# Week 8 Observation and Analysis

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Information about Week 8 Observation and Analysis
Education

Published on November 1, 2013

Author: jamiedavies

Source: slideshare.net

## Description

Week 8 Observation and Analysis

Complete the procedure of your observation in your observation folder. • Where did you do it? • When did you do it? • What did you do? (start with creating the coding scheme) • Who did you do it to? For full marks on a procedure the person reading it should be able to replicate what you did without asking you any questions. Have you missed anything?

Lesson Objectives By the end of the lesson you … • Must be able to describe (AO1) the observational method and its components. • Must be able to evaluate (AO2) you observation. • Should be able to identify different data types (nominal, ordinal and interval/ratio). Pg 6-8

Quantitative Data Qualitative Data

Descriptive vs. Inferential Descriptive Statistics • Summary of data to illustrate patterns and relationships – BUT can’t infer conclusions Inferential Statistics • Statistical tests that allow us to make conclusions in relation to our hypothesis. eg. Mann-Whitney or Spearman’s Rho or Chi Square.

DESCRIPTIVE Data Analysis y axis label y axis label Scattergram to show the Correlation between variable 1 and variable 2 x axis label 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 0 2 4 x axis label Titles are VERY important. Title your axis, the integers and give the graph a title. 6

100% 90% 80% 70% 60% 50% Unhealthy Healthy 40% 30% 20% 10% 0% Male Female

Levels of Measurement

Categorical Nominal - measure of central tendency: mode Data in categories (finished, fell, started) Continuous Levels of Data Interval / Ratio - measure of central tendency: mean Precise and measured using units of equal intervals (1m54s, 1m59s, 2m03s) Ordinal - measure of central tendency: median Data which are ranked or in order (1st 2nd 3rd) Ratio has a definite and meaningful zero point

20 minutes

NOMINAL DATA ORDINAL DATA INTERVAL DATA REPEATED MEASURES Sign test Wilcoxon sign test Related t test* MATCHED PAIRS Sign test Wilcoxon sign test Related t test* INDEPENDENT MEASURES Chi-squared Mann-Whitney 'U' Unrelated t test* CORRELATION Chi-squared Spearman Rho Pearson moment* TYPE DESIGN * For Parametric tests Parametric criteria must also be met.

NOMINAL DATA ORDINAL DATA INTERVAL DATA REPEATED MEASURES Sign test Wilcoxon sign test Related t test* MATCHED PAIRS Sign test Wilcoxon sign test Related t test* INDEPENDENT MEASURES Chi-squared Mann-Whitney 'U' Unrelated t test* CORRELATION Chi-squared Spearman Rho Pearson moment* TYPE DESIGN * For Parametric tests Parametric criteria must also be met.

An inferential statistical test allows us to make conclusions in relation to our hypothesis. We choose the appropriate statistical test based on the level of data that we have collected and the design of the experiment. When you conduct an inferential statistical test you will always end up with three values. • Calculated (observed) – this number is affected by the scores that you enter into the calculation and is the important number that you need to compare to the table value to ascertain if you are to accept or reject your hypothesis. • Table (critical) – this number is affected by the number or participants / number of conditions you have. Your calculated value is compared to this. • Significance level – how confident are we in the conclusion of the test.

• Must be able to describe (AO1) the observational method and its components. • Must be able to evaluate (AO2) you observation. • Should be able to identify different data types (nominal, ordinal and interval/ratio).

Finding the middle … Complete the worksheet on averages and range.

Lesson Objectives By the end of the lesson you … • Must be able to evaluate (AO2) an observation. • Must be able to carry out (AO3) an observation to collect data. • Should be able to describe (A01) P values and describe their impact on conclusions.

p ≤ 0.05 (p = probability)

Steps for testing hypotheses 1. Calculate descriptive statistics 2. Calculate an inferential statistic 3. Find its probability (p value) 4. Based on p value, accept or reject the null hypothesis 5. Draw conclusion < > ≤ ≥ less than greater than less than or equal to greater than or equal to

1. Proportion of girls categorised as early-maturers: California versus Arizona, p <0.05 2. Degree of agreement with the statement "All in all, it was worth going to war in Iraq." Republicans vs. Democrats, p=0.35 3. Rating of overall liking of movie: Film club members vs. nonclub members p = 0.173 4. Difference in reaction time between those consuming alcohol and those not, p<0.001 5. Number of lawn signs for candidates: Winner vs. loser, p=0.025 6. Degree of agreement with the statement "By law, abortion should never be permitted." Women vs. Men, p > 0.05 GREEN = SIGNIFICANT RED = NON-SIGNIFICANT

… is less than or equal to … p ≤ 0.05 The probability that the results are due to chance … … 5%

20 minutes

• Must be able to evaluate (AO2) an observation. • Must be able to carry out (AO3) an observation to collect data. • Should be able to describe (A01) P values and describe their impact on conclusions.

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