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Chapter 24 Designing a Quantitative Analysis Strategy: From Data Collection to Interpretation

This chapter discusses the important steps involved in designing a quantitative analysis strategy, including data collection, coding, pre-analysis, preliminary assessments, and principal analyses. It also covers data transformation, interpreting the results, and considering aspects such as credibility, meaning, importance, generalizability, and implications.

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Chapter 24 Designing a Quantitative Analysis Strategy: From Data Collection to Interpretation

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  1. Chapter 24Designing a Quantitative Analysis Strategy: From Data Collection to Interpretation

  2. J. Wakim’s Comments • I will inject slides into this program to make comments regarding each slide. Sometimes I will put them on the Polit-prepared slide. I meant to do this when your data collection was presented.

  3. J. Wakim’s Comments A researcher should start with a pre-data collection phase, before the pre-analysis phase • First, you should select the data analysis program you intend to use – such as SPSS or Excell – and become familiar with it. • Second, if you are using a paper and pencil instrument – whether yours or one that is already in existence – assign numbers to answers before you collect the data. Look at Polit, p. 644 the paragraph starting with “ Respondents through 2) signifying ‘no.’” It’s much easier fo the respondent to fill these in than for you to have to correct them later. • Third, make up your tables, putting in the variables so you have them ready when you collect your data.

  4. Coding Data • The process of transforming data into symbols compatible with computer analysis—usually numeric values • Types of coding: • Inherently quantitative variables (e.g., weight) • Precoded data (e.g., yes/no) • Uncategorized data (e.g., open-ended questions) • Missing values (e.g., refusals, don’t knows – on a Likert scale would be the coded as the middle number. On another instrument, you could give it a separate number

  5. Codebooks • Lists each variable in the data set – in Chris and Jessi’s data, there was one question that had four sections to it, so each section had to have a variable – this should be documented . • Includes information about variable’s placement in the file, its codes, and other basic information • Critical in documenting decisions about the data set

  6. Phase 1: Preanalysis Phase • Log in, check, edit raw data • Select computer software - do beforehand • Code data – create a file and its variables before collecting • Enter data into your prepared computer file • Inspect data for outliers – decimals in wrong places, a #3 in the 1= male and 2= female category – and clean the data • Save the data file for analysis

  7. Coding Data • The process of transforming data into symbols compatible with computer analysis—usually numeric values • Types of coding: • Inherently quantitative variables (e.g., weight) • Precoded data (e.g., yes/no) • Uncategorized data (e.g., open-ended questions) • Missing values (e.g., refusals, don’t knows – on a Likert scale would be the coded as the middle number. On another instrument, you could give it a separate number

  8. Phase 2: Preliminary Assessments • Assess missing values problems – this is usually taken care of by the computer program such as SPSS – it gives you all data, then “valid data” in the printouts (see the next slide) • Assess data quality – if too many do not answer – delete the variable, or substitute the mean or median value • Assess biases – compare the groups according to characteristics that might influence the results. • Assess assumptions for inferential statistics – the number of participants and whether a normal distribution can be assumed

  9. Phase 3: Preliminary Actions • Perform transformations and recodes – similar to what you did when you changed the Cronk data to older than 25 and younger than 25. • Address missing values problems • Construct scales, composite indexes • Perform other peripheral analyses

  10. Performing item reversals Constructing scales Performing counts Recoding variables Meeting statistical assumptions Creating dummy variables Data Transformation in Preparation for Analysis

  11. Phase 4: Principal Analyses • Perform descriptive statistical analyses – always first in results section • Perform bivariate statistical analyses – be sure to give the value you are using for statistical significance – usually p<.05. • Perform multivariate analyses • Perform needed post hoc tests – If F (in ANOVA) is significant, you want to know between which groups (levels) the significance lies

  12. Phase 5: Interpretive Phase • Integrate and synthesize analyses • Perform supplementary interpretive analyses (e.g., power analysis) – If this was not done prior to selecting the number of participants needed for both the control and the experimental group

  13. Interpretive Task Involves Consideration of Five Aspects of the Results • Credibility • Meaning • Importance • Generalizability • Implications

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