Psychology experiment report Lab #1

Java Python 

Lab #1 (6  pages): MAKE SURE TO USE LAB GUIDE and APA RESOURCES. MAINTAIN FORMAL WRITING STYLE, RESEARCH FOCUSED, THIRD PERSON PERSPECTIVE, NO PERSONAL PRONOUNS. CITE EVERYTHING WITH CORRECT IN TEXT CITAITONS AND PROOF READ!!!

Cover Page and Abstract

Intro (aprox 1.5 pgs)

Heading is Title of Your Paper

1. General background - broad overview of research on this topic (Body image concerns) (KEEP IT RESEARCH FOCUSED) THEORY – gender differences in body image concerns. A majority of the research (historically) focused on body fat, the outcomes, what this lead to. More current research (McCreary, Saucier, & Courtenay, 2005) is challenging this.

2. Previous Research – specific examples – 2 sources used in the article (be careful of correctly citing), McCreary et al. (2005) article in detail - theory, what they were looking for and what they found (RESULTS: 4 important findings 1) men score higher on DMS regardless of GBV. 2) 3 Masculine Measures are related to DMS 3) Feminine traits are not related to lower DMS 4) No Differential Salience *Final discussion (showing you know the article and understanding it)

3. Introduce Current Study - Why we’re doing it etc. to address limitations of McCreary et al. (2005), different population and measures. Differences between current study and McCreary et al. (2005) – for body image measure – BSQ – Drive for Muscularity Scale (DMS) and BSQS ***current study is using measures geared toward body fat and muscularity together. For gender - personality measure BSRI (different measure, exploring different aspect of gender role socialization).  

· Research Questions (not bullets, paragraph):

· Is there a difference between men and women in body image between concern with muscle mass and concern with body fat/body shape? If so what is it? Hyp – men are going to score higher on DMS women higher on BSQS (based upon McCrery’s theory and findings).

· What are the relationships between masculinity/femininity and drive for muscularity/body shape concerns?  Is masculinity correlated with drive for muscularity? Hyp –Yes positive correlation between masculinity and DFM (theory and prior research). For DFM and FEM McCreary theorized a relationship but didn’t find it so the current study wants to replicate and confirm or disprove original findings. Also investigating correlations for BSQS. 

Method section: (aprox 2 pgs)

Participants: A total of xxx participants completed surveys. There were xxx (%) men and xxx (%) women between the ages of xx and xx with the average age of xx years old. (Add other demographic information; percent of population below xx years old, report ALL sexual orientation, ALL class and ethnicity).

Procedure:

How you did it, with detail (Use guide for writing a lab report)…

Measures:

Sentences Explaining Our Measures: 1 survey – 2 measures BSQ consists of DMS and BSQS and Rate Your Traits = BSRI consists of MAS FEM and AND scales

Describe Scales:

Drive for Muscularity Scale (DMS). Use your article as a guide to describe this scale (how many items, what its measuring, how its scored, WE DID NOT REVERSE CODE, sample questions, validity); and then our specific results for the alphas – overall alpha = XXX

Body Shape Questionnaire Scale (BSQS). Do same for BSQS as for DMS (describe it in the same dai 写Psychology experiment report Lab #1 way as article did for DMS; use BSQ article to help); overall alpha = XXX

Bem Sex Role Inventory (BSRI). Do same (Use BSRI article) – measure for masculinity; femininity; androgyny; alpha for MAS = XXX and for FEM = XXX. Androgynous scale wasn’t used for analysis.  

Results (aprox 1.5-2 pgs)

Intro paragraph: explain analysis

Table 1. Means and Standard Deviations for the DMS and BSRI

Scale

Men (n = xxx)

Women (n = xxx )

M

SD

M

SD

DMS

xx

xx

xx

xx

BSQS

xx

xx

xx

xx

BSRI

Masculinity

xx

xx

xx

xx

Femininity

xx

xx

xx

xx

Explain Chart: descriptive statistics         

### 心理学数据集 Distill Psychology-10K-R1 的概述 Distill Psychology-10K-R1 是一个专注于心理学领域的大规模数据集,其设计目标是为了支持心理健康研究以及自然语言处理模型在心理分析中的应用。该数据集包含了约 10,000 条经过精心筛选的心理健康相关对话记录[^1]。 以下是关于此数据集的一些重要细节: #### 数据集路径与配置 在给定的 `config.py` 文件中,数据集的位置被定义如下: ```python DATASET_PATH = os.path.expanduser("/home/idal-01/wxc/deepseek/health_dataset/distill_psychology-10k-r1.json") ``` 这表明数据存储在一个 JSON 文件中,具体位置为 `/home/idal-01/wxc/deepseek/health_dataset/` 下的 `distill_psychology-10k-r1.json` 文件。 #### 训练配置 训练过程中使用的模型名称及其参数通过以下字典指定: ```python TRAIN_CONFIG = { "model_name": "/home/idal-01/wxc/deepseek/deepseek_1.5B" } ``` 这里指定了用于加载预训练权重的基础模型路径,即 DeepSeek 提供的一个具有 1.5B 参数的语言模型。 #### 数据集特点 Distill Psychology-10K-R1 主要具备以下几个特性: - **高质量标注**:每条记录均经过专业人士审核并标记,确保数据质量。 - **多样化场景覆盖**:涵盖了多种心理健康主题,包括焦虑、抑郁和其他常见情绪障碍。 - **隐私保护措施**:所有个人敏感信息均已脱敏处理,符合伦理标准和法律要求。 #### 使用建议 为了充分利用这一数据集,在实际项目开发前应仔细阅读官方文档说明,并确认本地环境满足相应依赖条件。此外还需注意遵循版权协议规定合理合法地利用这些资源。
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