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PSY 302 > Midterm #1 > Flashcards

Midterm #1 Flashcards

1
Q

what are the three most common used methological designs for collecting data

A
  • correlational design
  • experimental design
  • quasi-experimental design
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2
Q

describe a correlational design

A
  • investigates relationships between variables without the researcher controlling or manipulating any of them
  • you cant draw claims about cause-ande-effect
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3
Q

describe an experimental design

A
  • used to test the causal effect of one variable on another variable
    researchers manipulate which level of an independent variable participants are assigned to
    - IV = manipulated variable
    - DV = variable that changes as a result of IV manipulation
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4
Q

random assignment

A

participants randomly assigned to receive one of the manipulations

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5
Q

describe a quasi-experimental design

A
  • a combination of correlational and experimental
  • participants assigned to a group based on their level on an already-existing characteristics. Then, their scored on a dependent variable are measured.

ex. i ask my participants whether they studied or didnt study for the final exam, and then I measure their exam grades.

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6
Q

what is a discrete variable

A

a variable that has specific values and cannot have values in between these
- ask ‘how many’ –> ex. number of children in a household

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7
Q

what is a continuous variable

A

a variable that can take on fractional values
- ask ‘how much’ –> ex. yearly income

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8
Q

what are the scales of measurement

A
  • nominal
  • interval
  • ratio
  • ordinal
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9
Q

describe nominal

A

measuring a variable using categories (ex. type of pet)

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10
Q

describe interval

A

measuring a variable using numerical values; equal intervals between adjacent values (ex. temp in Fahrenheit)

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11
Q

describe ratio

A

measuring a variable using numerical vales; equal intervals between adjacent values and a zero means the absence of the variable (ex. height in inches)

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12
Q

describe ordinal

A

measuring a variable using rankings; adjacent values are not equally spaced (ex. ranking states by their populations)

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13
Q

what are the two main functions of statistics

A
  1. describing / summarizing data
  2. making inferences from a smaller set of people to a larger set of people
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14
Q

what are descriptive statistics

A

techniques for organizing a group of numbers so the data can be more easily comprehended
- descriptive numbers (ex. mean, median, and mode)
- tables and graphs

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15
Q

what are inferential statistics

A

techniques for drawing conclusions about a very large group from a smaller subset of people
- population: a large group of people that a researcher wants to draw conclusions about from their study
- sample: a smaller subset of cases selected from the larger population

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16
Q

what do each columns of a frequency table indicate

A

first column: name of the variable and all possible values from HIGHEST to LOWEST

second column: the frequency of each value in a data set

third value: the cumulative frequency, which is the frequency of a given value or lower than the given value

fourth column: percentages column, which is the frequencies transformed into frequencies (% = frequency / total number of people *100)

fifth column: the cumulative percentage, which is the cumulative frequencies transformed into percentages (%c = fc / total number of people *100)

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17
Q

what variable are bar graphs used for and why

A

discrete variables because they offer finite categories (ex. high school, associate’s, bachelor’s, etc.)

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18
Q

what variable are histograms graphs used for and why

A

continuous variables. bars are touching (continuous variable)

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19
Q

describe the shapes of a frequency distribution

A

modality
skewness
kurtosis

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20
Q

describe modality

A

refers to how many high points (i.e. peaks) it has. peaks represent values with the highest frequency.
- unimodal = one value at highest frequency
- bimodal = two values at highest frequency
- multimodal = three plus values at highest frequency

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21
Q

describe skewness

A

the measure of how symmetrical a frequency distribution is

symmetrical distribution
- the pattern of frequencies on one half of the distribution is a mirror image of the pattern of frequencies on the other half (‘normal distribution’)

skewed distribution
- the majority of scores “pile up” on one side
- negatively skewed = fewer scores on the negative side of the distribution
- positively skewed = fewer scores on the positive side of the distribution

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22
Q

describe kurtosis

A

refers to how peaked or flat a frequency distribution is

mesokurtic = typically seen (‘normal distribution’)
platykurtic = flatter than normal distr
leptokurtic = more peaked than normal distr

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23
Q

what is the measure of central tendency

A

a single score that represents how participants tended to score on a variable

there are three main measures of central tendency:
- mean
- median
- mode

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24
Q

what symbol indicates mean

A

Mu

M - represents the average value of a variable in an entire POPULATION

μ - used to denote the sample mean. The sample mean is similar to the population mean but is calculated using a sample of data rather than the entire population

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25
Q

where is the mean pulled for a negatively skewed graph

A

left to right:
mean, median, mode

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26
Q

where is the mean pulled for a normally skewed graph

A

mean, median, and mode all occur in the center

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27
Q

where is the mean pulled for a positively skewed graph

A

left to right:
mode, median, mean

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28
Q

variability

A

refers to how similar or different the scores in a set of data are from each other

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29
Q

what are the four typical measures of variability

A
  • range
  • IQR
  • variance
  • stdev
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30
Q

how to measure IQR

A
  1. arrange scores from smallest to largest
  2. find the median (this is IQ2)
  3. find the median of scores below IQ2 (this is IQ1)
  4. find the median of scores above IQ2 (this is IQ3)
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31
Q

variance

A

average squared deviation of scores from the mean

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32
Q

stdev

A

square root of the variance; average non-squared deviation of scores from the mean

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33
Q

how to calculate degrees of freedom

A

sample - 1

33
Q

sample size notation

A

sample data: n

population data: N

34
Q

variance notation

A

sample data: s2

population data: σ2

34
Q

stdev notation

A

sample data: s, SD

population data: σ

35
Q

what are the percentages for a normal distribution

A

z-scores
-3 to -2 –> 2%
-2 to -1 –> 14%
-1 to 0 –> 34%

36
Q

where does 68% of the data land

A

between -1 and 1

37
Q

where does 96% of the data land

A

between -2 and 2

38
Q

where does 100% of the data land

A

between -3 and 3

39
Q

what does the sign of the z-score tell you

A

+ z-score = above mean
- z-score = below mean

40
Q

what does the value of the z-score tell you

A

how many stdevs the score is away from the mean

41
Q

what is the purpose of standardizing scores

A

allows us to compare how people scored on different variables that could have been originally measured in different raw units

42
Q

what is probability

A

the likelihood that an event will occur ranging from 0 to 1

43
Q

what is the common zone

A

the area of the distribution where 95% of scores are found when a phenomenon is normally distributed

44
Q

what is the rare zone

A

the area of the distribution where only 5% of scores are found when a phenomenon is normally distributed

45
Q

p < 0.05

A

it is uncommon to find someone that lands in the rare zone
- probability of finding someone who lands in the rare zone is less than 5%
- probability of landing in the rare zone is notated as p < 0.05 (p-value less than 0.05)

46
Q

p > 0.05

A

it is common to find someone that lands in the common zone
- probability of finding someone who lands in the rare zone is greater than 5%
- probability of landing in the rare zone is notated as p > 0.05 (p-value greater than 0.05)

47
Q

representative sample

A

participants have the same attributes as those that exist in the population and in approximately the same proportions, improving generalizability

48
Q

what are the methods for obtaining a representative sample

A
  • random sample
  • large sample size
49
Q

what is a sampling distribution

A

a frequency distribution created by calculating all the possible sample means (or another type of statistic) that could be obtained by randomly sampling from a given population

49
Q

what are some practical issues with sampling

A
  • self-selection bias (occurs when not everyone who is asked to participate agrees to do so)
  • sampling error (discrepancy due to random factors between a sample statistic and the population parameter being estimated)
50
Q

central limit theorem (CLT)

A

describes characteristics of a sampling distribution of means when the sample size is large and every possible sample is obtained

50
Q

for CLT, what does it mean if n is large

A

if n is greater than or equal to 30, the mean of the sampling distribution of means is equal to the population mean

51
Q

for CLT, what does it mean if

A
52
Q

standard error of the mean

A

measures the precision of the sample mean to the population

53
Q

how dos the standard error of the mean change based on the shape of the population?

A

it doesn’t, it will be normally distributed no matter the shape.

54
Q

what is standard error

A

the STDEV of a sample distribution.

55
Q

what is a point estimate

A

a single value that is used to estimate a given population (ex. a sample mean is a point estimate of a population mean)

56
Q

what are interval estimates

A

a range of values around a point estimate within which a population parameter is likely to exist
- commonly, this is the 95% confidence interval

57
Q

what is the 95% confidence interval

A

if we were to take 100 different samples and compute a 95% confidence interval for each sample, then approximately 95 of the 100 confidence intervals will contain the true mean value (μ)

+/- 1.96 –> these are the standardized values that cover a range of 95% of means around the sample mean

involves a lower and an upper bound

58
Q

what is the difference between a single sample z-test and a single sample t-test

A

A z-test is used to test a Null Hypothesis if the population variance is known, or if the sample size is larger than 30, for an unknown population variance. A t-test is used when the sample size is less than 30 and the population variance is unknown.

59
Q

what is hypothesis testing

A

the procedure for testing a hypothesis about a POPULATION using only SAMPLE data

60
Q

what is a single sample z-test

A

used to compare a single sample to a population when a population has a known population mean and a known population stdev

61
Q

two tailed vs. one tailed hypothesis

A

two-tailed population:
hypothesis does not state a particular direction of the effect of the independent variable on the dependent variable

one-tailed population:
hypothesis states a predicted direction of the effect of the independent variable on the dependent variable

62
Q

how do you check for assumptions for the single sample z-test

A

use the assumptions table:
- column one = attribute
- column two = assumption
- column three = robustness (robust to violations. ex. even if you don’t have a truly random sample you could still perform the test but you wouldn’t be able to generalize your results)

63
Q

null hypothesis

A

(H0) states the expected results if the IV had no effect on the dependent variable (ex. the population mean)

64
Q

alternative hypothesis

A

(H1) states the expected result if the IV HAS an effect on the DV (ex. does not equal population mean)

65
Q

are hypothesis about populations or samples

A

populations

66
Q

what are decision errors

A

type I and type II errors

67
Q

type I error

A

the researcher concluding that there is an effect or a relationship between variables when in reality there is not (the null hyp. is true)

68
Q

type II error

A

the researcher concluding that there is not an effect or a relationship between variables when in reality there is (the null is false)

68
Q

what is a decision rule

A

is deciding how willing you are to make a type I error

69
Q

how to set a decision rule

A
  1. construct sampling distribution of means that represent all possible means that could be obtained of a certain sample size (if the null is true)
  2. set how willing you are to make a type I error (the convention used is the alpha (a) value)
70
Q

how to interpret the data in relation to the hypothesis test

A
  • if the calculation of the test statistic fall in the rare zone (p < 0.05), reject the null hypothesis, this means the results are statistically significant
  • if the results land in the common zone (p > 0.05), fail to reject the null hypothesis, this means that the results are not significant (theres still a 5% chance you are wrong)
70
Q

what does it mean when a = 0.05

A

that means that the rare zone on the sampling distribution of means is made up of the 5% most extreme sample means that are possible to obtain when the null hypothesis is true

one tailed = 5% is on one side
two tailed = 2.5% is on one side and 2.5% is on the other side

71
Q

how do you interpret the direction of the effect

A
72
Q

why do we calculate effect size

A
  • statistical significance does not tell you the practical limitations of the study’s results
  • effect size is a way of measuring the size of the effect of the explanatory variable of the DV
  • calculate cohnen’s d
73
Q

what do cohnen’s d values correspond to

A

when cohnen’s d = …
0.00, the size of the effect is none
0.20, the size of the effect is small
0.50, the size of the effect is medium
0.80 (or greater), the size of the effect is large

74
Q

what are the six steps of hypothesis testing

A
  1. test (pick the right statistical test)
  2. assumptions (check the assumptions table to make sure you can run the test)
  3. hypotheses (list the null and alternative hypotheses)
  4. decision rule (construct sampling distribution, specify alpha, and determine the critical vales)
  5. calculation (calculate the test statistic)
  6. interpretation and effect size (interpret the results and report the effect size)

PSY 302 (2 decks)

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