Appendix A & Chapter 1- Introduction Flashcards Preview

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Flashcards in Appendix A & Chapter 1- Introduction Deck (21):
1

Descriptive Statistic

Produces a number or a figure that summarizes or describes a set of data

2

Inferential Statistic

A method that uses sample evidence and probability to reach conclusions about unmeasurable populations

3

Population

All measurements of a specified group
Ex: If you were to have a study on Marian students, and you literally asked each and everyone of the Marian students. There are slim to none studies that are like this though.

4

Sample

A subset of a population, it may or may not be representative
Ex: If you were doing a study on college students in general and you used Marian students as a sample. They are a small amount of the overall picture of what you are trying to measure

5

Parameter

A numerical or nominal characteristic of a population
So if you have a study using population you took/have parameters of them

6

Statistic

A numerical or nominal characteristic of a sample
If you have a study using a sample you took statistics of them

7

Variable

Something that exists in more than one amount or in more than one form
Example: Height, IQ, dosage, gender, religion, income

8

What are the two different types of variables?

Quantitative and qualitative

9

Quantitative Variable

Variable whose levels indicate different amounts. The word quantity is in it. Has a numerical value.

10

Qualitative Variable

Variable whose levels are different kinds, not different amounts
Example: 1st, 2nd, 3rd doesn’t tell you how far apart, or by how much they are ranked. It’s qualitative

11

What are the different scales of measurement?

Nominal, ordinal, interval, ratio

12

Nominal

A scale in which numbers serve only as labels and do not indicate any quantitative relationship. If you have nominal data you are counting. You count the frequency of ‘oh, how many times do I have this number’. How many fives do I have, how many twos, or threes? You’re counting
Example: football uniform numbers, social security number, phone number

13

Ordinal

Scale in which numbers are ranks; equal differences between numbers do not represent equal differences between the things measured. Ordinal gives you order.
Example: Rank orders in a race; 1st, 2nd, 3rd place

14

Interval

Scale in which equal differences between numbers represent equal differences in the thing measured. The zero point is arbitrarily defined.
There’s not a legit zero defined.
I think celsius is ratio though??? Not sure…??
Example: Fahrenheit Temperature

15

Ratio

Scale with characteristics of interval scale; also, zero means that none of the things measured is present. A lot of psycho social studies do not have zeros, because, for example, if we measured self-esteem no one has a complete zero self-esteem, we all have something. This is not ratio.
Example: Weight, income, dosage

16

What is the only difference between interval and ratio data?

Ratio has a zero

17

What are the three different kinds of variables?

Independent, dependent, extraneous

18

Independent Variable

Variable controlled by the researcher; changes in this variable may produce changes in the dependent variable. What I assume has an effect on the dependent variable. Understand the direction of your experiment.

19

Dependent Variable

The observed variable that is expected to change as a result of changes in the independent variable in an experiment. What you are measuring, you don’t know what is going to happen with it.
Have to be careful with this one, because there may be things that have or are changing, but we need to make sure they meet the qualifications specified above.
What you assume is getting affected by the independent variable.

20

Extraneous/Confounding Variable

Variable other than the independent variable that may affect the dependent variable
Stuff that gets in the way of you have completely accurate results. Things that affect your data that you don’t want to, because they are controlled. Crappy designs have extraneous variables.

21

Vodka and Learning Example; Use all variables

You assume vodka has an effect on learning. Vodka is the independent variable. It changes and effects how you learn. Your learning is getting measured. But you can’t accurately predict what is going to happen. You could test people with completely different tolerances. And get completely different results so the data is messed up, main. Or if you get smart/dumb people grouped together on accident. One group does really well, because they’re smart not because you gave them three shots of vodka. And the other group who was given no alcohol does terrible, but it’s just because they’re all dumb.