Statistics DDL

studied byStudied by 30 people
5.0(1)
Get a hint
Hint

Variable

1 / 109

flashcard set

Earn XP

Description and Tags

Definitions

Statistics

110 Terms

1

Variable

A characteristic or attribute that can assume different values.

New cards
2

Population

Consists of all subjects ( human or otherwise) that are being studied.

New cards
3

Sample

A group of subjects selected from a population

New cards
4

Descriptive Statistics

consists of the collection, organization, summarization, and presentation of data.

New cards
5

Inferential statistics

Consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions.

New cards
6

Probability

The chance of an event occuring

New cards
7

hypothesis testing

decision-making process for evaluating claims about a population, based on information obtained from samples.

New cards
8

Qualitative variables

variables that have distinct categories according to some characteristic or attribute (nominal, ordinal)

New cards
9

Quantitative Variables

variables that can be counted or measured (ration, interval)

New cards
10

Discrete variables

Countable, whole numbers, and assume values that can be counted.

measured in whole units/digits

New cards
11

Continuous Variables

can assume an infinite number of values between any two specific values. They are obtained by measuring. They often include fractions and decimals can be measured in decimals

New cards
12

Nominal level of measurement

classifies data into mutually exclusive (nonover-lapping) categories in which no order or ranking can be imposed on the data. e.g. married/unmarried; alive/dead; yes/no

New cards
13

Ordinal level of measurement

classifies data into categories that can be ranked; however, precise differences between the ranks do not exist. e.g. language proficiency

New cards
14

Interval level of measurement

ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero. e.g. age

New cards
15

Ratio level of measurement

possesses all the characteristics of interval measurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population. e.g. temperature

New cards
16

Four levels of measurement

  1. Nominal

  2. Ordinal

  3. Interval

  4. Ratio

New cards
17

(Simple) Random Sample

sample in which all members of the population have an equal chance of being selected

each sample of a fixed size is equally likely to be drawn from the collection of all possible random samples of the given size.

New cards
18
<p>Systematic sample</p>

Systematic sample

a sample obtained by selecting every …th member of the population.

Randomly select a starting point (that is, a population unit) and then take every …th population unit after that.

New cards
19
<p>Stratified sample</p>

Stratified sample

a sample obtained by dividing the population into subgroups or strata according to some characteristic relevant to the study. (There can be several subgroups.) Then subjects are selected at random from each subgroup.

Divide the population into mutually disjoint (non-overlapping) groups or sub-populations called strata (singular form: stratum) and then take an SRS from each stratum.

New cards
20
<p>Cluster sample</p>

Cluster sample

obtained by dividing the population into sections or clusters and then selecting one or more clusters at random and using all members in the cluster(s) as the members of the sample

Divide the population into mutually disjoint (non-overlapping) clusters (that is, groups/sub-populations), where individual clusters mirror the diversity of the entire population, while the set of clusters are similar to each other. Then randomly select some of the clusters, and all the members belonging to these chosen clusters will constitute the cluster random sample.

New cards
21

Sampling error

the difference between the results obtained from a sample and the results obtained from the population from which the sample was selected.

The actual process of sampling causes sampling errors. It is the error caused by observing a sample only instead of enumerating the entire population. A sample will never be exactly representative of the population so there will always be some sampling error. As a rule, the larger the sample, the smaller will be the error.

New cards
22

nonsampling error

occurs when the data are obtained erroneously or the sample is biased, i.e., nonrepresentative.

New cards
23

Observational study

the researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations

New cards
24

Experimental Study

the researcher manipulates one of the variables and tries to determine how the manipulation influences other variables.

New cards
25

Independent variable

in an experimental study is the one that is being manipulated by the researcher. Also called the explanatory variable.

<p>in an experimental study is the one that is being manipulated by the researcher. Also called the explanatory variable. </p>
New cards
26

Dependent variable

Resultant or outcome variable

<p>Resultant or outcome variable</p>
New cards
27

Treatment group

The group that received special instruction

New cards
28

Control group

The group that does not receive special instruction.

Often given a placebo —a (fake) treatment that cannot influence the response variable.

New cards
29

Lurking Variable

A variable or factor that has an important effect on the relationship among the variables in a study but is not one of the explanatory variables studied.

Extraneous factors which can potentially influence the effect of the explanatory variable(s) on the outcome of the response variable

New cards
30

Confounding variable

Two variables are confounded when their effects on a response variable cannot be distinguished from each other. The confounded variables may be either explanatory variables, or lurking variables, or both.

New cards
31

Some common Non-sampling Errors

  1. Non-response Bias

  2. Self-funded or self-interest studies

  3. Misleading use of data

  4. Sampling Bias

  5. Convenience sampling

  6. Voluntary Response/Self-selected samples

New cards
32

Non-sampling errors

Caused by external factors not related to the sampling process:

  • Coverage error (under coverage, over coverage),

  • Defective measurement device,

  • Processing error (data entry error, coding error, editing error)

  • poor questionnaire design,

  • Interviewer bias, biased decision making

  • Non-response bias

  • self-selection bias and

  • respondent errors, etc.

New cards
33

Statistical Inference

Usually, a statistic is used to draw meaningful scientific conclusions or decisions (or, inference) about an unknown parameter. This process of drawing inference about an unknown population or a population parameter is called ______ _______

New cards
34

Categorical data

nominal or ordinal

New cards
35

Quantitative data

ration or interval

New cards
36

Validity

How accurate is the measure/research

New cards
37

Reliability

How consistent is the measure

New cards
38

Measurement error

Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value. It includes random error (naturally occurring errors that are to be expected with any experiment) and systematic error (caused by a mis-calibrated instrument that affects all measurements)

New cards
39

Random error

Random error (also called unsystematic error, system noise or random variation) has no pattern. One minute your readings might be too small. The next they might be too large. You can’t predict random error and these errors are usually unavoidable.

New cards
40

Systematic error

Systematic error (also called systematic bias) is consistent, repeatable error associated with faulty equipment or a flawed experiment design.

New cards
41

Central tendency

Central tendency (sometimes called measure of location, central location, or just center), attempts to describe an entire set of data with one figure — a statistic such as an average amount or a median price.

Central tendency doesn’t tell you specifics about the individual pieces of data, but it does give you an overall picture of what is going on anthe entire data set. There are three major ways to show central tendency: mean, mode and median.

<p><strong>Central tendency</strong> (sometimes called <em>measure of location</em>, <em>central location</em>, or just <em>center</em>), attempts to describe an entire set of data with one figure — a statistic such as an average amount or a median price.</p><p style="text-align: start">Central tendency doesn’t tell you specifics about the individual pieces of data, but it does give you an overall picture of what is going on anthe entire data set. There are three major ways to show central tendency: <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-median-mode/"><strong>mean, mode and median</strong></a><strong>.</strong></p>
New cards
42

Mean

The mean is the average of a data set. Influenced by extreme scores (unlike median)

New cards
43

Mode

The mode is the most common number in a data set

New cards
44

Median

The median is the middle of the set of numbers

New cards
45

Dispersion

Dispersion in statistics is a way of describing how spread out a set of data is. Measured by standard deviation, interquartile range, etc.

New cards
46

Variability

Variability (also called spread or dispersion) refers to how spread out a set of data is. Variability gives you a way to describe how much data sets vary and allows you to use statistics to compare your data to other sets of data. The four main ways to describe variability in a data set are:

New cards
47

Correlation

Correlation is used to test relationships between quantitative variables or categorical variables. In other words, it’s a measure of how things are related. Tells about the direction and strength of a linear relationship. Expressed in scatterplots which can be positive, negative or show no correlation; and can be between -1 and 1

<p>Correlation is used to test relationships between <a target="_blank" rel="noopener noreferrer" href="https://www.statisticshowto.com/what-are-quantitative-variables-and-quantitative-data/">quantitative variables</a> or <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.statisticshowto.com/what-is-a-categorical-variable/">categorical variables</a>. In other words, it’s a measure of how things are related. Tells about the direction and strength of a linear relationship. Expressed in scatterplots which can be positive, negative or show no correlation; and can be between -1 and 1 </p>
New cards
48

T-test

The t test tells you how significant the differences between group means are. It lets you know if those differences in means could have happened by chance. The t test is usually used when data sets follow a normal distribution but you don’t know the population variance.

New cards
49

Chi-Square

New cards
50

QQ Plot

Q Q Plots (Quantile-Quantile plots) are plots of two quantiles against each other. A quantile is a fraction where certain values fall below that quantile. For example, the median is a quantile where 50% of the data fall below that point and 50% lie above it. The purpose of Q Q plots is to find out if two sets of data come from the same distribution.

New cards
51

PP Plot

P–P plot is a probability plot for assessing how closely two data sets agree, or for assessing how closely a dataset fits a particular model

New cards
52

Standard deviation

Standard deviation is a measure of dispersement in statistics. “Dispersement” tells you how much your data is spread out. Specifically, it shows you how much your data is spread out around the mean or average. For example, are all your scores close to the average? Or are lots of scores way above (or way below) the average score?
Small sd indicates that the data points are close to the mean => low dispersion

Large sd indicates that the data points are distant from the mean => high dispersion

<p>Standard deviation is a measure of dispersement in statistics. “Dispersement” tells you how much your data is spread out. Specifically, it shows you how much your data is spread out around the <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-median-mode/#mean">mean</a> or <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.statisticshowto.com/arithmetic-mean/">average</a>. For example, are all your scores close to the average? Or are lots of scores way above (or way below) the average score?<br>Small sd indicates that the data points are close to the mean =&gt; low dispersion</p><p>Large sd indicates that the data points are distant from the mean =&gt; high dispersion</p>
New cards
53

Interquartile range

The interquartile range is a measure of where the “middle fifty” is in a data set. Where a range is a measure of where the beginning and end are in a set, an interquartile range is a measure of where the bulk of the values lie.

New cards
54

Variance

Variance measures how far a data set is spread out. It is mathematically defined as the average of the squared differences from the mean.

<p><span>Variance measures how far a data set is spread out. It is mathematically defined as the average of the squared differences from the mean.</span></p>
New cards
55

Null hypothesis

The null hypothesis, H0 is the commonly accepted fact; it is the opposite of the alternate hypothesis. Researchers work to reject, nullify or disprove the null hypothesis. Researchers come up with an alternate hypothesis, one that they think explains a phenomenon, and then work to reject the null hypothesis.

New cards
56

Coefficient of variation

The coefficient of variation (CV) is a measure of relative variability. It is the ratio of the standard deviation to the mean (average).

The CV is particularly useful when you want to compare results from two different surveys or tests that have different measures or values.

New cards
57
<p>Boxplot</p>

Boxplot

A boxplot, also called a box and whisker plot, is a way to show the spread and centers of a data set. Measures of spread include the interquartile range and the mean of the data set. Measures of center include the mean or average and median (the middle of a data set). Also shows outliers

New cards
58

Z-scores

Simply put, a z-score (also called a standard score) gives you an idea of how far from the mean a data point is. But more technically it’s a measure of how many standard deviations below or above the population mean a raw score is.

New cards
59
<p>Normal distribution</p>

Normal distribution

Bell-shaped, desirable distribution.

New cards
60

Skewness

A skewed distribution can be either positively skewed (the fre- quent scores are clustered at the lower end and the tail points towards the higher or more positive scores) or negatively skewed (the frequent scores are clustered at the higher end and the tail points towards the lower or more negative scores).

<p><span>A skewed distribution can be either </span><em><span>positively skewed </span></em><span>(the fre- quent scores are clustered at the lower end and the tail points towards the higher or more positive scores) or </span><em><span>negatively skewed </span></em><span>(the frequent scores are clustered at the higher end and the tail points towards the lower or more negative scores).</span></p>
New cards
61

Kurtosis

Refers to the degree to which scores cluster at the ends of the distribution (known as the tails) and how pointy a distribution is

<p><span>Refers to the degree to which scores cluster at the ends of the distribution (known as the </span><em><span>tails</span></em><span>) and how pointy a distribution is</span></p>
New cards
62

Bessel’s correction

Bessels’ correction refers to the “n-1” found in several formulas, including the sample variance and sample standard deviation formulas. This correction is made to correct for the fact that these sample statistics tend to underestimate the actual parameters found in the population.

New cards
63

Degrees of freedom

Degrees of freedom of an estimate is the number of independent pieces of information that went into calculating the estimate. It’s not quite the same as the number of items in the sample. In order to get the df for the estimate, you have to subtract 1 from the number of items. Let’s say you were finding the mean weight loss for a low-carb diet. You could use 4 people, giving 3 degrees of freedom (4 – 1 = 3).

New cards
64

Positive kurtosis

A distribution with positive kurtosis has many scores in the tails (a so-called heavy-tailed distribution) and is pointy. This is known as a leptokurtic distribution.

<p><span>A distribution with </span><em><span>positive kurtosis </span></em><span>has many scores in the tails (a so-called heavy-tailed distribution) and is pointy. This is known as a </span><span style="color: rgb(255, 82, 82)">leptokurtic</span><strong><span style="color: rgb(255, 82, 82)"> </span></strong><span>distribution.</span></p>
New cards
65

Negative kurtosis

A distribution with negative kurtosis is relatively thin in the tails (has light tails) and tends to be flatter than normal. This distribution is called platykurtic

<p><span>A distribution with </span><em><span>negative kurtosis </span></em><span>is relatively thin in the tails (has light tails) and tends to be flatter than normal. This distribution is called </span><strong><span style="color: rgb(255, 82, 82)">platykurtic</span></strong></p>
New cards
66

Sampling frame

In statistics, a sampling frame is the source material or device from which a sample is drawn. It is a list of all those within a population who can be sampled, and may include individuals, households or institutions.

New cards
67

Convenience sampling

Non-random sampling method. Units are selected for inclusion in the sample because they are the easiest for the researcher to access.

This can be due to geographical proximity, availability at a given time, or willingness to participate in the research.

New cards
68

Snowball sampling

Non-random sampling method. Also known as chain sampling or network sampling, snowball sampling begins with one or more study participants. It then continues on the basis of referrals from those participants. This process continues until you reach the desired sample, or a saturation point.

New cards
69

Purposive sampling

Non-random sampling method. Units are selected because they have characteristics that you need in your sample. In other words, units are selected “on purpose” in purposive sampling.

New cards
70

Central limit theorem

The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30. Here’s what the Central Limit Theorem is saying, graphically. The picture below shows one of the simplest types of test: rolling a fair die. The more times you roll the die, the more likely the shape of the distribution of the means tends to look like a normal distribution graph.

<p>The Central Limit Theorem states that the <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.statisticshowto.com/probability-and-statistics/sampling-in-statistics/sampling-distribution/"><strong>sampling distribution</strong></a><strong> of the </strong><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/sample-mean/"><strong>sample means</strong></a> approaches a <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.statisticshowto.com/probability-and-statistics/normal-distributions/">normal distribution</a> as the <a target="_blank" rel="noopener noreferrer" href="https://www.statisticshowto.com/probability-and-statistics/find-sample-size/">sample size</a> gets larger — <em>no matter what the shape of the </em><a target="_blank" rel="noopener noreferrer" href="https://www.statisticshowto.com/what-is-a-population/"><em>population</em></a><em> distribution</em>. This fact holds especially true for sample sizes over 30. <span>Here’s what the Central Limit Theorem is saying, graphically. The picture below shows one of the simplest types of test: rolling a </span><a target="_blank" rel="noopener noreferrer" href="http://loki3.com/poly/fair-dice.html">fair die</a><span>. The </span><strong>more times you roll the die</strong><span>, the more likely the shape of the distribution of the means tends to look like a</span><strong> normal distribution graph</strong><span>.</span></p>
New cards
71

Undercoverage bias

Undercoverage bias occurs when a part of the population is excluded from your sample. As a result, the sample is no longer representative of the target population. Non-probability sampling designs are susceptible to this type of research bias.

New cards
72

Sampling bias

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields.

Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity.

New cards
73

Non-response bias

Nonresponse bias happens when those unwilling or unable to take part in a research study are different from those who do.

In other words, this bias occurs when respondents and nonrespondents categorically differ in ways that impact the research. As a result, the sample is no longer representative of the population as a whole.

New cards
74

Socially desirable responses

aka social desirability bias. Social desirability bias occurs when respondents give answers to questions that they believe will make them look good to others, concealing their true opinions or experiences. It often affects studies that focus on sensitive or personal topics, such as politics, drug use, or sexual behavior.

New cards
75

Standard error of the mean

The standard error of the mean is the variability of sample means in a sampling distribution of means. The wider the distribution, the further the sample means tend to fall from the population mean. That’s not good when you’re using sample means to estimate population means! You want narrow sampling distributions where sample means fall near the population mean.

The variability of the sampling distribution is the standard error of the mean! More specifically, the SEM is the standard deviation of the sampling distribution. The SE tells you how far your sample statistic (like the sample mean) deviates from the actual population mean. The larger your sample size, the smaller the SE.

New cards
76

Outliers

Tested with boxplot. Outliers are stragglers — extremely high or extremely low values — in a data set that can throw off your stats. For example, if you were measuring children’s nose length, your average value might be thrown off if Pinocchio was in the class.

New cards
77

Statistical models

A statistical model is a collection of probability distributions on a set of all possible outcomes of an experiment. This means that the model helps determine which results are likely to occur when given certain conditions. For example, if you are forecasting the weather, you would use a statistical model that takes into account factors such as air pressure, temperature, humidity, etc. to come up with a prediction. In other words, it uses probability distributions to create an understanding of how likely certain outcomes are under certain conditions.

New cards
78

Parameters

Dont change. Variables change. Mean is a parameter, variables are scores. T-test can also be a parameter. Outcomes of statistical procedures.

New cards
79

Confidence intervals

A confidence interval is how much uncertainty there is with any particular statistic. Confidence intervals are often used with a margin of error. It tells you how confident you can be that the results from a poll or survey reflect what you would expect to find if it were possible to survey the entire population. Confidence intervals are intrinsically connected to confidence levels. Confidence levels are expressed as a percentage (for example, a 95% confidence level). It means that should you repeat an experiment or survey over and over again, 95 percent of the time your results will match the results you get from a population (in other words, your statistics would be sound!).

New cards
80

H0 significance testing

If the sample provides enough evidence against the claim that there’s no effect in the population (p ≤ α), then we can reject the null hypothesis. Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept. Be careful not to say you “prove” or “accept” the null hypothesis.

New cards
81

Type 1 error

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error.

<p><span>You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a </span><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.scribbr.com/statistics/type-i-and-type-ii-errors/">type I error</a><span>.</span></p>
New cards
82

Type 2 error

A Type II error means not rejecting the null hypothesis when it’s actually false. Type II error means failing to conclude there was an effect when there actually was. In reality, your study may not have had enough statistical power to detect an effect of a certain size.

<p><span>A Type II error means not rejecting the null hypothesis when it’s actually false. Type II error means failing to conclude there was an effect when there actually was. In reality, your study may not have had enough </span><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.scribbr.com/statistics/statistical-power/"><strong>statistical power</strong></a><span> to detect an effect of a certain size.</span></p>
New cards
83

Which variables fit t-test?

Quantitative dependent variable, categorical independent variable

New cards
84

When can a t-test be used? use real world examples

 Experimental conditions to which people are assigned:

Control group and treatment (or experimental group)

(Between-group or independent design).

 Groups based on a mutually exclusive characteristic, for

example gender.

 In the first case group membership is manipulated,

determined by the researcher, in the second case group

membership is naturally observed.

 When analyzing difference between groups, predictor

variable (grouping variable) is always categorical.

Dependent aka outcome variable (should be) is continuous –

interval or ratio. (However, ordinal data can work too).

New cards
85

Predictor/grouping variable

independent variable

New cards
86

Covariance

Covariance in statistics measures the extent to which two variables vary linearly. The covariance formula reveals whether two variables move in the same or opposite directions.

Covariance is like variance in that it measures variability. While variance focuses on the variability of a single variable around its mean, the covariance formula assesses the co-variability of two variables around their respective means. A high value suggests an association exists between the variables, indicating that they tend to vary together.

New cards
87

Correlation coefficients

knowt flashcard image
New cards
88

Rationale for the t-test

 Two samples of data are collected and the sample means calculated.

 Means vary from sample to sample, and we use standard error as a measure of how much means fluctuate => we use SE as an estimate of the error in our model.

 Null hypothesis: means from two random samples are very similar => they come from one population.

 If the means are significantly different, they come from different populations.

 The larger is the difference between the sample means (relative to the standard error – shows variability between the samples), the more likely it is that the two sample means differ because of the effect of manipulation (the independent variable).

New cards
89

What are the conditions needed for a t-test?

  • 1. Correct variable types (Dependent variable should be quantitative; Independent variable should be categorical with two levels)

    2. Independent or paired samples (Independent samples: One data point per participant; Paired samples: Two data points per participant)

    3. Normal distribution

    4. Homogeneity of variance (The variance should be roughly equal per group; Needed if your groups have different sample sizes; Rule of thumb: Largest SD should be less than twice as big as the smallest)

New cards
90

T-test process

 Create a hypothesis and determine significance level.

 Test assumptions -> see practical 5 on Thursday.

 Prepare necessary calculations (M, S2 ) – SPSS does this for you.

 Calculate t-value – SPSS does this as well.

 Compare t-value against t critical value using the t critical value table. This is a value we would expect to get if our null hypothesis was true.

 If the t-value we calculated exceeds the critical value at our level of significance, then we are confident that the difference between groups exists, and it is significant.

 Compare t-value against t critical value using the t critical value table. This is

a value we would expect to get if our null hypothesis was true.

 If the t-value we calculated exceeds the critical value at our level of significance, then we are confident that the difference between groups exists, and it is significant.

New cards
91

Significance level (α)

“chance for error”, the chance that your reject a null hypothesis (and accept an alternative hypothesis) by mistake.

New cards
92

Pearson’s r

The Pearson correlation coefficient (r) is the most common way of measuring a linear correlation. It is a number between –1 and 1 that measures the strength and direction of the relationship between two variables.

<p><span>The </span><strong>Pearson correlation coefficient (<em>r</em>)</strong><span> is the most common way of measuring a linear correlation. It is a number between –1 and 1 that measures the strength and direction of the relationship between two variables.</span></p>
New cards
93

When to calculate effect size?

Effect size tells you how meaningful the relationship between variables or the difference between groups is. It indicates the practical significance of a research outcome. A large effect size means that a research finding has practical significance, while a small effect size indicates limited practical applications.

Before starting your study

Knowing the expected effect size means you can figure out the minimum sample size you need for enough statistical power to detect an effect of that size.

After completing your study

Once you’ve collected your data, you can calculate and report actual effect sizes in the abstract and the results sections of your paper.

New cards
94

Paired samples

• This is also known as matched or related samples, which is not a common situation!

• Same individuals at 2 different times before and after some potentially relevant event.

• e.g.: You take a stats test before and after taking Statistics in Media

• Matched pairs of individuals, similar in all respects except the one defining the two samples.

• e.g.: Educational psychologists comparing abilities of identical

twins (no genetic difference) who have been brought up separately or educated differently.

• If you can’t match pairs of observations, the samples are not paired.

• If the sample sizes are unequal, they are not paired.

New cards
95

Paired t-test (paired sample test)

A paired t-test is used to compare a single population before and after some experimental intervention or at two different points in time (for example, measuring student performance on a test before and after being taught the material).

New cards
96

Cronbach’s alpha

To check the internal reliability of a scale (when using sub-scales in the questionnaire), we use Cronbach’s alpha measure.

We can also use Cronbach’s alpha when we want to combine several questions together, to check whether there is a consistency between them on theoretical and statistical levels.

This reliability test will produce a value between 0 (no reliability) and 1 (perfect reliability).

<p>To check the internal reliability of a scale (when using sub-scales in the questionnaire), we use Cronbach’s alpha measure.</p><p>We can also use Cronbach’s alpha when we want to combine several questions together, to check whether there is a consistency between them on theoretical and statistical levels.</p><p>This reliability test will produce a value between 0 (no reliability) and 1 (perfect reliability).</p>
New cards
97

Issues with Cronbach’s alpha

A high level for alpha may mean that the items in the test are highly correlated. However, α is also sensitive to the number of items in a test. A larger number of items can result in a larger α, and a smaller number of items in a smaller α. If alpha is high, this may mean redundant questions (i.e. they’re asking the same thing).

A low value for alpha may mean that there aren’t enough questions on the test. Adding more relevant items to the test can increase alpha. Poor interrelatedness between test questions can also cause low values, so can measuring more than one latent variable.

New cards
98

Testing for homogeneity of variance

  • Levene’s test

  • Similar to normality tests

  • SPSS - Analyse → descriptions → explore → plots → normality plots with tests

New cards
99

Which type of test?

Check whether quantitative variables are related (without

hypothesizing a cause-and-effect relationship).

e.g. ”How are time spent on Instagram and money spent on clothes related?”

Correlantion

New cards
100

Which type of test?

Compare differences among group means.

e.g. “What is the difference in average screen time of Instagram users from the two groups?”

T-test

New cards

Explore top notes

note Note
studied byStudied by 15938 people
... ago
4.9(156)
note Note
studied byStudied by 1 person
... ago
5.0(1)
note Note
studied byStudied by 53 people
... ago
5.0(1)
note Note
studied byStudied by 123 people
... ago
5.0(2)
note Note
studied byStudied by 12 people
... ago
5.0(1)
note Note
studied byStudied by 44 people
... ago
5.0(2)
note Note
studied byStudied by 90 people
... ago
5.0(2)
note Note
studied byStudied by 125845 people
... ago
4.9(606)

Explore top flashcards

flashcards Flashcard (78)
studied byStudied by 7 people
... ago
5.0(1)
flashcards Flashcard (21)
studied byStudied by 4 people
... ago
5.0(1)
flashcards Flashcard (70)
studied byStudied by 36 people
... ago
5.0(1)
flashcards Flashcard (308)
studied byStudied by 7 people
... ago
5.0(1)
flashcards Flashcard (49)
studied byStudied by 174 people
... ago
5.0(3)
flashcards Flashcard (25)
studied byStudied by 5 people
... ago
5.0(1)
flashcards Flashcard (38)
studied byStudied by 12 people
... ago
5.0(1)
flashcards Flashcard (71)
studied byStudied by 2 people
... ago
5.0(1)
robot