--- url: "https://questionstar.com/principles-of-survey-research/chapter-5/" title: "Chapter 5: Data Analysis: A Concise Overview of Statistical Techniques" type: "lehrbuch-chapter" chapter: 5 chapterTitle: "Data Analysis: A Concise Overview of Statistical Techniques" --- # Chapter 5: Data Analysis: A Concise Overview of Statistical Techniques *61 slides (154–214) · Principles of Survey Research.* ## Slides - [Slide 154: Data Analysis: A Concise Overview of Statistical Techniques](slide-154.html.md) — 5.1 Descriptive Statistics: Organizing and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 155: Types of Statistical Data Analysis](slide-155.html.md) — Summarizes the observations from the sample and presents them clearly. - [Slide 156: Data Analysis](slide-156.html.md) — 5.1 Descriptive Statistics: Organizing and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 157: Data Analysis](slide-157.html.md) — 5.1 Descriptive Statistics: Organizing and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 158: Frequencies and Relative Frequencies](slide-158.html.md) — Frequency distribution indicates, for each value, how often it occurs in the data. - [Slide 159: Bar Graph](slide-159.html.md) — Bar height = frequency or relative frequency - [Slide 160: Pie Chart](slide-160.html.md) — Should always show relative frequencies. - [Slide 161: Data Analysis](slide-161.html.md) — 5.1 Descriptive Statistics: Organizing and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 162: Tables](slide-162.html.md) — Discrete Variable is a quantitative variable that has either a finite number of values or an infinitely countable number of values (e.g. 0, 1, 2, 3, …). - [Slide 163: Tables and Histograms](slide-163.html.md) — From the Table to the Histogram - [Slide 164: Histogram](slide-164.html.md) — A histogram graphically depicts a grouped frequency distribution. - [Slide 165: Frequency Polygon](slide-165.html.md) — Mark the midpoint at the top of each bar of the histogram. - [Slide 166: Cumulative Tables and Ogives](slide-166.html.md) — shows the sum of the frequencies up to and including the respective row. - [Slide 167: Data Analysis](slide-167.html.md) — 5.1 Descriptive Statistics: Organizing and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 168: Measures of Central Tendency](slide-168.html.md) — Easy to compute: just sum up and divide. - [Slide 169: Measures of Central Tendency](slide-169.html.md) — Handles outliers well – often the most accurate depiction of a group. - [Slide 170: Measures of Central Tendency](slide-170.html.md) — Good for exclusive choices (this one or the other; no compromises) – works with nominal data. - [Slide 171: Measures of Central Tendency: Using Mean and Median to Identify the Distribution Shape](slide-171.html.md) — Measures of Central Tendency - [Slide 172: Measures of Dispersion](slide-172.html.md) — Measures of Dispersion - [Slide 173: Measures of Dispersion](slide-173.html.md) — The mean acts like a balance point – the average deviation from the mean is always zero. - [Slide 174: Measures of Dispersion](slide-174.html.md) — Standard deviation keeps the units of measurement of the original data. - [Slide 175: Relationship between the Standard Deviation and the Shape of the Normal Distribution](slide-175.html.md) — Measures of Dispersion - [Slide 176: Data Analysis](slide-176.html.md) — 5.1 Descriptive Statistics: Organizing and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 177: Cross-Tabulations](slide-177.html.md) — Cross-tabulations summarize the joint distribution of two (or more) discrete variables in a table. - [Slide 178: Cross-Tabulations](slide-178.html.md) — Example: two variables - [Slide 179: Cross-Tabulations](slide-179.html.md) — The third variable - [Slide 180: Cross-Tabulations](slide-180.html.md) — Case 1 · the third variable - [Slide 181: Cross-Tabulations](slide-181.html.md) — Case 2 · the third variable - [Slide 182: Cross-Tabulations](slide-182.html.md) — Case 3 · the third variable - [Slide 183: Data Analysis](slide-183.html.md) — 5.1 Descriptive Statistics: Displaying and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 184: Data Analysis](slide-184.html.md) — 5.1 Descriptive Statistics: Displaying and Presenting Data · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3 Summarizing… - [Slide 185: Hypothesis Testing](slide-185.html.md) — A five-step procedure that, based on a sample and using probability theory, determines whether a hypothesis is sufficiently supported. - [Slide 186: Hypothesis Testing](slide-186.html.md) — Starting example - [Slide 187: Hypothesis Testing](slide-187.html.md) — Null hypothesis (H₀) is a claim of the status quo — that there is no difference or no effect. - [Slide 188: Hypothesis Testing](slide-188.html.md) — Significance (α) — probability that a true null hypothesis is rejected. - [Slide 189: Hypothesis Testing](slide-189.html.md) — Analogy: innocence in a criminal trial. H₀: The defendant is innocent. - [Slide 190: Hypothesis Testing](slide-190.html.md) — Analogy: a rustling in the bushes — is that a lion? H₀: There is no lion in the bushes. - [Slide 191: Hypothesis Testing](slide-191.html.md) — Significance (α) — the probability that a true null hypothesis is rejected. - [Slide 192: Hypothesis Testing](slide-192.html.md) — Our example is about the distribution of non-metric variables (rare/frequent internet use; men/women) in one sample. - [Slide 193: Hypothesis Testing](slide-193.html.md) — One sample · distribution · non-metric → the χ² test for goodness of fit. - [Slide 194: Hypothesis Testing](slide-194.html.md) — The χ²test statistic (chi-square) tests the statistical significance of the relationship observed in a crosstab. - [Slide 195: Hypothesis Testing](slide-195.html.md) — Step 3 · expected frequencies - [Slide 196: Hypothesis Testing](slide-196.html.md) — χ² should always be computed using only absolute frequencies. If the data are given in percentages (relative frequencies), they must first be converted… - [Slide 197: Hypothesis Testing](slide-197.html.md) — TScal — the observed (calculated) value of the test statistic. - [Slide 198: Hypothesis Testing](slide-198.html.md) — Step 4 · Comparison - [Slide 199: Hypothesis Testing](slide-199.html.md) — Step 5 · Decision - [Slide 200: Hypothesis Testing](slide-200.html.md) — If the sample was carefully selected and drawn, we can claim with 95% confidence that there is no such relationship. - [Slide 201: Data Analysis](slide-201.html.md) — 5.1 Descriptive Statistics: A Concise Overview of Statistical Techniques · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3… - [Slide 202: Testing the Strength of a Relationship](slide-202.html.md) — χ² tests only the significance of a relationship and says nothing about its strength. - [Slide 203: Phi Coefficient](slide-203.html.md) — The higher φ, the stronger the relationship between the variables. - [Slide 204: Contingency Coefficient](slide-204.html.md) — The higher C, the stronger the relationship between the variables. - [Slide 205: Cramer's V](slide-205.html.md) — The higher V, the stronger the relationship between the variables. - [Slide 206: Lambda Coefficient](slide-206.html.md) — Indicates the extent to which knowing the value of one variable helps in predicting the other variable. - [Slide 207: Lambda Coefficient](slide-207.html.md) — Sum of the maximum frequencies of all columns - [Slide 208: Data Analysis](slide-208.html.md) — 5.1 Descriptive Statistics: A Concise Overview of Statistical Techniques · 5.1.1 Organizing Qualitative Data · 5.1.2 Organizing Quantitative Data · 5.1.3… - [Slide 209: Types of Relationships between Two Variables](slide-209.html.md) — Unless the data come from a controlled experiment, we can only claim the existence of a relationship between the variables — but not the causal direction… - [Slide 210: Linear Correlation](slide-210.html.md) — Two variables correlate positively when higher values of one variable correspond to higher values of the other variable. - [Slide 211: Linear Correlation Coefficient](slide-211.html.md) — The (Pearson) linear correlation coefficient measures the strength of the linear relationship between two variables. - [Slide 212: Linear Correlation Coefficient](slide-212.html.md) — Worked Example - [Slide 213: Regression Analysis](slide-213.html.md) — The regression analysis is a powerful and flexible tool for analyzing associative relationships between a metric dependent variable and one or more… - [Slide 214: Regression Analysis](slide-214.html.md) — How many product units will we sell if we spend €85.000 on advertising? --- *Part of [Principles of Survey Research](../index.md) · Dr. Paul Marx*