Research: Module 6 COPY Flashcards

(59 cards)

1
Q

parametric measures

A

require data that follows a specific distribution (typically normal) and assume equal variances

used for continuous data

examples: t-tests, ANOVA, Pearson r

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

nonparametric measures

A

make fewer or no assumptions about the data distribution (not normally distributed)

used for both continuous and categorical data, small sample sizes

examples: Spearman’s

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

continuous vs. categorical data

A

continuous: interval/ratio

categorical: ordinal/ranked

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

T-tests

A

a statistical method used to determine if there is a significant difference between means of 2 groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

3 basic types of t-tests

A

comparing a sample mean to a population mean (we are not doing this)

comparing the means of 2 independent samples

comparing the means of paired dependent samples (same subject repeated testing)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

independent t-test

A

used when comparing 2 unrelated groups

look for differences between 2 separate, unrelated groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

dependent t-test/paired t-test

A

used when comparing related groups, often measurements from the same subjects at different times

look for changes within the same subjects or matched pairs

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

what type of t-test would you run for the following scenario: comparing test scores of students in 2 different classes

A

independent t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what type of t-test would you run for the following scenario: measuring the same students’ performance on test before and after a training program

A

dependent t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what type of t-test would you run for the following scenario: comparing the blood pressure of 2 different groups

A

independent t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

what type of t-test would you run for the following scenario: comparing a person’s blood pressure before and after taking medication

A

dependent t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

one tail tests

A

looks for a difference in a specific direction (either greater than or less than)

use if you have a clear explanation about the direction of the effect

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

two tail test

A

looks for any difference (either greater than or less than)

use when under about the direction or effect or if you are interested in ANY difference

“there is going to be a difference PERIOD”

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

critical region

A

In one-tailed tests, the critical region lies entirely on one side of the probability distribution — either the left tail or the right tail — depending on your alternative hypothesis.

The critical region is where the test statistic must fall for you to reject the null hypothesis (H₀) in favor of the alternative hypothesis (H₁)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

pros and cons to a one-tail test

A

pros: can be more powerful (require smaller sample size) to detect an effect in the predicted direction

cons: may miss significant effects if the actual effect is in the opposite direction

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

pros and cons to a two-tail test

A

pros: more versatile and can detect effects in either direction, in any difference

cons: requires a larger sample size to achieve the same level of significance compared to a one-tail test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

would you use a one tail or two tail test with this hypothesis: Recovery time is less than the current average recovery time

A

one tail test

-specifies a direction of the effect (greater than/less than)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

would you use a one tail or two tail test with this hypothesis: the new method’s scores are different from the old method’s score

A

two tail test

-simply states there is a difference, not if it will be greater than/less than

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

type 1 error

A

rejecting the null hypothesis when it is actually true (false positive)

concluding there is a difference when there is NOT a difference

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

type 2 error

A

failing to reject the null hypothesis when it is actually false (false negative)

concluding there is not a difference between groups when there IS a difference

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

power

A

the probability that a hypothesis will correctly reject a false null hypothesis
(probability that your test will detect a real difference if one actually exists.)

probability of avoiding a type 2 error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

larger sample sizes → ______ power

A

higher power

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

larger effect size → ________ power

A

higher power

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

higher alpha → _______ power

A

increases power AND risk of type 1 error

A higher alpha means you’re being less strict, so you’re more likely to reject the null hypothesis — including in cases when you shouldn’t.

25
lower variability → _________ power
higher lower variability = less spread out data
26
a study with a high power is more likely to detect...
a real effect if it exists
27
a study with a low power may lead to...
a false negative because it may fail to detect a real effect
28
allocation ratio
the ratio of the sample sizes between different groups in a study, typically used when comparing **2+ groups** it indicated how many participants are assigned/allocated to each group relative to each other
29
allocation ratio of 1:1
means **equal** group sizes number of participants in each group is the same
30
allocation ratio of 1:2
means **unequal** group sizes ex: group 1 has 50 participants, group 2 has 100
31
what is the effect of an unequal allocation ratio on sample size
unequal allocation ratio requires a **larger total sample size** compared to an equal allocation ratio to maintain the same level of power
32
correlation tests
tests that analyze a **relationship** they can identify relationships between variables, but **they DO NOT establish cause/effect** like experimental studies do
33
what are the variables of a correlation test
predictor variable criterion variable
34
predictor variable
the variable from which predictions are made similar to the IV in an experimental study, but it is NOT manipulated by the researcher
35
criterion variable
the variable being predicted or explained by the predictor variable similar to the DV in an experimental study
36
what are the predictor and criterion variables in the following scenario: a study that examines the relationship between hours of study and exam scores
predictor: hours of study criterion: exam scores
37
correlational research demonstrates the ________, _________, and __________ of a relationship.
existence, strength, and direction
38
correlational research does NOT demonstrate ___________.
cause and effect relationship
39
ANOVA
analysis of variance test used to determine if there is a statistical difference between the **means of 3+ groups**
40
F-statistic
determines whether the variation between sample means is significant or not
41
2 types of ANOVAs
one way and two way
42
one way ANOVA
used when you have **1 IV** (a factor with multiple levels) and **1 DV** it examines whether the means of the DV differ significantly across the different levels of the IV
43
two way ANOVA
used when you have **2 IV's** and **1 DV** allows you to assess the individual effects of each IV and also the combined effect of both variables on the DV **(interaction effect)**
44
would you use a one way or two way ANOVA for the following scenario: comparing the average test scores of students taught using 3 different teaching methods
one way ANOVA one IV with 3 levels
45
would you use a one way or two way ANOVA for the following scenario: examining the impact of both advertising spend and product placement on sales revenue
2 way ANOVA 2 IV's
46
MANOVA
**purpose:** to examine the relationship between multiple IVs and **multiple DVs** use when you have **2+ categorical IVs** and **2+ continuous DVs** tests whether there is a significant differences between the means of 2+ groups (IVs) on a combination of DVs
47
multiple regression
**Purpose:** to examine the relationship between multiple IVs and a **single DV** use when you have **2+ continuous OR categorical IVs** and **1 continuous DV** models the relationship between the IVs and DV using a linear equation
48
what are the assumptions of multiple regression
linearity, independence of errors, homoscedasaticity, and formality of residuals
49
would you use a MANOVA or multiple regression for the following scenario: studying if there are differences in math and science tests scores between male and female students who have different levels of access to technology
MANOVA 2 DVs: math and science test scores 2 IVs: male/female students, different access to technology
50
would you use a MANOVA or multiple regression for the following scenario: studying how age, income, and education level affect a person's savings
multiple regression 3 IVs 1 DV
51
regression
refers to a set of techniques used to model and analyze the relationship between a DV and 1+ IVs aims to find the **"best-line"** or curve that represents the relationship between these variables, **allowing for predictions** or estimates of the DV based on the IV
52
regression types
linear multiple logistic
53
linear regression
assumes linear relationship between variables and uses a straight line to model the relationship
54
multiple regression
extends linear regression to include **multiple IVs**
55
logistic regression
used when DV is categorical (e.g yes/no) and **predicts probability** of an event occurring
56
outcome/response variable
the variable being predicted or whose relationship is being studied (I.e. DV)
57
predictors, features, covariates
the variables used to predict or explain the DV (I.e. IV)
58
regression line/curve
the line/curve that best represents the relationship between the variables, minimizing the difference between predicted and actual values
59
predictive modeling
forecasting future values of the DV based on the IV