class interval

A

Class limits-

39
Q

the number of scores falling in each class interval

A

Class frequency -

40
Q
  • the difference between the upper limit of the class and the preceding class
A

Class size

41
Q

the midpoint of a class interval (upper limit + lower limit divide 2)

A

Class mark -

42
Q

true class limits;

A

Class boundaries -

43
Q

is defined as halfway between the lower-class limit of the class and the upper-class limit of the preceding class,

A

Lower class boundary (LCB)

44
Q

is defined as halfway between the upper-class limit of the class and the lower class limit of the succeeding in class.

A

upper-class boundary (UCB)

45
Q

individual scores are combined into categories, or intervals and the listed along with the frequency socres in each interval

A

class interval frequency -

46
Q

used to show the distribution of a categorical variables

A

Bar charts -

47
Q
  • comparing levels of nominal variables
A

Vertical Bar Graphs

48
Q
  • commonly used to compare levels of ordinal variables
A

Horizontal Bar Graphs

49
Q

used to show what part of the whole each level of cateogory variable is

A

Pie charts -

50
Q

used to visualize the value of variable over time

A

Line graphs -

51
Q

uses intervals of values and has no gap between bars

A

Histogram -

52
Q

it uses the actual values of the quantitative variables
(leaf is the ones digit and the stem is the preceeding numbers)

A

Stem plot/stem and leaf plot -

53
Q

show us how the scores in a set are scattered or distributed around the mean.

A

Measures of variability or dispersion

54
Q

refers to the highest score minus the lowest score.

A

Range

55
Q

is a measure of variability that tells us how the scores cluster around the mean.

is the most commonly used and the most important measure of variability.

A

Standard Deviation -

56
Q

to illustrate the processes of organizing and describing data.

A

Frequency Distributions

56
Q

individual scores are combined into catergroies, or intervals, and then listed along with the frequency scores in each interval

A

Class interval frequency

57
Q

The most commonly used measure of central tendency. Adding scorestogether and dividing the sum by the total number scores.

A

Mean

58
Q

Another measure of central tendency that is used in situations in whic the mean might not be the representative of a distribution
(there’s an extreme score)

A

Median

59
Q

The third measure of central tendency. It is the score in a distribution that occurs with the greatest frequency

A

Mode

60
Q

Provides a quantitative measure of the differences between scores in a distribution and describes the degree to which the scores are spread out or clustered together

A

Variability

ACDS (97 decks)

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