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Final summary Flashcards

1
Q

population

A

consists of all the items or individuals about which you want to draw a conclusion

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2
Q

sample

A

is the portion of the population selected for analysis

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3
Q

measuring

A

means linking numerical values to research objects

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4
Q

Observation unit i.e. statistical unit

A

is a single research object

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5
Q

observation

A

is the measured result (value) that is related to one research object

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6
Q

variable

A

is a characteristic of an item or
an individual

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7
Q

Discrete variables

A

arise from a counting process

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8
Q

Continuous variables

A

arise from a measuring
process

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9
Q

Sampling methods can be categorized

A

Probability - s in which the elements being included have a known chance of being selected
Non-probability samples - participants are selected in a purposeful way.

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10
Q

Probability sampling methods

A

Simple random sampling
- Systematic random sampling
- Stratified sampling
- Cluster sampling
- Sequence sampling
Probability samples are samples in which the elements being included have a
known chance of being selected.

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11
Q

Non-probability sampling methods

A

-Judgment sampling
-Quota sampling
Non-probability samples are ones in which participants are selected in a purposeful way.

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12
Q

Systematic random sampling

A

The sampling units are chosen from the sampling frame at a uniform interval at a
specified rate
- Sampling interval k = N/n (N = size of the population, n=sample size
- The starting point is selected from the first interval and the very kth element is selected
- For example: N = 200, n = 10 → k = 200/10 = 20

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12
Q

Simple random sampling

A

elements in the whole population are numbered and selected by using random numbers

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13
Q

Stratified sampling

A

the population devided into exclusive strata/groups (based on
nationality, profession, gender….)
- Each element can be included only in one strata
- Sample is drawn randomly from each strata/group

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14
Q

Cluster sampling

A
  • each cluster represents the whole population
  • random clusters are selected to the sample
  • -selected clusters are included fully or randoms samples are selected
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15
Q

Sequence sampling

A

elements are picked up sequentially until the results do not change anymore

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16
Q

Judgement sampling

A

Relies on judgement or expertise

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17
Q

Quota sampling

A

The first step is to estimate the sizes of the various subclasses or strata in the population
-sampling continues until each quota is full
- Even quota
- Proportional quota
- Optimal quota

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18
Q

Even quota

A

the same number of elements is picked from each strata (e.g. 100
male and 100 female)

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19
Q

Proportional quota

A

if in population 60% male and 40% female, the sample
is drawn in the same proportions: 60 male + 40 female = total sample size 100

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20
Q

Convenience sampling

A

the participant are self-selecting, there is no sample design

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21
Q

Sampling frame

A

a list of the population

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22
Q

Sample size

A

is affected by the desired accuracy of the results

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23
Q

p= (confidence of p)

A

percentage in the sample, The maximum margin of error (e ) is reached with p = 50%

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24
S
standart deviation, Measure the average scatter around the mean
25
e
margin of error
26
Z
critical value
27
Confidence of mean
method of calculating a sample size, based on deviation of the population mean (margin of error)
28
Confidence of percentage
method of calculating a sample size, based on percentage margin of error
29
Margin of error tables
1. presents the effect of the sample size on the margin of error of percentages and a 95% confidence level 2. presents the sample size based on margin of error and population size
30
Cumulative percentage distribution
divided the cumulative frequency by the number of observations, F%
30
Frequency
number of observations, f, Number of each value of the variable in the sample
30
Cross tabulation
presents the results of two (or more) variables The table contains frequencies in each cell at the intersection of rows and columns
30
Cumulative frequency
is the running total for the data, F
31
Relative frequency
is the frequency in each class divided by the total number of observations, f%
32
Numerical descriptive measures are classified
as measures of central tendency and measures of variation and shape
33
Measures of central tendency
mode, median, mean, quartiles and fractals
34
Mode
The value in a set of data that appears most frequently
35
Median
● The middle value in a set of data that has been ranked from smallest to largest ● Half the values are smaller or equal to the median and half the values are larger or equal to the median ● If there is an even number of values, the median is either of the two values in the middle, or mean of the two middle values
36
Arithmetic mean
The arithmetic mean (often simply called the "mean" or "average") is a measure of central tendency that represents the sum of all values in a data set divided by the number of values. ˉ X is the arithmetic mean.
37
Xi
represents each individual value in the data set
38
Quartiles
Arrange the data in ascending order. Find Q2 (the median): If the number of data points is odd, the median is the middle number. If the number of data points is even, the median is the average of the two middle numbers. Find Q1 (first quartile): The first quartile is the median of the lower half of the data (excluding the overall median if the number of data points is odd). Find Q3 (third quartile): The third quartile is the median of the upper half of the data (excluding the overall median if the number of data points is odd).
39
Fractals
It is any other division of the data. It is necessary that the data can be arranged in descending or ascending order
40
Range
Largest value minus the smallest value
41
Interquartile range
● Interquartile Range = Q3 - Q1 ● Extreme values do not affect
42
Normal disrtibution
Normality is tested by: Kolmogorov-Smirnov and Shapiro-Wilk tests - If the sample size is less than 50 Shapiro-Wilk test is used, if over 50, Kolmogorow-Smirnov test is used - If sig.>0.05 -> the variable is normally distributed
43
statistical testing
if some phenomenon is present in the sample, is it also present in the population. Statistical testing tells which of the hypotheses is supported
44
Hypothesis
is some theory of a particular parameter of the population
45
Null hypothesis
is always formed as “no difference” or “no correlation” H0: σ1 = σ2
46
Parametric tests assume
- Data at the interval or ratio level of measurement - Normal distribution of the population (the test variable is normally distributed)
47
p-value
is the probability of getting a test statistic equal to or more extreme than the sample result, given that the null hypothesis is true. The p-value is often referred to as the observed level of significance If Р is less than 0,05 => Н1 correct
48
correlation
relationships between variables Knowing the value of value X, we can say something additional about Y
49
Variation
measures the spread of values in a data set
50
The shape of a data set
represents a pattern of all values, from lowest to highest value
51
If sig (p-value) < 0,05
Н1 correct
52
methods to analyse statistical correlation
- Cross tabulation Chi-square (χ2) - Statistical measures o Pearson’s correlation coefficient o Spearman’s rank-order correlation (non-parametric) o Partial correlation
53
Scatter plot
From the scatter plot you can see approximately 1) existence of correlation 2) character of the correlation 3) Extreme values (outliers)
54
Report the result in Chi square test
There is a statistically significant correlation between gender and choice of department (because Chi-square p = 0,013 < 0,05). Male students are typically most often studying Business administration, while females are divided between Business administration and International business more evenly
55
The credibility of the Chi-square test
if the test is credible, the expected values in each cell should not be less than 1 and the expected value can be under 5 only in 20% of the cells
56
Pearson’s correlation coefficient
| r | > 0.7 strong linear correlation 0.3 ≤ | r | ≤ 0.7 average linear correlation | r | < 0.3 weak linear correlation
57
if p < 0.05
there is a statistically significant correlation between the variables
58
Practical interpretation of the results of Pearson's
There is a positive (r = 0,363), average (0,3 < | r | < 0,7) linear correlation between gender and participation in lessons. On average, women participate more than men.

R&D (4 decks)

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