Ch.13 Analyzing Data

Inferential Statistics

Statistics that generalize findings from a sample to a population

Inferential Statistics Include:

Chi square

Pearson correlation

Comparing means

T-test: independent vs. dependent

ANOVA: one-way and two-way

Two Types of Inferential Statistics

Parameter Estimation

Hypothesis Testing

Two Types of Hypothesis Testing

Parametric

Nonparametric

Parametric procedure

Require assumptions to be met for statistical findings to be valid

Nonparametric Procedure

Make no assumptions about the shape of the distribution and are referred to as distribution-free tests

Correlation

Measure that defines the relationship b/w two variables

Pearson Correlation

You have one group; you are comparing variables within that one/sample group YOU ARE NOT COMPARING 2 GROUPS

Ex.

Minutes of exercise vs. weight loss

Hours of study vs. GPA

Number of pages read vs. Exam Score

How is T-test and Person Correlation Different?

In t-test you have 2 groups; and you are comparing a variable b/w 2 groups.

T-Test

A popular parametric procedure for assessing whether two group means are significantly different from one another

Independent T-test

When you have an IV with a nominal data but with no more than 2 groups and your DV is a continuous data

e.g. weight loss, A1C levels

EXAMPLE

IV: 2 groups

Group 1: internal mammary as graft

Group2: saphenous vein as a graft

DV: post operative pain

Dependent T-Test (Paired t-test)

Appropriate test for situations in which scores in the first group can be paired with a score in the second group

Chi-Square

Nonparametric procedure used to assess whether a relationship exists b/w two nominal-level variables; symbolized as X²

Analysis of Variance (ANOVA)

Inferential procedure used to determine whether there is a significant difference among three or more group means

1-way ANOVA

One independent variable with several levels

Ex: Variable “nursing specialty”

Three levels: pediatric nursing, community-health nursing, and surgical nursing

2-way ANOVA

Represents two independent variables with several levels

Descriptive Statistics

Measures of central tendency: means, median, mode

Measures of dispersion: range, standard deviation, variance

Calculate: mean, median, mode

Mean: adding all scores in a distribution and dividing total by the number of scores

Median: Arranging scores in rank order. If there is an odd number of scores, the median is the middle score.

Mode: Frequently occurring number

Measures of dispersion

Range: subtracting lowest score in the distribution from the highest score

Variance: sum of the squared deviations divided by the number of scores

Standard Deviation: square root of the variance

Skewness Figure

Positive: the tail is to the right

Negative: the tail is to the left

Mode: is always at the highest point

In a non-normal distribution:

The mean is always nearest the tail

The median is always between the mode and the mean

Descriptive statistics vs Inferential statistics

Descriptive statistics are used to summarize measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation)

Inferential statistics focus on determining how likely it is that results based on a sample are the same as those that would be obtained for the population.

Meta Analysis vs Secondary Analysis