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pyb110 > Exam Revision > Flashcards

Exam Revision Flashcards

1
Q

nominal

A

variables that have no numerical value and instead are categories (e.g. catholic, christian etc.).

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2
Q

ordinal

A

no numerical value as such but something that can somewhat be calculated (e.g, low, medium, high).

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3
Q

equal interval

A

can be numerical, must have an equal distance (e.g. 5000, 10000, 15000).

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4
Q

frequency tables

A

arranged to that there are three columns.

  1. number
  2. frequency
  3. percentage
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5
Q

grouped frequency tables

A

the same as normal frequencies except numbers are grouped into equal intervals in order to reduce the number of rows.

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6
Q

histograms

A

arranged so that the x variable, the horizonal axis is the number and that the y axis is the frequency.

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7
Q

frequency polygon

A

developed using a histogram. each maximum value of each number is taken and then plotted into a graph.

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8
Q

unimodal

A

has one maximum peak.

has one mode.

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9
Q

bimodal

A

two peaks and therefore two values that have a greater frequency than the others.

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10
Q

multimodal

A

multiple peaks, occurs when there are multiple values with a greater frequency than the others. i.e. multiple modes.

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11
Q

negative skew

A

The left tail is longer; the mass of the distribution is concentrated on the right of the figure. The distribution is said to be left-skewed, left-tailed, or skewed to the left, despite the fact that the curve itself appears to be skewed or leaning to the right; left instead refers to the left tail being drawn out and, often, the mean being skewed to the left of a typical center of the data.

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12
Q

positive skew

A

The right tail is longer; the mass of the distribution is concentrated on the left of the figure. The distribution is said to be right-skewed, right-tailed, or skewed to the right, despite the fact that the curve itself appears to be skewed or leaning to the left; right instead refers to the right tail being drawn out and, often, the mean being skewed to the right of a typical center of the data.

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13
Q

leptokurtic

A

thinner than the normal distribution. there is a sharper peak around the mean.

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14
Q

platykurtic

A

distribution is broad and flat.

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15
Q

characteristics of the Z distribution

A

Mean =0

SD=1

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16
Q

criterion variable

A

dependent variable

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17
Q

predictor variable

A

independent variable

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18
Q

positive correlation

A

A positive correlation is a relationship between two variables where if one variable increases, the other one also increases.

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19
Q

negative correlation

A

Negative correlation is a relationship between two variables in which one variable increases as the other decreases, and vice versa.

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20
Q

Difference between strong and weak correlation

A

The more closer the value of r is to its endpoints, the stronger is the correlation. If the value of r is close to 0 then we conclude that the correlation is weak and hence there is no linear relationship between the variables.

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21
Q

descriptive statistics

A

summarise data

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22
Q

inferential statistics

A

generalise data from a sample into a population.

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23
Q

characteristics of the normal distribution

A

mean = median = mode

  • bell curved
  • symmetrical
  • unimodal
24
Q

proportion, percentage and probability

A

are all interchangeable terms.

25
Q

one tailed tests

A

used when the hypothesis is directional (i.e. hypothesised to either increase or decrease).

26
Q

two tailed tests

A

used when the hypothesis is non-directional. (i.e. there will be a ‘change’).

27
Q

cut off points for the Z distribution

A 
one tailed (0.01)= +/- 2.33 
two tailed (0.01)= +- 2.58 
one tailed (0.05)= +/- 1.64
two tailed (0.05)= +- 1.96
28
Q

Type I error

A

when you reject the null hypothesis but it is true.

= alpha = cut off

29
Q

type II error

A

when you retain the null hypothesis but it is false.

= a -B = power

30
Q

Power

A

probability that the study will give a significant result if the research hypothesis is true. i.e. rejecting the null when it is false.
is the area of the H1 distribution which is beyond the critical value on the Ho distribution.

31
Q

sampling error

A

the mean of one sample is not likely to be the mean of the population.

32
Q

minimising error

A

easiest way to minimise error is to use a larger sample.

increasing sample size will also increase power.

33
Q

what happens when a is reduced?

A

probability of type I error decrease, while type II increases.

34
Q

characteristics of the distribution of means

A
  • unimodal
  • symmetrical
    this is as average scores become more likely in a distribution of means.
    more scores in a sample, the more it will resemble a normal distribution.
35
Q

z test vs t test

A

z test is conducted when the SD and M is known.

t test is when the SD or variance is unknown.

36
Q

t test single sample vs t test dependent

A

single sample is a test in which a sample mean is being compared to a known population mean and an unknown population variance.
t test dependent is when there are two scores for each person and the population variance is unknown. Here, the population mean is assumed to be 0 and all calculations are doe on difference scores.

37
Q

t test dependent vs t test independent

A

t dependent is difference scores taken from one group of people.
t independent is scores from two different groups of people.

38
Q

t test assumptions

A
  1. The assumption for a t-test is that the scale of measurement applied to the data collected follows a continuous or ordinal scale
  2. The second assumption made is that of a simple random sample, that the data is collected from a representative, randomly selected portion of the total population.
  3. The third assumption is that the data, when plotted, results in a normal distribution, bell-shaped distribution curve.
  4. The fourth assumption is that a reasonably large sample size is used. A larger sample size means that the distribution of results should approach a normal bell-shaped curve.
  5. The final assumption is homogeneity of variance. Homogeneous, or equal, variance exists when the standard deviations of samples are approximately equal.
39
Q

t test independent assumptions

A
  • scores are independent
  • populations are assumed to be normally distributes.
  • population distributions are assumed to have the same variance (homogeneity of variance).
40
Q

t test dependent assumptions

A

The dependent variable must be continuous (interval/ratio).
• The observations are independent of one another.
• The dependent variable should be approximately normally distributed.
• The dependent variable should not contain any outliers.

41
Q

assumptions of the chi squared test

A

that the observations are indpendent of each other (unrelated).
thus, there is no repeated measures and each person can only supply one observation.

42
Q

characteristics of the chi squared distribution

A

Chi-square is non-symmetric.
There are many different chi-square distributions, one for each degree of freedom.
The degrees of freedom when working with a single population variance is n-1.

43
Q

characteristics of the t distribution

A

gradually approaches a normal distribution as sample size increases.

44
Q

determining whether to retain or reject the null hypothesis

A

if the score is greater than the critical score then reject.
if it is smaller, retain.

45
Q

t critical vs z critical

A

the cut off value is more extreme (higher) for the t test.

46
Q

methods of sampling

A
  • random sampling
  • non random sampling which may include;
    ; haphazard selection (person next to you).
    ; convenience sample (friends and family).
    ; snowball sample (friends pass word onto their friends).
47
Q

statistical significance

A

if the probability is less than the cut off (5% or 1%), then it is unlikely to be due to chance.

48
Q

the comparison distribution represents…

A

the situation where H0 is true .

49
Q

using 1% cut off rather than 5% results in..

A

higher chance of a type II error.

50
Q

qualitative research

A
  • suggests that we can never get rid of our subjectivity. Therefore, we cannot properly see the world through the eyes of another.
    • Qualitative research is a means for exploring and understanding the meaning individuals or groups ascribe to a social or human problem.
    • Qualitative research is a human focused approach to research design, which aims o delve into people’s experiences, perceptions, behaviour and beliefs.
    • Qualitative research explores the processes at play on society, examines the meanings that individuals make of particular events, and provides a window into understanding why people do what they do and think what they think.
51
Q

paradigms in social research

A 
- Philosophy
		○ How the world functions? 
	- Ontology 
		○ What exists and what is real? 
	- Epistemology 
		○ What constitutes valuable knowledge? 
		○ How is knowledge acquired? 
	- Methodology 
		○ What constitutes data? 
		○ How should data be collected?
52
Q

positivist paradigm

A 
- Philosophy 
		○ Scientific materialism 
	- Ontology 
		○ Laws of nature 
	- Epistemology 
		○ Measurable and observable 'proof'. 
	- Methodology 
Experimental design, survey research.
53
Q

social constructionist paradigm

A
  • Philosophy
    ○ Person centred reality as a social construct, pluralisation of life worlds, contextual verities.
    • Ontology
      ○ Relations and processes are real, not states and characteristics.
    • Epistemology
      ○ Self verified evidence, grounded theory, recorded testimony.
    • Methodology
      ○ Ethnography, case study research, action research.
54
Q

deductive research

A

-bottom up approach

55
Q

inductive research

A

bottom up approach

pyb110 (1 decks)

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