1 / 43

Design and Data Analysis in Psychology I English group (A)

Design and Data Analysis in Psychology I English group (A). School of Psychology Dpt. Experimental Psychology. Susana Sanduvete Chaves Salvador Chacón Moscoso Milagrosa Sánchez Martín. Study of one variable: frequency distributions and graphic representations. Lesson 2.

perkinsk
Download Presentation

Design and Data Analysis in Psychology I English group (A)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Design and Data Analysis in Psychology IEnglish group (A) School of PsychologyDpt. Experimental Psychology Susana Sanduvete Chaves Salvador Chacón Moscoso Milagrosa Sánchez Martín

  2. Study of one variable: frequency distributions and graphic representations Lesson 2 Design and Data Analysis in Psychology I

  3. 1. Introduction • Two basic descriptive strategies are used in order to represent a large number of data and provide information about them: • Frequency tables. • Graphic representations. • They are different depending on the type of variable.

  4. 1. Introduction QUALITATIVE OR NOMINAL ORDINAL (QUASI-QUANTITATIVE) DISCRETE QUANTITATIVE CONTINUE QUANTITATIVE

  5. 1. Introduction • The election of the type of variable depends on the information you have; e.g. variable: alcoholism. • Qualitative (2 categories): Abstemious/Drinker • Ordinal: Abstemious/Moderate drinker/Heavy drinker • Quantitative discrete: Number of drinks and cocktails drunk per week. • Quantitative continuous: Liters of alcohol ingested per week. • The more concrete the information is, the better.

  6. 2. Qualitative variables (nominal or categorical) Example: What is your marital status? Single 1 Marital Status Married 2 Widow/er 3

  7. 2. Qualitative variables (nominal or categorical) • Depending on the number of values, the qualitative variables are: • Binary, binomial or dichotomous. • Polytomous or multinomial.

  8. 2. Qualitative variables (nominal or categorical) Xi : Are the different values of the variable “Marital status": Single, Married and Widowed • Sex: Male / Female • Gender: Male / Female • Smoking: Yes / No • Sex relation: Yes / No • Media: Radio / TV / Press • Decriminalization of abortion: Against / Indifferent / In favor • Sick: Psychotic / Neurotic / Organic • Political opinion: Center / Left / Right • Skills: Ambidextrous / Left / Right • Political parties: PP / IU / PSOE / PA • Type of Drug: Cocaine / Marijuana / Heroin

  9. 2. Qualitative variables (nominal or categorical) Marital status

  10. 2. Qualitative variables (nominal or categorical) • Frequency distribution: • Definition: table with data in which are represented at least: • The values of the variable (Xi). • The frequencies of these values (fi). • The values of the variable should be: • Clearly defined. • Mutually exclusive. • Exhaustive.

  11. 2. Qualitative variables (nominal or categorical) Different values of the variable“marital status” Number of times that each value appears S 18 M 9 W 3

  12. 2. Qualitative variables a) Absolute frequency: fi fi Number of subjects that belong to each category (f1 , f2 , f3) fi f1 + f2 + f3 = n n Total number of subjects

  13. 2. Qualitative variables (nominal or categorical)

  14. 2. Qualitative variables (nominal or categorical) • b) Relative frequency or proportion: rfi • Definition: it is the ratio of the absolute frequency and n. • It is useful to understand the importance that a value has in the group: • Example:

  15. 2. Qualitative variables (nominal or categorical) Properties: ∑ rfi = rf1 + rf2 + rf3 = 1 1ª 0 ≤ rfi≤ 1 2ª

  16. 2. Qualitative variables (nominal or categorical) Example: 0.6 0. 3 0.1 1

  17. 2. Qualitative variables (nominal or categorical) • c) Percentages: %i • The percentage is equal to the relative frequency multiplied by 100 %i= rfi x 100 x 100

  18. 2. Qualitative variables (nominal or categorical) Properties: ∑ %i = %1 + %2 + %3 = 100% 1ª 0 ≤ %i≤ 100 2ª 60 30 10 100

  19. 2. Qualitative variables (nominal or categorical) • Graphic representation:Bar chart More frequency, more bar height ORDINATE ABSCISSA

  20. 2. Qualitative variables (nominal or categorical) Pie chart

  21. 3. Ordinal variables (quasi-quantitatives) Example: Degree of Responsibility Greatest 5 High 4 Medium 3 Low 2 1 None

  22. 3. Ordinal variables • Socioeconomic status: High / Medium / Low • Level of agreement: Poor / Fair / Moderate / Substantial / Excellent • Responsibility level: Very High / High / Medium / Low / Very Low • Social class: High / Medium / Low • Skill level: High / Medium / Low • Concentration ability: Poor / Fair / Moderate / Substantial / Excellent • Degree of emotional distress: Very High / High / Medium / Low / Very Low • Income Level: High / Medium / Low

  23. 3. Ordinal variables Degree of responsibility

  24. 3. Ordinal variables • Frequency table:

  25. 3. Ordinal variables d) Cumulative frequency: Fi F1 = f1 F2 = F1 + f2 F3 = F2 + f3 F4 = F3 + f4 F5 = F4 + f5

  26. 3. Ordinal variables F1 = f1 = 6 F2 = F1 + f2 = 6 + 10 = 16 F3 = F2 + f3 = 16 + 48 = 64 F4 = F3 + f4 = 64 + 12 = 76 F5 = F4 + f5 = 76 + 4 = 80 An alternative F1 = f1 F2 = f1 + f2 F3 = f1 + f2 + f3 F4 = f1 + f2 + f3 + f4 F5 = f1 + f2 + f3 + f4 + f5

  27. 3. Ordinal variables e) Cumulative relative frequency (or cumulative proportion): CRFi Definition: ratio of cumulative frequency of a value Xi and the total frequency CRF1 = rf1 CRF2 = CRF1 + rf2 CRF3 = CRF2 + rf3 CRF4 = CRF3 + rf4 CRF5 = CRF4 + rf5

  28. 3. Ordinal variables CRF1 = rf1 = 0.075 CRF2 = CRF1 + rf2 = 0.075 + 0.125 = 0.20 CRF3 = CRF2 + rf3 = 0.20 + 0.60 = 0.80 CRF4 = CRF3 + rf4 = 0.80 + 0.15 = 0.95 CRF5 = CRF4 + rf5 = 0.95 + 0.05 = 1

  29. 3. Ordinal variables f) Cumulative percentages: ci% c1% = %1 c2% = c1% + %2 c3% = c2% + %3 c4% = c3% + %4 c5% = c4% + %5

  30. 3. Ordinal variables c1% = %1 = 7.5% c2% = c1% + %2 = 7.5 + 12.5 = 20% c3% = c2% + %3 = 20 + 60 = 80% c4% = c3% + %4 = 80 + 15 = 95% c5% = c4% + %5 = 95 + 5 = 100%

  31. 3. Ordinal variables • Graphic representation: • Bar chart

  32. 3. Ordinal variables Cumulative bar chart

  33. 4. Discrete quantitative variable Example: number of children of families from Seville 20 couples are selected from the city of Seville and asked about the number of children they have. Their answers are: 2, 1, 0, 3, 2, 2, 3, 1, 1, 0, 1, 2, 1, 2, 0, 2, 4, 2, 3, 1

  34. 4. Discrete quantitative variable • Frequency table:

  35. 4. Discrete quantitative variable • Graphic representation: • Bar chart

  36. 4. Discrete quantitative variable Cumulative bar chart

  37. 5. Continuous quantitative variable • Example: Attention test scores (16 participants) Interpretation: 22 represents all the values from 21.5 to 22.5; 18 represents all the values from 17.5 to 18.5.

  38. 5. Continuous quantitative variable • Frequency table:

  39. 5. Continuous quantitative variable • Graphic • representation: • Histogram

  40. 5. Continuous quantitative variable Frequency polygon

  41. 5. Continuous quantitative variable with intervals • When there are numerous different values of the variable, the strategy to follow is to join these values in intervals. • Basic steps to create intervals: • Range: max-min (exact limits). • Exact limits= value ± 0.5 x measurement unit • 2. Estimate the number of intervals: • 3. Interval amplitude: I=range/

  42. 5. Continuous quantitative variable with intervals • Example: Attention test scores • Range: max-min (exact limits) = 25.5 – 15.5 = 10 • Upper exact limit = 25 + 0.5 x 1 Lower exact limit = 16 - 0.5 x 1 • 2. Estimate the number of intervals: • 3. Interval amplitude: I=10/4 = 2.5 ≈ 3

  43. 5. Continuous quantitative variable • Frequency table:

More Related