Exam 2 Flashcards

1
Q

What are the two types of Variables?

A

Independent Variable (IV) and Dependent Variable (DV)

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2
Q

What is a variable?

A

construct that takes on different values

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3
Q

What is an Independent variable?

A

the variable that is thought to influence changes in another variable

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4
Q

What is a Dependent variable?

A

the variable thought to be changed by another variable

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5
Q

What is the definition of causal relationships?

A

the assumption that changes in the independent variable cause observed changes in the dependent variable

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6
Q

What is the definition of Recursive causal model?

A

a variable relationship where one variable influences another but not the other way around

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7
Q

What is the definition of Nonrecursive causal model?

A

a variable relationship where a variable can be both the cause and effect

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8
Q

What is the definition of non-causal relationships?

A

the assumption that variables are associated but don’t cause changes in the other

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9
Q

What are Ordered or continuous variables?

A

variables that can be assigned numerical values such as age, weight, temperature and income

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10
Q

What are Nominal or Categorical variables?

A

variables that can be differentiated only on the basis of type such as gender and race

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11
Q

What is main effect?

A

effects due to multiple independent variables working alone to affect the dependent variable(s)

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12
Q

What is interaction effect?

A

effects due to multiple independent variables working together to affect the dependent variable(s)

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13
Q

What are Research Questions?

A

formal question posed to guide research

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14
Q

What are 2 things research questions can accomplish?

A

describe communication behavior and relate communication behavior to other variables

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15
Q

What does a descriptive question do?

A

Attempt to categorize a concept and measures how it varies in type or amount

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16
Q

What is an example of a descriptive question

A

What factors impact writing assessment practices of writing faculty?

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17
Q

What does a relationship question do?

A

measures relationships between variables

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18
Q

What is an example of a relationship question

A

What is the relationship between years of teaching experience and level of confidence in assessing writing?

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19
Q

What is a hypothesis?

A

predicts a relationship between independent and dependent variables

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20
Q

What are the 2 types of relationships between variables

A

positive and negative relationship

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21
Q

What is a Positive Relationship?

A

This type of variable relationship shows that an increase in an independent variable increases the dependent variable. This is also known as direct relationship

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22
Q

What is a Negative Relationship?

A

This type of variable relationship shows that an increase in an independent variable decreases the dependent variable. This is also known as inverse relationship

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23
Q

What is a one-tailed hypothesis?

A

a hypothesis that specifically predicts the specific nature of the relationship

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24
Q

What is an example of a one-tailed hypothesis?

A

I predict that the longer years of experiences a teacher has the more confident they will be in assessing students’ writing. This implies a positive influence and relationship between the 2 variables.

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25
Q

What is a two-tailed hypothesis?

A

a hypothesis that does not specifically predict the nature of a relationship between variables

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26
Q

What is an example of a two-tailed hypothesis?

A

I predict that years of experience will have an impact on levels of confidence.

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27
Q

What is the form and example of a nominal independent variable question?

A

-Is there a difference between a and b in relation to C?
-Is there a difference between teachers with 1 year and 5 years of teaching experience in relation to what they assess in student writing?

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28
Q

What is the form and example of a nominal independent variable hypothesis?

A

-A is greater on C than B. or A is lower on C than B.
-Teachers with 5 years of experience have more confidence than teachers with 1 year of experience. Or Teachers with 1 year of experience have less confidence than teachers with 5 years of experience.

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29
Q

What is the form and example of an ordered independent variable question?

A

-Is there a relationship between x (IV) and y (DV)?
-Is there a relationship between years of teaching experience and confidence?

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30
Q

What is the form of an ordered independent variable hypothesis?

A

-There is a (positive/ negative) relationship between x and y.
-There is a positive relationship between years of teaching experience and confidence.

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31
Q

What is Statistical data analysis?

A

the process of examining what quantitative data means to researchers

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32
Q

What is Statistics?

A

numeral indicator assigned to a set of data

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33
Q

What is Descriptive statistical data analysis?

A

an approach used to create simple descriptions about the characteristics in the data set

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34
Q

What are 3 ways to describe data?

A

-Create summary statistics ⇒ numerical indicators that summarize the data in terms on mean or variance
-Convert raw scores into standard scores
-Create visual displays of data

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35
Q

What 2 ways are Inferential statistical data analysis used?

A

-To estimate the characteristics of the population samples
-To test for differences in the relationships among variables

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36
Q

What are the 3 central tendency?

A

mode, median, and mean

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37
Q

What is mode?

A

indicates the score or value that occurs most frequently

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38
Q

Pros of using mode?

A

very easy to determine and great with nominal data

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39
Q

Cons of using mode?

A

not very useful when using ordinal, interval, or ratio data; also there could be multiple modes

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40
Q

What is Bimodal?

A

when there are 2 modes (a.k.a. 2 variables scores the same high amount)

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41
Q

What is Multimodal?

A

when there are 3+ modes (a.k.a 3+ variables scored the same high amount)

42
Q

What is median?

A

divides data exactly in half

43
Q

Pros of using median?

A

describes the center point of ordinal, interval, and ratio data; looks are more variables than mode; not affected by outliers

44
Q

What is the outlier effects?

A

when there is an extreme score, it can impact mean

45
Q

What is mean?

A

the average score of data rounded up to 2 decimal points (9.285 = 9.29)

46
Q

Pro of using mean?

A

the most effective measure of central tendency of data

47
Q

Con of using mean?

A

affected by extreme scores

48
Q

What are Measures of Dispersion?

A

report how much vary from each other or how they are spread out from the center point of data set

49
Q

What are the 3 measures of dispersion?

A

range, variance, and standard deviation

50
Q

What is Range?

A

reports the distance between the highest and lowest scores in a distribution

51
Q

How is the range calculated?

A

by subtracting the lowest number from the highest number also called the extreme values

52
Q

Pros of range?

A

can help show how spread out the data is and if the variables studied varied

53
Q

Con of range?

A

affected by extreme scores

54
Q

What is Lowspread?

A

the range of values between the median and the lowest score

55
Q

What is Highspread?

A

the range of values between the median and the highest score

56
Q

What is Interquartile range?

A

the range of values between the 25% and 75% percentile

57
Q

What is Semi-interquartile range?

A

the total calculated after the interquartile range is divided in half

58
Q

What is Variance?

A

the average distance of the scores in a distribution in square units

59
Q

What is Deviation score?

A

the amount one score differs from the mean

60
Q

What is the variance formula?

A

Σ(x-ẋ)2/ N

61
Q

Σ = stands for…

A

sum of scores

62
Q

X = stands for…

63
Q

Ẋ = stands for…

64
Q

N = stands for…

A

total # of scores

65
Q

What are the Steps for calculation variance?

A
  1. Get mean
  2. Subtract each score from the mean
  3. Square the difference
  4. Add them all up
  5. Divide by the total # of variables
66
Q

What is standard deviation?

A

average from the mean in original measurement units

67
Q

What is the Standard deviation formula?

A

√variance ⇒ you take the square root of the variance score

68
Q

What are Frequency distributions?

A

tally of the number of times particular values on a measurement scale occur in a data set

69
Q

What are Frequency tables?

A

a visual display in table form of the frequency of each category on a scale

70
Q

What are Inferential statistics?

A

statistical procedures that allow you to make claims about a population based on sample characteristics

71
Q

What are the purposes of using inferential statistics?

A

-Estimation
-Significance testing

72
Q

Whats is Estimation in inferential statistics?

A

is used to generalize the results obtained from a sample to its parent population

73
Q

Whats is Significance testing in inferential statistics?

A

examines how likely differences between groups and relationships between variables occur by chance

74
Q

What are the Basic Assumptions when using Inferential Statistics?

A

-Make sure you have a random selection in the sample
-Variable should be “normally” distributed in the population

75
Q

What is the The Normal Curve?

A

-chance events in large quantities tend to distribute themselves in the form of a bell curve
-Theoretically mean, median, mode all occur in the same place
-The sides never touch the base lines
-Probability

76
Q

What are the percentages that fall between each standard deviation on the normal curve?

A

-2.1%, 13.5%, 34.1%, 34.1%, 13.5%, 2.1%
-68.2%, 99.4%, 99.7%

77
Q

How are the standard deviations organized in a curve bell?

A

(-3, -2, -1, X, +1, +2, +3)

78
Q

If a sample distribution has a mean of 48 and a standard deviation of 6, what is the probability of obtaining a score from 42 to 54?
A) 14%; B) 68%; C) 5%; D) 98%

79
Q

If a sample distribution has a mean of 0 and a standard deviation of 10, what is the probability of someone scoring between 0 and 20?
A) 48%; B) 68%; C) 95%; D) other

80
Q

What is Skewness as it relates to the Normal Bell Curve?

A

skewness means that there is unequal distribution of data because of extreme scores

81
Q

What are the names of the 2 basic types of skewness?

A

Positive skewed distribution and negatively skewed distribution

82
Q

How is skewness determined?

A

Skewness is distributed to the side where the extreme score are (left = negative; right = positive)

83
Q

What does a negative skew mean?

A

this means most scores are high and there are some extreme scores that are low

84
Q

What does a positive skew mean?

A

this means that most scores are low and there are some extreme scores that are high

85
Q

What is Kurtosis?

A

height (tall or short) of curve bell tells you how the scores are distributed in the curve bell

86
Q

What are the 3 basic types of Kurtosis?

A

leptokurtic, mesokurtic, platykurtic

87
Q

What is Leptokurtic distribution?

A

curve is tall ⇒ this tells you that scores are relatively similar

88
Q

What is Mesokurtic distribution?

A

curve is normal

89
Q

What is Platykurtic distribution?

A

curve is short ⇒ this means that scores are all over the place

90
Q

What is the null hypothesis?

A

-this means there is no pattern in the data;
-it is all random;
-it’s all a coincidence

91
Q

How does the null hypothesis relate to a researcher’s hypothesis (or research question)?

A

they have to proof it wrong and proof that there is are patterns and relationships

92
Q

What is the Significance level or p value?

A

probability that a hypothesis is wrong

93
Q

What is the industry standard for what researchers should use as a p value in analyzing statistical results?

A

5% that a researcher is wrong

94
Q

What are the 3 Steps for significance testing?

A

-Pose a question and hypothesis
-Do the study and collect data
-Test the null (is there no relationship

95
Q

How do you test the null hypothesis?

A

*Set significance level (5% or 0.05)
*Compute calculated value (statistical test is done to test it out)
*Compare calculated to critical value (calculated should be the same or more)

96
Q

What is a Type I error?

A

Occurs when researchers reject the null hypothesis and accept a researcher hypothesis when in fact the null hypothesis is possibly true and should have been accepted.

97
Q

What can you do with your significance level thresholds to prevent a Type I error?

A

lower the significance level (.01 lowers to 1% chance of committing a type 1 error)

98
Q

What disadvantages to the type I error remedy?

A

by lowering the level, it can increase the likelihood of committing a type II level error.

99
Q

What is a Type II error?

A

occurs when researchers accept a null hypothesis when it was probably false and rejected their hypothesis

100
Q

What can you do with your significance level thresholds to prevent a Type II error?

A

higher the significance level such as .10

101
Q

What disadvantages to this remedy?

A

by increasing the level, it increases the chance of committing a type I error.